Forecasting demand for single-period products: A case study in the apparel industry

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel ‘top-flop’ approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.     

The advantage of indices of dispersion in queueing approximations

A recent robust queueing approximation for open queueing networks exploits partial characterizations of each arrival process by its rate and index of dispersion for counts (IDC), which is a scaled version of the variance–time curve. Even though only means and variances (as functions of time) are involved, we show that the IDC provides a basis for more accurate approximations than traditional two-moment partial characterizations. For the $GI∕GI∕1$ queue, this approach applied to the arrival and service processes fully characterizes the model.     

Vapour phase chemistry of oxovanadium iv β-diketonates

A series of fluorinated and one non-fluorinated β-diketonate complexes of oxovanadium IV have been prepared, their gas phase IR spectra examined, vapour pressures determined and their vapour phase thermal decomposition investigated. Decomposition of the fluorinated complexes occurred according to the overall reaction scheme VO(β-diket)₂»VOF₂ + Furanone. The furanones C₅HF₅O₂, C₅H₄F₂O₂ and C₈H₁₀F₂O₂ were isolated and identified. The non-fluorinated complex gave the parent diketone and a vanadium oxide as its principal products.     

太赫兹时域光谱在邮件隐匿危险品检测中的应用

随着电子商务的快速发展,邮件数量剧增,在邮件中隐匿危险品已经成为犯罪分子重要的犯罪手段,威胁公共安全和社会稳定。邮件的安全检查变得尤为重要,而常规的检测技术不能准确识别危险品。太赫兹波是介于红外和微波之间的电磁波,邮件中隐匿的爆炸物、毒品和有害生物因子等在太赫兹波段存在特征吸收光谱,而邮件常用的非极性包装材料可以被太赫兹波穿透。太赫兹波还具有低能性、相干性等特性,这些特性使得太赫兹技术可以实现邮件隐匿危险品高灵敏度的无损检测。文章介绍了太赫兹技术的特性,太赫兹时域光谱系统的组成和获取光学常数的菲涅尔公式解析法。该方法通过样品透射或反射信号和参考信号来获取包括吸收光谱在内的材料参数。将样品的太赫兹特征吸收光谱和已建立的各种危险品的光谱特征数据库进行比对,可以判断是否为危险品以及危险品种类。对爆炸物、毒品在太赫兹波段的特征吸收光谱的研究成果,及在各种非极性材料遮挡下吸收光谱的特异性的研究进展进行了总结。获取吸收光谱的解析法适用于较厚样品,针对薄样品,还介绍了一种P-谱法。该方法不需要参考信号就能准确获取覆盖物下样品的吸收光谱。除直接利用吸收光谱做检测外,近些年还提出了很多其他识别危险品的方法,如光谱动力学分析法,化学计量学方法和基于太赫兹时域光谱的成像分析法等。其中,光谱动力学分析法可以很好的区分吸收频率有重叠的物质;化学计量学方法可以对混合物进行成分的定性和定量分析;光谱成像法可以完成较大面积的隐匿危险品识别。分析了太赫兹时域光谱技术识别有害生物因子的可行性,以及针对有害生物因子携带量小的特点,总结了太赫兹时域光谱技术在提高生物因子检测灵敏度方面的研究进展。探讨了太赫兹技术在邮件安检应用中一些有待解决的问题,如太赫兹功率有限、受环境因素影响较大、缺乏统一的标准等,展望了未来的发展趋势。     

ARIMA模型在达尔文通信模式研究中的运用

首先利用ARIMA模型,研究了进化论的提出者查尔斯·达尔文(Charles R.Darwin,1809/02/12-1882/04/19)从1866到1879年期间的通信量变化规律,并根据模型优化的AIC和BIC准则求得ARIMA优化模型.接着,应用ARIMA优化模型,对达尔文1880至1881年的月通信数量的时间序列进行了预测.预测的月通信数量与实际通信数量的对比说明,ARIMA优化模型拟合效果良好,并能够较好的反映达尔文学术生涯后期实际通信量的时间变化规律.研究结果表明,ARIMA优化模型可以作为一种研究人类通信模式的有效模型加以广泛应用.