试论腾冲县和顺图书馆的特点及发展对策

腾冲县和顺图书馆本身就是一本厚重的图书,在其发展过程中具有藏书丰富、馆藏质量较高、积极开展丰富多彩的乡村文化建设活动等特点,为地方精神文明建设做出了重要贡献。进入21世纪,具有区域特色的和顺图书馆面临一系列发展难题,对这些难题进行分析,并提出发展对策。     

生物化学“教学研考用”五位一体研究性教学初探

文章是在笔者多年生物化学课程教学实践的基础上,对研究性教学和探究式学习进行总结和探讨,初步形成了“教学研考用”五位一体的研究性教学模式,并对该教学模式进行了阐述,以期对当前高校课程教学方法的改革有一定的参考和借鉴价值。     

思想品德教科书中公民教育价值取向基本框架

教科书课程内容的价值取向决定着整个课程的基本价值取向.思想品德教科书中公民教育价值取向的基本框架为:意识形态教育、道德教育和公民意识教育.     

细说氯水

1 氯水中的化学成份 氯水(指新制氯水)是一种成份比较复杂的混合物,其化学成份及微粒之间数目多少关系以及表现出的特征性质如下.     

均衡发展视域下的义务教育资源配置

如何合理配置教育资源促进教育均衡发展是目前亟待解决的问题,本文试图在对现状的描述及其原因分析的基础上提出合理配置教育资源促进义务教育均衡发展的思路。     

高师化学专业开展探究性实验的研究与实践

基础教育新课程标准倡导科学探究的学习方式,目的在于提高学生的科学素养,培养创新能力.高校开展实验探究活动是培养学生科学探究能力的重要途径.结合高师化学专业实验改革,就实验探究的原则、形式及内容进行探讨.通过实例分析了高师化学实验中开展探究活动的设计案例,总结了开展实验探究活动的做法、体会和效果.     

Minus total κ-subdomination in graphs

Let G=(V, E)be a simple graph without isolated vertices. For positive integer κ, a 3-valued function f:V → {-1, 0, 1} is said to be a minus total k-subdominating function(MTκSF)if ∑u∈N(u)f(u)≥ 1 for at least κ vertices v in G, where N(v)is the open neighborhood of v. The minus total κ-subdomination number γ-κt(G)equals the minimum weight of an MTkSF on G. In this paper, the values on the minus total κ-subdomination number of some special graphs are investigated. Several lower bounds on γ-κt of general graphs and trees are obtained.     

国外高校图书馆技术支持服务实践与启示

[目的/意义]适应不断变化的信息技术环境和用户需求,实现服务内容多样化,是高校图书馆开展技术支持服务的必然要求。[方法/过程]运用网络调查的方法,选取2018年THE世界大学排名榜前50所高校图书馆中技术支持服务开展较好的12所进行调研,分析和归纳这些高校图书馆在技术支持服务实践方面的经验和特点,并为国内高校图书馆开展相关服务提供参考建议。[结果/结论]调研结果显示,各图书馆在栏目名称的设置上不尽相同,但又大同小异,其服务项目概括来说主要包括5个方面：计算机硬件与软件服务、打印与复印及扫描服务、其他技术设备借用服务、技术咨询与培训服务、综合型技术创新服务。基于国外良好实践经验,我国高校图书馆在开展技术支持服务的过程中应结合自身实际,从服务理念、资源建设和服务规范等方面着手。     

Dynamic output feedback sliding mode control for spacecraft hovering without velocity measurements

In consideration of target angular velocity uncertainty and external disturbance, a modified dynamic output feedback sliding mode control (DOFSMC) method is proposed for spacecraft autonomous hovering system without velocity measurements. As a stepping-stone, an additional dynamic compensator is introduced into the design of sliding surface, then an augmented system is reconstructed with the system uncertainty and external disturbance. Based on the linear matrix inequality (LMI), a sufficient condition is given, which guarantees the disturbance attenuation performance of sliding mode dynamics. By introducing an auxiliary variable, a modified version of adaptive sliding mode control (ASMC) law is designed, and the finite-time stability of sliding variable is established by the Lyapunov stability theory. Compared with other results, the proposed method is less conservative and can decrease the generated control input force significantly. Finally, two simulation examples are performed to validate the effectiveness of the proposed method.     

Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction

Since the risk of loan defaulting in peer-to-peer (P2P) lending is notoriously difficult to evaluate, a deep neural network-based decision-making approach is proposed in this work for more effective assessment of P2P lending risks. Although normally a dozen features were used for neural network modeling in previous studies carried out by other researchers on similar topics, more comprehensive features including both numeric and categorical ones (e.g. home ownership and purpose of loan), are considered in this work for improved modeling. Since categorical data cannot be used directly as the input of neural networks, they are converted to numerical data using one-hot encoding function. The deep neural network (DNN) used in this work is a multilayer perceptron (MLP) with three hidden layers trained by the back-propagation algorithm. In empirical analysis, the loan data issued by the Lending Club through 2007–2015 are classified into three classes, i.e. safe loan, risky loan and bad loan using TensorFlow. The training and test data sets consist of 221,712 and 55,428 data observations, respectively. Since most of the data belong to the class of safe loan, Synthetic Minority Over-Sampling Technique (SMOTE) is used to improve the DNN prediction accuracy. It is shown that with the proposed approach the test data are classified at an accuracy of 93%, which is much higher than the predication accuracy of 75% obtained using MLP with only one hidden layer.