云计算ERP在现代组织中的应用研究

为了规范现代组织的管理流程和提高管理效率,ErP在现代组织中得到了广泛的应用与推广。随着云计算的飞速发展,现代组织已逐步将传统ErP和云计算相结合,提出了基于云计算的ErP解决方案。基于此,阐述了云计算ErP的概念和特征,深入探索了云计算ErP在现代组织中的应用和面临的挑战。厦门大学大学生创新创业基金(DC2013013)资

黑龙江木本植物盛花期对气候变暖的响应

本文利用黑龙江省森林植物园50种木本植物52年的盛花期观测数据和该地区过去52年间全年和每个月的温度数据,探讨黑龙江地区木本植物盛花期对气候变暖的响应,同时分析了生活型(乔木vs.灌木)和果实类型(肉质vs.非肉质)在这种响应方面的差异。结果表明:研究地点在过去的52年里升温趋势明显,全年平均温度或春季平均温度的变化幅度每10年均升高达0.49℃,高于全球和全国的平均水平;50种植物中,多数植物(76%)盛花期主要与4月或5月平均温度显著相关,盛花期对气候变暖响应明显,平均提前2.09 d/℃,但提前速率存在明显的种间差异。其余12个种(24%)的盛花期不受温度升高影响或影响不显著;不同物种盛花期对春季不同月份平均温度变化的响应存在差异,大多数(34种)开花较早的物种与4月平均温度变化呈极显著(P<0.01)或显著相关(P<0.05),少数(4种)开花较晚的物种与5月平均温度变化显著相关(P<0.05);生活型(乔木vs.灌木)和果实类型(肉质vs.非肉质)对这种响应未表现出显著的相关性。上述研究结果初步揭示了我国东北地区木本植物盛花期对气候变暖的响应特点,与国内外同类研究的结论基本一致并相互佐证

气-液-固三相流化床能量最小多尺度模型与气泡尾窝模型比较

将新建的气液固三相流化床能量最小多尺度(EMMS)模型与常用的无固体颗粒气泡尾窝模型、Nacef等给出的基于无固体颗粒气泡尾窝模型的改进模型(1)和改进模型(2)的相含率预测结果进行对比,并与实验数据进行了比较。结果表明,所建三相EMMS模型具有较好的定量预测性能

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