西太平洋马里亚纳海沟附近海山的金相柳珊瑚属(珊瑚虫纲:软珊瑚目:金柳珊瑚科)二新记录种

为探究热带西太平洋海山生物多样性,2016年利用"发现"号遥控无人潜水器(ROV)对西太平洋马里亚纳海沟附近的M2海山进行了采样调查,其中获得了3个深水金相柳珊瑚样本。通过光学显微镜和扫描电镜观察,鉴定其为黑发金相柳珊瑚Metallogorgia melanotrichos(WrightStuder,1889)和长刺金相柳珊瑚Metallogorgia macrospina Kükenthal,1919。这两种金相柳珊瑚均为该区域的新记录种,对其作了详细的形态描述,并依据特征性状对该属所有物种作了分类检索。     

江河入海流量在线监测不确定度评估方法研究与应用

鉴于国内目前缺少流量在线监测不确定性评估方面的研究,针对江河入海流量在线监测过程中可能产生的各种不确定性,本文采用不确定度概念对不确定性进行评估,给出了不确定度的主要来源、各来源不确定度的评估方法,以及江河入海流量在线监测总不确定度评估模型,并应用该模型对辽河入海流量在线监测的不确定度进行了评估。     

长条状粗颗粒在垂直管路中最小输送速度试验研究

随着陆地金属资源的日益枯竭,深海矿产资源已经成为各国的重要战略目标。在深海采矿过程中,海底锰结核的形状除了圆球状,还有长条状等,颗粒形状对固液两相流管道输送特性具有很大影响。基于固液两相流垂直管道提升输送试验系统,探究不同工况下长条状颗粒在垂直上升流中最小输送速度的变化规律及特性,并得到了长条状群体颗粒最小输送速度计算公式。结果表明:长条状颗粒在垂直管道中上升过程中,颗粒中心轴与输送方向趋向于垂直,使颗粒在管道截面的投影面积最大化;长条状单颗粒的最小输送速度随着颗粒长径比增大而减小;在不同长径比工况下,随着管段颗粒平均浓度减小,长条状群体颗粒的最小输送速度均增大,且随着长径比增大,群最小输送速度减小。     

浅水多波束测深仪高分辨率波束形成方法研究

常规波束形成技术以其稳健性好、计算量低等特点得到广泛应用,但其空间分辨率受阵元个数限制,不能突破瑞利限。因此,常规波束形成技术应用于高精度浅水多波束测深仪时,存在空间分辨率不足和旁瓣干扰等问题。对比研究了最小方差无失真(MVDR)及多重信号分类(MUSIC)波束形成在浅水多波束测深仪中的应用,给出了浅水多波束测深仪的常规波束形成、MVDR和MUSIC处理方法,并结合能量法和相位法两种底检测测深算法,处理了iBeam8120浅水多波束测深仪外场数据,验证了本文方法的性能。     

含沙浑水体的黏度测量及应用分析

含沙浑水体的高速冲击会对海底构筑物造成破坏,在计算浑水体对构筑物的作用力时,黏度是一项重要参数。本文利用落球试验和流变仪测试试验,测定了不同浓度含沙浑水体的黏度,给出其起始黏度与动力作用后的稳定黏度。结果表明:含沙量在大于400g/L时,浑水体为可用赫巴模型描述的非牛顿流体,并可简化为宾汉体;在含沙量小于400g/L时,浑水体仍可用宾汉体模型描述,在忽略较小的初始剪切应力时,可简化为牛顿流体。浓度大于400g/L的浑水体的起始黏度约为稳定黏度的100倍。文中讨论了含沙浑水体起始黏度与稳定黏度在工程计算应用中的适用情况。     

Succession of the Nanoplankton Community during a Brown Tide in a Scallop Culture Area in China

Ocean Science Journal - Brown tides caused by Aureococcus anophagefferens have occurred frequently in the coastal waters of Qinhuangdao since 2009. The Changli coast is the area most strongly...     

柔肝通络汤治疗脑梗死急性期40例临床观察

目的：观察柔肝通络汤治疗脑梗死急性期（阴虚络瘀证）的临床疗效。方法：将脑梗死急性期（阴虚络瘀证）患者80例,随机分为2组,每组各40例。治疗组采用柔肝通络汤治疗,对照组采用西医常规治疗。结果：综合疗效总有效率治疗组为87.5%,对照组为72.5%,组间比较,差异有统计学意义（P＜0.05）;中医证候疗效总有效率治疗组为90.0%,对照组为75.0%,组间比较,差异有统计学意义（P＜0.05）;2组血液流变学指标、NIHSS评分治疗前后组内比较及治疗后组间比较,差异均有统计学意义（P＜0.01）。结论：柔肝通络汤治疗脑梗死急性期（阴虚络瘀证）有较好疗效,优于西医常规治疗。     

Optical Identification of Four Hard X-ray Sources from the INTEGRAL Sky Surveys

Astronomy Letters - We continue the study begun in Karasev et al. (2018) and present the results of our optical identifications of four hard X-ray sources from the INTEGRAL sky surveys. Having...     

Evolution and Pulsations of Population I Post-AGB Stars

Astronomy Letters - Evolutionary calculations of population I stars with initial masses M0 = 1 M⊙, 1.5 M⊙ and 2 M⊙ were carried out up to the stage of the proto-planetary nebula....     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.