Langmuir–Blodgett films of pyridinium-dicyanomethanide dyes mixtures with photobleachable absorption bands

We report on Langmuir-Blodgett (LB) films characterization of 4-[5-dicyanomethanido)thien-2-y1]-N-(n-hexadecyl)pyridinium (C₁₆H₃₃-PDCNT), and 1-(N-(n-hexadecy]-4-pyridinio)-2-[5-(dicyanomethanido)thien-2-yl]ethene (C₁₆H₃₃-PDCNTE); LB films of the pure compounds and of the mixtures of the two compounds were prepared at 291 K: UV-vis investigation revealed the presence of photobleachable absorption bands, the ones at about 530 nm and 640 nm were due to charge transfer transitions of the monomer of C16H33-PDCNT and C₁₆H₃₃-PDCNTE, respectively; the sharp, photobleachable ones shifted to shorter wavelengths were due to H-aggregates of the two compounds. By changing the molar ratios of the two compounds in the mixtures and in other cases by annealing the LB films, the absorption maxima of the sharp, photobleachable bands due to H-aggregates could be tuned in the range 415–467 nm. These LB films are thus very promising in view of optical data storage applications.     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.     

财政支出、电力消费与农民收入的时空特征分析

本文将财政农业人均支出和农村人均用电量作为解释变量,农民人均纯收入作为被解释变量,利用2000-2005年中国31个省、市和自治区的有关时空数据,建立相应的空间自回归模型.模型数据统计分析表明:a)农民人均纯收入不仅存在空间自相关的特点,而且具有异质性;b)农民人均收入对财政农业人均支出、农村人均用电量存在明显的空间依赖性;c)用财政农业支出和农村用电量来反映农民收入水平十分有效,且都具有显著的正面影响.据此,文章提出了一些有针对性的提高农民收入的政策建议.     

Bipower-type estimation in a noisy diffusion setting

We consider a new class of estimators for volatility functionals in the setting of frequently observed Itō diffusions which are disturbed by i.i.d. noise. These statistics extend the approach of pre-averaging as a general method for the estimation of the integrated volatility in the presence of microstructure noise and are closely related to the original concept of bipower variation in the no-noise case. We show that this approach provides efficient estimators for a large class of integrated powers of volatility and prove the associated (stable) central limit theorems. In a more general Itō semimartingale framework this method can be used to define both estimators for the entire quadratic variation of the underlying process and jump-robust estimators which are consistent for various functionals of volatility. As a by-product we obtain a simple test for the presence of jumps in the underlying semimartingale.     

在DEA中有关输出与输入的比值的模型的探讨

对以决策单元的输出与输入的比值为目标函数的多目标规划模型,证明了有关它与(弱)DEA有效(C2R)关系的三个定理.     

A note on the central limit theorem for bipower variation of general functions

In this paper we present a central limit theorem for general functions of the increments of Brownian semimartingales. This provides a natural extension of the results derived in [O.E. Barndorff-Nielsen, S.E. Graversen, J. Jacod, M. Podolskij, N. Shephard, A central limit theorem for realised power and bipower variations of continuous semimartingales, in: From Stochastic Analysis to Mathematical Finance, Festschrift for Albert Shiryaev, Springer, 2006], where the central limit theorem was shown for even functions. We prove an infeasible central limit theorem for general functions and state some assumptions under which a feasible version of our results can be obtained. Finally, we present some examples from the literature to which our theory can be applied.     

A reduced‐order simulated annealing approach for four‐dimensional variational data assimilation in meteorology and oceanography

Four‐dimensional variational data assimilation in meteorology and oceanography suffers from the presence of local minima in the cost function. These local minima arise when the system under study is strongly nonlinear. The number of local minima further dramatically increases with the length of the assimilation period and often renders the solution to the problem intractable. Global optimization methods are therefore needed to resolve this problem. However, the huge computational burden makes the application of these sophisticated techniques unfeasible for large variational data assimilation systems. In this study, a Simulated Annealing (SA) algorithm, complemented with an order‐reduction of the control vector, is used to tackle this problem. SA is a very powerful tool of combinatorial minimization in the presence of several local minima at the cost of increasing the execution time. Order‐reduction is then used to reduce the dimension of the search space in order to speed up the convergence rate of the SA algorithm. This is achieved through a proper orthogonal decomposition. The new approach was implemented with a realistic eddy‐permitting configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) of the tropical Pacific Ocean. Numerical results indicate that the reduced‐order SA approach was able to efficiently reduce the cost function with a reasonable number of function evaluations. Copyright © 2008 John Wiley & Sons, Ltd.