The characteristics of heavy minerals composition and distribution in surface sediment from the Xinghua Bay of Fujian

对兴化湾17个站位表层沉积物中63～125μm粒级重矿物组分、含量、组合及分布特征进行了分析研究,并探讨了泥沙物质来源及重矿物与沉积环境的关系.结果表明,兴化湾重矿物平均含量(质量分数)为12 08%,高出其邻近的湄州湾(5 67%)6个百分点;重矿物共计37种,以磁铁矿、角闪石、绿帘石、钛铁矿、赤铁矿、褐铁矿、锆石为主.矿物种类揭示该海湾的泥沙主要来源于湾顶河流输入和周边陆域及湾内岛屿基岩风化侵蚀产物,而湾口以外海域的输入物质较少;依据主要重矿物含量和分布特征,将兴化湾划分为4个矿物组合区,各区重矿物组合类型不仅与物质来源有关,而且受水动力条件和沉积环境制约.The composition, assemblage and distribution characteristics of heavy minerals from 63 to 125 μm grain-sizes in surface sediment from the Xinghua Bay (17 stations) are studied. And then, matter source of silt and relationship of heavy minerals with sedimentary environment are discussed. The results show that there are 37 kinds of heavy minerals and the average content of them is 12.08% which exceeds 6% compared with the Meizhou Bay ((5.67%)). The dominant minerals are magnetite, hornblende, epidote, ilmenite, hematite, limonite, zircon and so on. Mineral kinds reveal that silt in this bay mainly comes from fluvial input and eroded products of bedrock in circumjacent land and islands of the Xinghua Bay , however, the matter origin from outside area of this bay is less. The Xinghua Bay can be divided into 4 mineral assemblage zones based on heavy mineral contents and distribution characteristics, which are not only related to matter source, but also controlled by hydrodynamic condition and sedimentary environment in the Xinghua Bay.福建省自然科学基金资助项目(D9910006)

国内8款常用植物识别软件的识别能力评价

随着智能手机和人工智能技术的发展,以手机app为载体的植物识别软件慢慢走进公众生活、科普活动和科研活动的各个方面。植物识别app的识别正确率是决定其使用价值和用户体验的关键因素。目前,国内应用市场上有许多植物识别app,它们的开发目的和应用范围各异,软件本身的关注点、数据库来源、算法、硬件要求也存在很大差异。对于不同人群,植物识别app有不同的意义,如对于科研人员来说,识别能力强的app是提高效率的一大工具;对植物爱好者来说,具一定准确率的识别app可以作为入门的工具。因此,对各app的识别能力进行分析与评价显得尤为重要。本文选取了8款常用的app,分别对400张已准确鉴定的植物图片进行识别,其中干旱半干旱区、温带、热带和亚热带4个区各选取100张。这些图片共计122科164属340种,涵盖了乔木、灌木、草本、草质藤本和木质藤本5种生长型,包含23种国家级保护植物。种、属、科准确识别正确分别计4分、2分、1分,以此标准对软件识别能力按总得分进行排序,正确率得分由高到低依次为花帮主、百度识图、花伴侣、形色、花卉识别、植物识别、发现识花、微软识花

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies