艾纳香素对护肝及血小板聚集的作用

从菊科植物艾纳香(Blumea Balsamifera)分离得新化学物5,3′,5-三羟基-7-甲氧基二氢黄酮,简称艾纳香素(Blumeatin Blu),Blu对扑热息痛、醋酸泼尼松所致的旰损伤有保护作用,使血清转氨酶和丙二醛水平下降,扑热息痛中毒死亡率有减少作用,Blu在体外对花生四稀酸,5-羟色胺及肾上腺素诱导大鼠及人血小板聚集反应,有明显的促进作用,且具量效关系,1.26μmol/L为最适剂量.     

基于知识元的数字图书馆多粒度集成知识服务研究

[目的/意义] 为满足用户多粒度的知识需求,提出一种数字图书馆多粒度集成知识服务模式。[方法/过程] 首先对知识元的类型与描述规则以及不同类型知识元的实体对象结构加以定义;然后根据知识元抽取、标引和链接的方法,提出基于显性、隐性知识链接的多粒度知识集合集成方法;最后通过规范化分解用户提交的知识需求,构建"需求——知识——资源"三级映射,实现数字图书馆多粒度的集成知识服务模式。[结果/结论] 该方法将知识服务的控制单位从粗粒度的文献单元,深入到以知识元、知识元集合为单位的细粒度知识单元中,为用户提供不同粒度的知识资源。     

分子伴侣、蛋白质聚集和大分子拥挤环境对蛋白质折叠的影响(英)

研究了细胞内存在的分子伴侣、蛋白质聚集和大分子拥挤环境对蛋白质折叠的影响.首先,发现分子伴侣GroEL与底物蛋白的结合有 半位 "和 全位"两种模式,它是由底物蛋白的分子形状、分子大小以及与GroEL的相互作用性质决定的.接着,发现两种不同的蛋白质一起复性时相互不干扰,提示细胞内蛋白质折叠可能不受其他蛋白聚集的影响;后又发现α 乳清蛋白的前熔球态不仅是分子伴侣也是蛋白质聚集体的作用对象.最后,研究大分子拥挤环境对蛋白质折叠热力学和动力学的影响,揭示了这种影响的复杂性和多样性.     

Automatic ranking of information retrieval systems using data fusion

Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods.     

Improving network service performance and reliability via links trunking technologies

INTRODUCTION Because of the critical role that computing sys-tems and networks play in today’s business envi-ronment, it is not surprising that business success often goes to the company with the best information and the most efficient process for delivering that in-formation. As the principal tool for business col-laboration, corporate LANs are a vital resource in enabling today’s enterprises to adopt competitive strategies. The number of users wanting to share and access data across…     

科技型人才聚集环境及聚集效应分析（一）——制度环境对科技型人才聚集效应的影响分析

制度环境是影响科技型人才聚集效应产生与提升的重要环境因素。在论述了科技型人才聚集效应概念和特征的基础上，分析了制度环境的内涵，从人才身份管理制度、人才配置制度、人才产权制度、人才使用制度以及人才激励制度五方面探讨了制度环境对科技型人才聚集效应的影响，提出了优化我国制度环境的对策，以促进科技型人才聚集经济性效应的产生与提升。     

市场竞争中的群聚优势

竞争是市场经济客观存在的产物，是企业（公司）生存发展的动力，在市场竞争中，影响竞争的因素和条件很多，其中群聚优势的作用不可低估。它对提升企业（公司）生产率；促使企业（公司）创新；培植新的企业（公司）有着积极的推动作用。     

FRACTAL MORPHOLOGY IN DLA UNDER MULTIPARAMETER

ＦＲＡＣＴＡＬＭＯＲＰＨＯＬＯＧＹＩＮＤＬＡＵＮＤＥＲＭＵＬＴＩＰＡＲＡＭＥＴＥＲＷａｎｇＪｕｎｓｈｅｎｇ（王俊生）；ＷｅｉＹｕ（韦钰）（ＮａｔｉｏｎａｌＬａｂｏｒａｔｏｒｙｏｆＭｏｌｅｃｕｌａｒａｎｄＢｉｏｍｏｌｅｃｕｌａｒＥｌｅｃｔｒｏｎｉｃｓ）...     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

基于改进KNN的文本分类方法

本文针对VSM (向量空间模型)中KNN (K最近邻算法)在文本处理环境下的不足,根据SOM (自组织映射神经网络)理论、特征选取和模式聚合理论,提出了一种改进的KNN文本分类方法。应用特征选取和模式聚合理论以降低特征空间维数。传统的VSM模型各维相同的权重并不适应于文本处理的环境,本文提出应用SOM神经网络进行VSM模型各维权重的计算。结合两种改进,有效地降低了向量空间的维数,提高了文本分类的精度和速度。