11 research outputs found

    Analysis of the relationship between gynecological diseases and tongue petechia

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    文章通过对舌诊在妇科疾病的应用分析,认为舌诊作为中医的重要诊法,是探讨脏腑内在状况的生物全息元的手段之一,在妇科临床诊断中具有重要作用。而经过王; 彦晖教授临床经验,加上在临床采集病例对照之后,笔者发现,在临床上妇科疾病出现舌尖瘀点的确较其它疾病高,值得我们日后作进一步的研究与探讨。Based on the analysis on the application of tongue diagnosis on; gynecological diseases, it was found that, as an important diagnostic; method in TCM, tongue diagnosis was one of the useful methods to explore; the biological holographic element of internal organs, which also played; an important role in clinical diagnosis of gynecologic diseases. With; professor WANG Yan-hui's clinical experience, and after comparison of; clinical cases, it was found that tongue petechia was concerned with; gynecological diseases, deserving of further research

    构建和谐社会与宗教的理论审视

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    今年以来,本刊就“构建社会主义和谐社会与宗教”这一课题,给予了持续的关注和讨论,宗教界和宗教工作者都围绕这一主题进行了专题讨论。本期,我们主要请有关学者就这一课题发表自己的研究与见解。从各位学者的发言中,我们深切感受到了当代宗教学者的理论热情与社会关切。正如学者们所提出的:在构建社会主义和谐社会的进程中,宗教工作者、宗教界人士和学者三方,应当明确各自的职责,各扬其长,相互合作,谐调互动。诚如是,则社会和谐可期,民族复兴可待

    固体废物重金属污染源解析技术研究进展

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    2005~2014年CERN野外台站气象观测场土壤含水量数据集

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    土壤水分是影响陆地–大气边界层能量和物质传输的重要因子。土壤水分含量是中国生态系统研究网络(CERN)陆地生态系统水环境长期定位观测的重要指标。截至2014年,CERN全国范围内包括农田、森林、草地、荒漠与湿地等生态类型的34个陆地生态系统台站,依据陆地水环境观测规范、质量保证与质量控制规范,设立观测样地,并开展土壤含水量的长期定位观测与数据汇交及质控工作。CERN水分分中心选取了这34个台站2005~2014年气象观测场的土壤含水量长期监测数据,通过进一步统一规范数据格式,形成了全国范围内较长时间序列的公开共享数据集,为土壤含水量时空动态的遥感反演、模型估算验证提供地面实测数据支撑

    2005~2014年CERN地下水位数据集

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    地下水是一个地区重要的自然资源,地下水位数据可为研究地下水的长期变化提供重要的参考资料。本数据集收集整理了中国生态系统研究网络(CERN)34个台站采用人工或自动记录方法观测的2005~2014年地下水位深度数据。重新整理后的数据格式更加规范,质量也有所提高。此外,为了便于用户了解台站地下水位的概况(如平均深度及其变化),我们还计算了各台站地下水位深度的平均值及其标准差

    2004–2016年中国生态系统研究网络水体酸碱度和总溶解性固体数据集

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    水体的酸碱度(pH)和总溶解性固体(TDS)是中国生态系统研究网络(CERN)的重要监测指标,可为生态系统水体质量长期变化研究提供重要数据。降水pH可以表征其是否为酸沉降,地表水和地下水的pH则关系到水质是否对植物生长和动物饮用存在危害等。TDS是表征水体溶解性固体总含量的指标,同样影响到植物根系的水分吸收和动物的生存分布。本数据集收集整理了CERN农田、森林、荒漠、草原、沼泽5种典型生态系统34个生态站2004–2016年降水、地表水、地下水pH和TDS数据。本数据集可为分析降水、地表水、地下水的酸碱度和TDS的时间变化和空间格局提供数据,可为研究中国典型生态系统水质酸碱度和盐碱化的长期变化提供数据支撑

    2004-2016年中国生态系统研究网络(CERN)台站水中八大离子数据集

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    水是自然界重要的组成物质,是生态系统的主要环境因子,中国生态系统研究网络(CERN)对中国典型生态系统水环境开展了长期定位监测。本数据集收集整理了CERN 34个生态站2004–2016年地下水、静止地表水、流动地表水的八大离子(Ca~(2+)、Mg~(2+)、Na~+、K~+、HCO_3~-、CO_3~(2-)、SO_4~(2-)、Cl~-)数据,包含了农田、草地、森林、荒漠、沼泽5类中国典型生态系统。我们对数据进行了准确性检验,剔除异常值,整理后的数据格式更规范,提高了数据的可靠性。本数据集有助于认识各生态系统的水化学变化特征

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

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    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 pνˉK+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 pνˉK+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 9.6×10339.6 \times 10^{33} years, competitive with the current best limits on the proton lifetime in this channel

    JUNO sensitivity on proton decay pνK+p → νK^{+} searches

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

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    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 pνˉK+ 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 pνˉK+ p\to \bar{\nu} K^+ is 36.9% ± 4.9% with a background level of 0.2±0.05(syst)±0.2\pm 0.05({\rm syst})\pm 0.2(stat) 0.2({\rm stat}) events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 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
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