A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

Bioaccessibility and Risk of Exposure to Metals and SVOCs in Artificial Turf Field Fill Materials and Fibers

To reduce maintenance costs, municipalities and schools are starting to replace natural grass fields with a new generation synthetic turf. Unlike Astro‐Turf, which was first introduced in the 1960s, synthetic field turf provides more cushioning to athletes. Part of this cushioning comes from materials like crumb rubber infill, which is manufactured from recycled tires and may contain a variety of chemicals. The goal of this study was to evaluate potential exposures from playing on artificial turf fields and associated risks to trace metals, semi‐volatile organic compounds (SVOCs), and polycyclic aromatic hydrocarbons (PAHs) by examining typical artificial turf fibers (n = 8), different types of infill (n = 8), and samples from actual fields (n = 7). Three artificial biofluids were prepared, which included: lung, sweat, and digestive fluids. Artificial biofluids were hypothesized to yield a more representative estimation of dose than the levels obtained from total extraction methods. PAHs were routinely below the limit of detection across all three biofluids, precluding completion of a meaningful risk assessment. No SVOCs were identified at quantifiable levels in any extracts based on a match of their mass spectrum to compounds that are regulated in soil. The metals were measurable but at concentrations for which human health risk was estimated to be low. The study demonstrated that for the products and fields we tested, exposure to infill and artificial turf was generally considered de minimus, with the possible exception of lead for some fields and materials.     

基于CAN总线的隧道通风监测系统设计

为了实现隧道通风监测系统的高安全可靠性、远距离信息传输、多节点网络监测等要求,提出了基于CAN总线 技术的隧道通风监控系统。设计了基于CAN总线技术的控制系统硬件结构,介绍了烟雾和温度检测原理;设计了基于 CAN总线的通风系统监测节点;设计了工控机与CAN总线通信接口适配卡;设计了系统主程序、事件检查和处理子程序 的流程图。应用结果表明该系统具有传输距离远、监测网络节点多等特点,大大提高了系统的实时性和可靠性。     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。     

Public relations and artificial intelligence: It’s not (just) about robots

Organizations of all kinds, as well as their in-house or agency public relations teams, increasingly co-opt Artificial Intelligence (AI) to enhance their capabilities. This paper examines a relatively new topic that has received little scholarly attention: the growing relationship between AI and public relations. It outlines several key roles that AI may play in future, based on trends in other industries, and considers the implications for public relations practitioners, their clients and employers. It therefore launches a dialogue about the diversity and extent of AI’s uses in public relations practice. The paper argues that, to date, commentators have placed too much emphasis on AI’s potential for task automation; AI’s broader technological, economic and societal implications for public relations warrant greater critical attention. This does not imply that practitioners need become expert technologists; rather, they should develop a sufficient understanding of AI’s present and potential uses to be able to offer informed counsel.     

Back propagation neural networks and multiple regressions in the case of heteroskedasticity

This paper compares the performance between regression analysis and a clustering based neural network approach when the data deviates from the homoscedasticity assumption of regression. Heteroskedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroskedasticity in classical regression theory is weighted least-square regression (WLS). In order to deal the problem of heteroskedasticity, backpropagation neural network is applied. In this context, an algorithm is proposed which is based on robust estimates of location and dispersion matrix that helps in preserving the error assumption of the linear regression. Analysis is carried out with appropriate designs using simulated data and the results are presented.     

Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application

The traditional Cobb–Douglas production function uses the compact mathematical form to describe the relationship between the production results and production factors in the production technology process. However, in macro-economic production, multi-structured production exists universally. In order to better demonstrate such input–output relation, a composite production function model is proposed in this article. In aspect of model parameter estimation, artificial fish swarm algorithm is applied. The algorithm has satisfactory performance in overcoming local extreme value and acquiring global extreme value. Moreover, realization of the algorithm does not need the gradient value of the objective function. For this reason, it is adaptive to searching space. Through the improved artificial fish swarm algorithm, convergence rate and precision are both considerably improved. In aspect of model application, the composite production function model is mainly used to calculate economic growth factor contribution rate. In this article, a relatively more accurate calculating method is proposed. In the end, empirical analysis on economic growth contribution rate of China is implemented.     

Neural networks in production planning and control

The purpose of this paper is to present a selection of the most significant ANN (artificial neural network) applications used to solve PPC (production planning and control) problems. Therefore, the paper itself is a reasoned classification rather than a simple survey. The PPC domain is divided into various areas, and for each area one or more examples of ANNs are discussed, together with the benefits and drawbacks of their application to that particular area. Finally, two case studies developed by the authors are reported and discussed to analyse the main implementation problems to be considered when using ANNs.     

项目研究法在培养学生创新素质中的应用

如何密切理论教学与工程实践的联系是目前高等教育提高教学质量,提高学生实践能力需要思考的一个重要问题。以《工业通风与除尘》课程为例,采用项目研究法对这一问题进行了的讨论。通过设定具体条件,让学生在课堂学习过程中,将理论与实践紧密结合起来,并将课程的各部分内容有机结合起来,从而为应用创新型人才培养在课程教学上提供一些思路。     

通风与空调工程漏风量测试装置的改进

风管漏风量的大小是检验通风与空调系统工程质量和降低能耗的一项重要指标，以“通风与空调工程施工及验收规范GB50243－97”的一项新增条款为依据，对漏风量测试装置作了分析，着重对以孔板作节流元件的风管式漏风测试装置在风压调整及自动取压两方面作了改进，并做了相应的试验，为新规范在行业内的实施与推广作必要的准备。