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961.
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。  相似文献   
962.
首次对南海柳珊瑚Leptogorgia rigida中甾体类次生代谢产物进行研究。采用硅胶柱柱层析、十八烷基硅烷键合硅胶填料、Sephadex LH-20、高效液相色谱(HPLC)及薄层色谱(TLC)等多种方法进行分离纯化, 通过核磁共振波谱(NMR)、质谱(MS)等现代波谱分析及物理常数对照等方法进行结构鉴定。从南海柳珊瑚Leptogorgia rigida次生代谢产物中分离得到8个甾类化合物, 结构依次为: 3β, 6β, 11-trihydroxy-9, 11-seco-5α-chloest-7-en-9-one (1); 3β, 6α, 11- trihydroxy-9, 11-seco-5α-chloest-7-en-9-one (2); 3β, 6α, 11-trihydroxy-5α-chloest-7, 22(E)-dien-9-one (3); pregnan-4-en-3, 20-dione (4); 5α-pregnan-3, 20-dione (5); 3β-hydroxy-pregnan-5-en-20-dion (6); 3β-hydroxy-5α-pregnan-20-one (7); 和3β, 6α, 11-trihydroxy-9, 11-seco-5α-gorgosterol (8)。所有化合物均首次从Leptogorgia属中得到。  相似文献   
963.
针对试验舰质心确定困难问题,提出了一种基于定位和姿态数据的试验舰质心确定方法。通过建立试验舰姿态与传感器中心诱导升沉的关系,推导出了试验舰锚泊状态下质心在船固坐标系中位置的解算模型。实验结果表明,采用本方法进行试验舰质心确定不需添测深仪以外的设备,确定的试验舰质心具有较高的精度,能明显地提高舰载无人飞行器惯导对准效率。  相似文献   
964.
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。  相似文献   
965.
本文以一种新型波浪能发电平台为研究对象,分别采用基于三维势流理论的软件Sesam和基于有限体积法的Flow3D软件,对平台和发电浮子进行水动力分析。应用Sesam-HydroD模块计算了平台和发电浮子在频域内的运动响应,将平台和发电浮子的垂荡运动响应进行对比分析,结果表明,在正常海况下,平台与发电浮子垂荡运动相对幅值满足捕能系统的发电需求。应用Flow3D软件对平台整体进行水动力分析,结果表明,平台与发电浮子相对运动振幅在0.3~0.4m间,可满足发电需求。在此基础之上,应用Orcaflex软件,通过时域耦合动力分析的方法,计算了平台在自存工况和作业工况下的运动响应和系泊缆动张力响应,结果表明:在自存工况下,平台锚泊线的安全系数符合规范要求,平台具有良好的安全性能,能够适应恶劣的海洋环境;在作业工况下,平台的垂荡运动响应对波浪方向变化并不敏感,捕能系统不受波浪方向变化的影响,满足发电需求。另外,本文的研究结果能为类似的海洋平台的研究提供建议和参考。  相似文献   
966.
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。  相似文献   
967.
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.  相似文献   
968.
机载激光点云中高压电塔自动识别方法   总被引:2,自引:2,他引:0  
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。  相似文献   
969.
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.  相似文献   
970.
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.  相似文献   
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