Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network

Bayesian networks (BNs) are probabilistic expert systems which have emerged over the last few decades as a powerful data mining technique. Also, BNs have become especially popular in biomedical applications where they have been used for diagnosing diseases and studying complex cellular networks, among many other applications. In this study, we built a BN in a fully automated way in order to analyse data regarding injuries due to the inhalation, ingestion and aspiration of foreign bodies (FBs) in children. Then, a sensitivity analysis was carried out to characterize the uncertainty associated with the model. While other studies focused on characteristics such as shape, consistency and dimensions of the FBs which caused injuries, we propose an integrated environment which makes the relationships among the factors underlying the problem clear. The advantage of this approach is that it gives a picture of the influence of critical factors on the injury severity and allows for the comparison of the effect of different FB characteristics (volume, FB type, shape and consistency) and children's features (age and gender) on the risk of experiencing a hospitalization. The rates it consents to calculate provide a more rational basis for promoting care-givers’ education of the most influential risk factors regarding the adverse outcomes.     

Manufacturing network configuration in supply chains with product recovery

Efficient implementation of product recovery requires appropriate network structures. In this paper, we study the network design problem of a firm that manufactures new products and remanufactures returned products in its facilities. We examine the capacity decisions and expected performance of two alternative manufacturing network configurations when demand and return flows are both uncertain.     

Decision Theory Applied to a Linear Panel Data Model

This paper applies some general concepts in decision theory to a linear panel data model. A simple version of the model is an autoregression with a separate intercept for each unit in the cross section, with errors that are independent and identically distributed with a normal distribution. There is a parameter of interest γ and a nuisance parameter τ, a N×K matrix, where N is the cross‐section sample size. The focus is on dealing with the incidental parameters problem created by a potentially high‐dimension nuisance parameter. We adopt a “fixed‐effects” approach that seeks to protect against any sequence of incidental parameters. We transform τ to (δ, ρ, ω), where δ is a J×K matrix of coefficients from the least‐squares projection of τ on a N×J matrix x of strictly exogenous variables, ρ is a K×K symmetric, positive semidefinite matrix obtained from the residual sums of squares and cross‐products in the projection of τ on x, and ω is a (N−J) ×K matrix whose columns are orthogonal and have unit length. The model is invariant under the actions of a group on the sample space and the parameter space, and we find a maximal invariant statistic. The distribution of the maximal invariant statistic does not depend upon ω. There is a unique invariant distribution for ω. We use this invariant distribution as a prior distribution to obtain an integrated likelihood function. It depends upon the observation only through the maximal invariant statistic. We use the maximal invariant statistic to construct a marginal likelihood function, so we can eliminate ω by integration with respect to the invariant prior distribution or by working with the marginal likelihood function. The two approaches coincide. Decision rules based on the invariant distribution for ω have a minimax property. Given a loss function that does not depend upon ω and given a prior distribution for (γ, δ, ρ), we show how to minimize the average—with respect to the prior distribution for (γ, δ, ρ)—of the maximum risk, where the maximum is with respect to ω. There is a family of prior distributions for (δ, ρ) that leads to a simple closed form for the integrated likelihood function. This integrated likelihood function coincides with the likelihood function for a normal, correlated random‐effects model. Under random sampling, the corresponding quasi maximum likelihood estimator is consistent for γ as N→∞, with a standard limiting distribution. The limit results do not require normality or homoskedasticity (conditional on x) assumptions.     

网络时代的审美文化与审美产业——以审美经济学为视角

以审美经济学为视角考察网络文化产业,发现:审美泛化与仿真式"拟象"同新媒体文化息息相关;审美文化的历史嬗变表明,审美文化研究已成为我国美学发展中的一个重要取向,其现实层面则表明,审美文化呈现出多元共生的复杂格局;审美经济与审美消费主义的关联揭示出网络文化产业是一种典型的符号经济业态,需要遵循虚拟经济的发展规律和主体间性的审美法则;审美产业化呈现出超功利审美与功利性后果密切结合的趋势,与此相应的则是网络文化产业的大众化与平民化走向。     

贫困大学生社会支持网的现状、问题及对策——以福州地区高校为例

本研究采用定量研究和定性研究相结合的方法,选择福州地区四所高校的贫困大学生作为调查研究对象,通过对贫困大学生的问卷调查和个别访谈,在实证分析的基础上,研究贫困大学生社会支持网的现状、支持的内容及其来源,分析影响贫困大学生社会支持网的社会因素和个体因素,并针对贫困大学生社会支持网存在的问题提出对策和建议。     

网络言论侵犯隐私权的法律防范与救济

分析指出,网络言论侵犯隐私权是一种新的侵权方式,它使公民隐私权的外延扩大,从而受到更多形式的伤害。为了更好地保护公民的隐私权,必须进一步完善法律机制,从法律防范与救济角度作深入地有益地探讨。     

面向应用型网络工程方向教学体系的改革与研究

针对不同的"网络工程"就业方向,构建和优化了分层次的教学课程体系,并结合实际情况就"双师型"教师培养、实验室及校外实习基地建设中的一些困难进行了探讨,为应用型网络工程方向的专业建设提供了一种可借鉴的思路。     

网络问政：中国共产党执政能力建设的新课题

网络问政是互联网时代的有效执政方式,也是现代执政党实现科学、有效执政的必然选择。网络问政能力已经成为现代执政党的核心执政能力之一。网络问政在中国才刚刚起步,但国外已有先例,其实质是执政党对待网络民意的态度问题。在互联网日渐发达和普及率持续走高的今天,积极借鉴、吸收国外网络问政的经验、教训,已经成为中国共产党加强自身核心执政能力建设的新课题。对执政的中国共产党而言,重视网络民意,是实现网络问政的前提;引导网络民意,是实现网络问政的保证;回应网络民意,是实现网络问政的根本;构建网络反腐新模式,是加强廉政能力建设的应有之举。     

虚拟文化体验与真实体验文化——基于体验经济的网络文化产业发展路径

网络文化产业作为体验经济的重要业态之一,亟须探索实现又快又好发展的具体路径。基于体验经济的基本理论和网络体验的虚拟性与真实性,以时间占有为目的的在线消费模式,乃数字内容经营的精髓。分为包月和按时间计费的电信式收费方式,是网络文化产业在线消费模式的最佳计费选择和有效营利模式。但网络文化产业的可持续发展不仅需要有效的营利模式,更需要足以吸引网友的网络文化产品与服务。     

计算机实验室中的ARP病毒防治技术研究

针对高校计算机实验室网络中ARP病毒盛行严重影响教学的情况,阐述了ARP协议的工作原理、特征及攻击类型,分析了ARP协议与ARP欺骗的关系,并从多个方面提出了ARP病毒的防治技术.