图书馆工作中的"信息不对称"研究

简介了什么是信息不对称理论,分析了信息不对称理论在图书馆工作中的种种表现及造成的危害,并提出抑制图书馆信息不对称之对策。     

Robust self-triggered MPC with fast convergence for constrained linear systems

In this paper, a robust self-triggered model predictive control (MPC) scheme is proposed for linear discrete-time systems subject to additive disturbances, state and control constraints. To reduce the amount of computation on controller sides, MPC optimization problems are only solved at certain sampling instants which are determined by a novel self-triggering mechanism. The main idea of the self-triggering mechanism is to choose inter-sampling times by guaranteeing a fast decrease in optimal costs. It implies a fast convergence of system states to a compact set where it is ultimately bounded and a reduction of computation times to stabilize the system. Once the state enters a terminal region, the system can be stabilized to a robust invariant set by a state feedback controller. Robust constraint satisfaction is ensured by utilizing the worst-case set-valued predictions of future states in such a way that recursive feasibility is guaranteed for all possible realisations of disturbances. In the case where a priority is given to reducing communication costs rather than improvement in control performance in a neighborhood of the origin, a feedback control law based on nominal state predictions is designed in the terminal region to avoid frequent feedback. Performances of the closed-loop system are demonstrated by a simulation example.     

When multi-functional landscape meets Critical Zone science: advancing multi-disciplinary research for sustainable human well-being

Environmental degradation has become one of the major obstacles to sustainable development and human well-being internationally. Scientific efforts are being made to understand the mechanism of environmental degradation and sustainability. Critical Zone (CZ) science and research on the multi-functional landscape are emerging fields in Earth science that can contribute to such scientific efforts. This paper reviews the progress, similarities and current status of these two scientific research fields, and identifies a number of opportunities for their synergistic integration through functional and multi-functional approaches, process-based monitoring, mechanistic analyses and dynamic modeling, global long-term and networked monitoring and systematic modeling supported by scaling and deep coupling. These approaches proposed in this paper have the potential to support sustainable human well-being by strengthening a functional orientation that consolidates multi-functional landscape research and CZ science. This is a key challenge for sustainable development and human well-being in the twenty-first century.     

知识服务——现状、进展及挑战

国外对基于语义Web技术知识服务研究集中在数字图书馆领域。研究主要围绕用户需求的语义表达,落实于知识服务的"以人为本"上。国内学者对此研究主要集中在语义门户、网络化知识组织系统和基于语义Web检索理论方面。基于语义Web技术开发知识服务所面临的挑战来自三个方面:知识服务统一标准规范;知识服务主体的语义互联策略;知识服务用户需求的语义描述策略。表3。图2。参考文献28。     

数据时代图书馆服务发展方向

[目的/意义]大数据开启了图书馆服务的一次重大时代转型，在这样的新环境下，应该对图书馆服务发展模式衍变有新的理解，在服务方式、内容与机制等多方面进行思考，用大数据的思维看待图书馆服务的变革。[方法/过程]依据大量的文献和事实，从图书馆服务功能的角度，对图书馆服务发展模式演进过程进行综合性分析，论证图书馆服务发展模式衍变的内在逻辑与外在基础。[结果/结论]根据新业态的产生路径，提出数据时代图书馆服务发展的两种新业态：基于数据挖掘的知识发现服务新业态和基于数据融合的创新性应用服务新业态。     

面向社交媒体的细粒度ADR本体的半自动构建方法研究

[目的/意义]提出一个药物不良反应本体的半自动构建方法，构建的细粒度药物不良反应本体为利用社交媒体挖掘潜在的药物不良反应信号提供语义资源库。[方法/过程]首先，采用业务层次和语言层次相分离的设计理念，将用户在社交媒体中评论的药物不良反应表示成"对象要素-属性要素-描述概念"的形式。细粒度体现在社交媒体用户对药物同一不良反应描述概念表达的多样性上。然后，基于深度学习的思想，利用基于word2vec的描述概念候选词抽取算法自动地抽取出更多的描述概念候选词构建本体。[结果/结论]以糖尿病药物的建模实例表明，提出的细粒度药物不良反应本体的半自动构建方案，提高了本体构建的智能化水平，构建的细粒度药物不良反应本体为利用社交媒体挖掘潜在的药物不良反应信号提供语义资源库。     

太原市高校体教专业学生短跑课损伤现状与预防

通过对太原市体教专业学生在田径短跑课中运动损伤调查统计,发现太原市高校体育教育专业学生在田径短跑课中,由于准备活动不充分、学生自我保护意识差、技术动作要领不正确、带伤上课等原因造成了运动损伤,针对以上问题提出加强基本功训练、加强运动中的保护和自我保护意识等建议。     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.