绿潮暴发对浮游动物群落结构影响研究

本调查于2016年和2017年的5月、8月,对日照近岸海域浒苔暴发区进行了4个航次的浮游动物断面调查。4个航次中,2016年和2017年5月航次获得的种类数要明显低于同年8月航次,而平均丰度和湿重生物量均高于同年8月航次。造成这种情况的原因可能是调查海域8月浒苔暴发结束,浒苔的消亡过程会大量消耗营养盐,导致水体中营养盐含量下降,进而影响到其他浮游植物和浮游动物的生长。应用PRIMER软件中的单变量分析得到群落种数(S)、丰富度(d)、香农-威纳指数(Shannon-Weaner index)(H′)和均匀度(J),从多样性指数分析可以看出,5月航次的生物多样性均劣于同年8月航次。不同年度相同季节航次中出现的优势种极其相似,而不同季节航次出现的优势种则大不相同。     

Truss Spar平台在波流联合作用下运动响应预报方法比较

基于细长体水动力模型比较了Truss Spar平台在波流联合作用下运动响应预报的三种方法。分别采用波流耦合、速度叠加及力叠加计算Truss Spar平台在波流联合作用下的水动力载荷,根据流场水质点运动规律和Truss Spar外部形状特点,分段高效计算水动力载荷。利用Runge-Kutta-Fehlberg方法求解刚体非线性运动方程得Truss Spar在波流场中的运动响应。研究结果表明力叠加法所预报的Truss Spar纵荡和纵摇运动明显大于其他两种方法的相应运动响应预报结果,而波流耦合法与速度叠加法所预报的纵荡与纵摇运动响应幅值相当,三种方法所预报的垂荡运动响应的大小取决于具体波流参数。     

雅浦海沟南缘铁锰结核矿物与地球化学特征及其成因研究

本文采用X射线衍射、全岩地球化学和电子探针等测试方法,对雅浦海沟南部附近海域获得的铁锰结核样品进行了显微构造、矿物和地球化学分析,并探讨了其成因。结果表明:铁锰结核的显微构造主要包括平行纹层构造、柱状构造、叠层状构造和同心环状构造;显微构造和探针结果显示铁锰结核在生长初期处于底部海洋动力比较强烈的环境,后期生长环境逐渐趋于稳定;铁锰结核的矿物组分以水羟锰矿、钠水锰矿、石英和钙十字石为主;样品中Fe、Mn元素含量较高且含量比较接近,Cu、Co、Ni和REE相对富集,REE分布模式整体比较平缓并都出现较强的Ce正异常和重稀土元素亏损现象;文中两块铁锰结核都为水成成因,成矿物质主要来自同期的海水沉淀,同时也受一定海底火山物质和陆源风尘物质的影响。     

高通量测序分析北仑河口红树林潮间带底栖真核生物分布

为了解红树林不同潮位沉积物中底栖真核生物群落分布,基于18S rRNA基因采用高通量测序方法分析了广西北仑河口陆缘、林中和海缘3个潮位红树林沉积物中底栖生物群落结构。结果表明,北仑河口潮间带红树林沉积物中底栖生物多样性丰富,Shannon-Wiener指数变化范围在6. 08～6. 73之间; PCA分析表明潮间带中底栖生物群落差异较大,陆缘红树林中扁形动物、节肢动物和软体动物相对丰度较高,林中区域中纤毛虫、环节动物和轮虫相对丰度较高,海缘红树林中硅藻相对丰度较高;红树林中主要OTUs有桡足类的太平洋纺锤水蚤(Acartia pacifica)、硅藻类的海链藻(Thalassiosira sp.)、纤毛虫类的前管虫(Prorodon teres)、多毛类的小头虫(Capitella sp.)。高通量测序方法能较全面反映红树林区微型/小型底栖生物群落,研究结果为丰富红树林底栖生物群落研究和解析底栖生物在红树林生态系统发挥的作用提供基础数据。     

Isolation of protein from Chlorella sorokiniana CY1 using liquid biphasic flotation assisted with sonication through sugaring-out effect

Microalgae,a sustainable source of multi beneficial components has been discovered and could be utilised in pharmaceutical,bioenergy and food applications.This study aims to investigate the sugaring-out effect on the recovery of protein from wet green microalga,Chlorella sorokiniana CY1 which was assisted with sonication.A comparison of monosaccharides and disaccharides as one of the phaseforming constituents shows that the monosaccharides,glucose was the most suitable sugar in forming the phases with acetonitrile to enhance the production of protein(52% of protein).The protein productivity of microalgae was found to be significantly influenced by the volume ratio of both phases,as the yield of protein increased to 77%.The interval time between the sonication as well as the sonication modes were influencing the protein productivity as well.The optimum protein productivity was obtained with 10s of resting time in between sonication.Pulse mode of sonication was suitable to break down the cell wall of microalgae compared to continuous mode as a lower protein yield was obtained with the application of continuous mode.The optimum condition for protein extraction were found as followed:200g/L glucose as bottom phase with volume ratio of 1:1.25,10s of resting time for ultrasonication,5s of ultrasonication in pulse mode and 0.25g of biomass weight.The high yield of protein about 81% could be obtained from microalgae which demonstrates the potential of this source and expected to play an important role in the future.     

模糊聚类定权法对SLR定轨精度的影响

针对卫星激光测距(satellite laser ranging,SLR)精密定轨过程中存在的测站观测数据合理定权问题,将一种改进的模糊聚类算法引入到SLR观测数据定权中。基于国际激光测距服务(International Laser Ranging Service,ILRS)提供的全球SLR测站性能报告,对测站进行近实时滑动分类定权,改变SLR数据处理中权重的经验或者随意性选取模式。经过LAGEOS1卫星2014年1月至2016年12月3年全球SLR实测数据处理的测试。结果表明,当考虑LAGEOS标准点总数、LAGEOS标准点RMS值以及LAGEOS标准点合格率这3项测站质量控制因素确定的测站权值能最大限度地提高卫星定轨精度和观测数据的使用效率,对参与计算的365个3d弧段数据,91.46%弧段精度得到提高,平均提高约3.7mm,且每个测站的定轨残差RMS也得到了降低。这对于正在迈向毫米级测量精度的SLR技术至关重要。     

机载激光点云中高压电塔自动识别方法

提出了一种基于格网特征的机载激光点云高压电塔自动识别方法。首先对机载激光点云数据进行滤波去噪处理;然后对点云数据进行规则格网化特征分析获得高压电塔粗识别区域;最后对粗识别区域进行外接邻域网格线性特征悬空点集检测以确定电塔识别结果,并以分层切片法分析获取电塔平面中心坐标。采用大型无人机实际线路巡检获取的机载点云数据对本文算法进行验证,试验结果表明本算法可实现高压电塔的快速自动识别,对无人机电力巡检智能诊断具有一定的促进作用。     

检定场的机载激光雷达精度评价方法

为了对机载激光雷达测量精度进行科学地评价,保障数据成果质量,满足新型仪器装备计量检测的需要,该文基于空对地检定场圆形标志点,利用平均值法和最小外包圆法对机载激光雷达的平面精度和高程精度进行评价研究。结果表明:针对低空机载激光雷达可以获取高密度的地面点云,可以利用轮廓突出且特殊形状的控制点进行三维精度评价;利用平均值法和最小外包圆法均可以对机载激光雷达进行快速、有效地三维精度评价。     

Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.