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231.
茶叶的冲泡时间与微量元素浸出量的关系 总被引:1,自引:0,他引:1
采用火焰原子吸收光谱法分别测定绿茶和红茶中铜、铁、锰和锌的浸出量,进而研究冲泡茶叶时间与微量元素浸出量的关系.在冲泡时间为2~20 min时,测定了绿茶(红茶)中微量元素的浸出量:铜为0.068 8~0.114 9(0.064 5~0.140 3)μg·m L-1,锰为0.855 9~2.334 1(0.804 5~2.138 7)μg·m L-1,铁为0.151 5~0.207 5(0.147 3~0.208 8)μg·m L-1,锌为0.149 9~0.191 9(0.139 9~0.185 7)μg·m L-1.方法的回收率和相对标准偏差(n=5)为90.9%~103.1%和0.10%~0.82%.研究表明,当茶叶浸泡时间在2~10 min时,微量元素浸出量随时间的增加而增加,10 min后趋于平稳.因此,从微量元素的角度分析,茶叶的最佳冲泡时间为10 min. 相似文献
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A reliability‐based traffic assignment model for multi‐modal transport network under demand uncertainty
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In densely populated and congested urban areas, the travel times in congested multi‐modal transport networks are generally varied and stochastic in practice. These stochastic travel times may be raised from day‐to‐day demand fluctuations and would affect travelers' route and mode choice behaviors according to their different expectations of on‐time arrival. In view of these, this paper presents a reliability‐based user equilibrium traffic assignment model for congested multi‐modal transport networks under demand uncertainty. The stochastic bus frequency due to the unstable travel time of bus route is explicitly considered. By the proposed model, travelers' route and mode choice behaviors are intensively explored. In addition, a stochastic state‐augmented multi‐modal transport network is adopted in this paper to effectively model probable transfers and non‐linear fare structures. A numerical example is given to illustrate the merits of the proposed model. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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This study investigates the time-dependent reliable shortest path problem (TD-RSPP), which is commonly encountered in congested urban road networks. Two variants of TD-RSPP are considered in this study. The first variant is to determine the earliest arrival time and associated reliable shortest path for a given departure time, referred to as the “forward” TD-RSPP. The second problem is to determine the latest departure time and associated reliable shortest path for a given preferred arrival time, referred as the “backward” TD-RSPP. It is shown in this article that TD-RSPP is not reversible. The backward TD-RSPP cannot be solved by the algorithms designed for the forward problem using the reverse search from destination to origin. In this study, two efficient solution algorithms are proposed to solve the forward and backward TD-RSPP exactly and the optimality of proposed algorithms is rigorously proved. The proposed solution algorithms have potential applications in both advanced traveler information systems and stochastic dynamic traffic assignment models. 相似文献
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