Subjects: Nursing >> Nursing submitted time 2022-12-05
Abstract： Erysipelas as common skin infection diseases， traditional Chinese and western medicine therapy and nursing and the advantages and disadvantages on each are not identical， this article summarizes the poison eagerness and one case of patients with skin erysipelas with exten⁃ sive damage of combine traditional Chinese and western medicine， both inside and outside gover⁃ nance of the nursing main points， including the use of traditional Chinese medicine for patients with bloodletting therapy， Chinese medicine external treatment， the meat of the nursing measures such as the implementation， The concept and scheme of skin injury nursing in modern medicine provide some experience and practical basis for the treatment and nursing of such patients with in⁃ tegrated traditional Chinese and Western medicine
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Subjects: Geosciences >> Geography submitted time 2020-01-06 Cooperative journals: 《干旱区地理》
Abstract：选用1962—2017年（10月~次年5月）西南地区（四川、贵州、云南和重庆市）90 个地面气象观测台站的逐日降水和日平均气温实测气象要素资料,运用综合气象干旱指数[WTBX](CI)[WTBZ]统计出西南地区累计干旱日数和频次，并分析两者近56 a来的时空变化特征，再挑选其高、低值年进行大气环流形势讨论，最后制作差值图（均为高值年减低值年）与相关场构造的图进行比较。研究结果表明：累计干旱日数和频次均呈逐渐降低的趋势；两者的年代距平在20世纪60年代~80年代同为正，而在20世纪90年代同为负，其后21世纪初两者距平则相反；累计干旱日数具有5 a和9 a的年际周期变化，12 a的年代际周期，干旱频次具有8 a左右的年际周期和20 a的年代际周期；两者均在四川西部地区和云南中北部为大值中心，云南西部、重庆和贵州中东部为小值中心；高、中和低层的环流形势也缺少水汽和系统抬升等配置关系。
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract： Association rules mining is an important research topic in data mining. Big data processing puts forward higher requirements for the efficiency of association rules mining algorithm, where the most time consuming step is frequent pattern mining. For the problem that the state of art frequent pattern mining algorithm is not efficient, a frequent pattern mining algorithm based on interval interaction and transaction mapping (IITM) is proposed, which combines Apriori algorithm and FP-growth algorithm. This algorithm just needs to scan the dataset twice to generate the FP tree, and then scan the FP tree to map the ID of each transaction to the interval. It growths the frequent pattern by interval interaction and solves the problem that the Apriori algorithm needs to scan the dataset multiple times, the FP-growth algorithm needs to iterate to generate the conditional FP tree, which reduce the efficiency of the frequent pattern mining. Experiments on real dataset show that the IITM algorithm is superior to Apriori, FP-growth, and PIETM algorithms at different support.
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