小氮肥清洁生产实践

文章介绍了沅江市氮肥厂废水闭路循环、尾气回收、废渣综合利用等工程,并介绍了该厂的生产工艺、污染源分布及排污特点。     

小麦期货收益时间序列分析

本文研究了我国郑州商品期货交易所小麦期货近三年来的收益时间序列,对其进行了基本的统计学分析,结果发现分布是非正态的,较正态分布有尖峰厚尾,具有长记忆效应。进一步对其中具有ARCH效应的序列合约进行了分析,采用GARCH和EGARCH类模型进行了描述,分析了期货收益的波动集群性和杠杆效应。     

盾构机自动控制技术现状与展望

随着我国社会主义市场经济的迅速发展以及科技水平的不断提升,交通运输行业得到了蓬勃发展。当前,随着我国地下隧道等各项工程建设数量的不断上升,盾构机的重要性不言而喻。加强对盾构机自动控制技术的研究工作,不断解决该技术应用过程中存在的问题,才能有效地促进技术应用,为提升我国地下工程施工安全性能奠定技术支持。     

我国港机CAD的应用与发展

港口装卸机械产品计算机辅助设计简称港机ＣＡＤ，本文介绍了港机ＣＡＤ１５年来发展的历程和取得的成绩，指出了港机ＣＡＤ今后的研究方向与课题。     

论我国物流机械制造企业技术真空的形成及解决途径

阐述了当前我国物流机械制造业所面临的问题和现状,分析并提出了物流机械制造企业今后努力及其发展方向。     

城乡交错带农业旅游发展战略——以上海为例

近年来,农业旅游逐渐成为农业和旅游业新的经济增长点,城乡交错带是农业旅游发展的主要区域。文章分析了城乡交错带发展农业旅游的重要性及优势,划分了地域结构及范围,探讨了农业旅游的功能定位及产品策划;并以上海为例,提出发展思路及保障措施。     

昌黎缝纫机零配件产业集群发展策略研究

产业集群作为一种新兴的经济发展模式和区域经济的基础特征,已经成为区域竞争优势的重要支点。本文介绍了昌黎缝纫机零配件产业集群的基本情况,指出了其发展过程中的一些问题,就此提出了发展策略。     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.     

A derivatives trading recommendation system: The mid‐curve calendar spread case

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work is aimed to develop a trading recommendation system, and to apply this system to the so‐called Mid‐Curve Calendar Spread (MCCS) trade. To suggest that such approach is feasible, we used a list of 35 different types of MCCSs; a total of 11 predictive and 4 benchmark models. Our results suggest that linear regression with l1‐regularisation (Lasso) compared favourably to other approaches from a predictive and interpretability point of views.