信息技术与高中数学课程整合探究

信息技术与数学课程整合已成为必然趋势,各种教学辅助软件的出现使教师应接不暇。数学软件Maple凭借其简单的操作和强大的功能等优势被越来越多的数学教师作为辅助教学软件。本文用数学实验的方法介绍了Maple在高中数学教学中的几个应用实例,并对信息技术与课程整合与传统教学法之间的优缺点进行了比较。     

The 2015 welfare reform of the National Basic Livelihood Security System in South Korea: Effects on economic outcomes

In July 2015, South Korea’s National Basic Livelihood Security System (NBLSS) was reformed for the purposes of eliminating welfare blind spots and reducing poverty. The reform is expected to affect the recipients’ economic behaviours and choices. In this study, we used changes in benefits and eligibility for the NBLSS under the customised benefit system to identify the effects of the change in the NBLSS on a proposed set of economic outcomes – income, labour supply, consumption, savings, poverty reduction. To estimate the effects, we used data from the 10th–12th waves of the Korea Welfare Panel Study and employed a difference‐in‐differences framework integrated with the propensity scores. We found that the NBLSS helps the poor to reduce financial and material hardships through income and consumption increments, but that it does not provide disincentives to the recipients from participating in the labour market or from saving.     

Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices

Abstract

This article proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.     

互联网医疗服务法律监管研究

随着互联网技术的进步以及医疗改革的深入,互联网医疗服务成为新生业态.然而,我国互联网医疗服务法律监管体系还不够完善,缺乏统一规范的监管体系,多头监管、个人隐私隐患大、纠纷处理解决机制不健全等现实困境尚未解决,不利于互联网医疗服务行业的发展和人民群众身心健康.为解决上述问题,通过梳理发达国家的成功经验,如美国的移动医疗应用程序监管和个人隐私保护、欧盟的互联医疗数据管理、英国的行业协会监管等,结合我国现有国情,建议从完善立法体系、建设隐私保护机制、构建行业监管、明晰医疗服务主体责任等方面健全互联网医疗法律监管制度,以期推动互联网医疗服务行业发展,保障医患双方的合法权益.     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同，本文将利用多期价格信息估计价格趋势，以提高在线策略的性能。首先，基于多期价格数据，利用L₁-中位数估计得到预期价格趋势。然后，通过期望效用最大化，提出一个新的具有线型时间复杂度的在线策略，EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离，进而证明了EGLM策略具有泛证券投资组合性质。最后，利用国内外6个证券市场的历史数据进行实证分析，结果表明相较于UP（Universal Portfolio）策略和EG策略，EGLM策略有更好的竞争性能。     

Security Investment in Contagious Networks

Security of the systems is normally interdependent in such a way that security risks of one part affect other parts and threats spread through the vulnerable links in the network. So, the risks of the systems can be mitigated through investments in the security of interconnecting links. This article takes an innovative look at the problem of security investment of nodes on their vulnerable links in a given contagious network as a game‐theoretic model that can be applied to a variety of applications including information systems. In the proposed game model, each node computes its corresponding risk based on the value of its assets, vulnerabilities, and threats to determine the optimum level of security investments on its external links respecting its limited budget. Furthermore, direct and indirect nonlinear influences of a node's security investment on the risks of other nodes are considered. The existence and uniqueness of the game's Nash equilibrium in the proposed game are also proved. Further analysis of the model in a practical case revealed that taking advantage of the investment effects of other players, perfectly rational players (i.e., those who use the utility function of the proposed game model) make more cost‐effective decisions than selfish nonrational or semirational players.     

Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method

Abstract

In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.     

The quantile-based flattened logistic distribution: Some properties and applications

In this paper, the quantile-based flattened logistic distribution has been studied. Some classical and quantile-based properties of the distribution have been obtained. Closed form expressions of L-moments, L-moment ratios and expectation of order statistics of the distribution have been obtained. A quantile-based analysis concerning the method of matching L-moments estimation is employed to estimate the parameters of the proposed model. We further derive the asymptotic variance–covariance matrix of the matching L-Moments estimators of the proposed model. Finally, we apply the proposed model to simulated as well as two real life datasets and compare the fit with the logistic distribution.     

Addiction science and the perception of freewill

Do people think individuals with Substance Use Disorder (SUD) have freewill? How does addiction science inform views on the issue? We distinguish between two senses of freewill: 1) libertarian freewill , in which freedom turns on a particular metaphysical conception of action (sometimes operationalized as “could have done otherwise”), and 2) compatibilist freewill , in which freedom depends on the relation between the actor's psychology and her actions (e.g., “was the act what she wanted to do?”). We argue that, in different ways, scientific accounts can impact conceived freewill by linking addictive behavior to mechanisms that observers view as peripheral to the actor ( motivation modularity) . While a variety of impacts on conceived compatibilist freewill are plausible, we argue that contemporary addiction science has no direct bearing on conceived libertarian freewill. Addiction science may, however, indirectly impact conceived libertarian freewill by priming an explanatory framework in which intention is superfluous (especially materialism).     

Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.