浮力装置触发深海管道水平向整体屈曲效果的数值模拟研究

海洋油气资源的运输主要通过海底管道进行,管道在工作时受到较大的温度荷载,会产生整体屈曲变形。深海管道设计中常采用人为装置触发一定程度的水平向整体屈曲变形,来释放轴向的温度应力,浮力装置是常用的触发方式之一。本文通过数值模拟研究,分析了不同浮力大小和不同浮力施加范围下,管道水平向整体屈曲的临界屈曲力,得出临界屈曲力随浮力大小和施加范围变化的情况;并研究了不同土体阻力下,浮力装置触发整体屈曲的效果。研究表明,水平向土体阻力较大时,浮力装置触发水平向整体屈曲的效果较好。浮力装置的触发效果对轴向土体阻力不敏感。     

辽宁省海洋服务新业态培育与法律保障研究

海洋服务业已成为海洋经济发展的重要引擎,"十三五"时期是辽宁省海洋经济加快转型升级的关键期,要求培育新动能、发展新模式。因此,培育海洋服务新业态对辽宁省海洋经济发展具有重要意义。文章针对辽宁海洋服务业发展滞后,规模偏小、区域发展不平衡,同质化严重、科技含量低,层次相对低下、与其他产业融合不足,资源整合较差等问题,提出培育海洋服务新业态重要路径为:积极发展四大产业,提高经济规模总量;优化产业区域布局,形成错位发展;提高科技含量,提升海洋服务业层次;促进海洋服务业与其他行业融合协调,提升资源整合度。法律保障措施为:完善法规体系、严格项目审批、提高执法水平、健全应急管理。     

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。     

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.     

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。     

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.     

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.