Molecular dynamics of CO in few-cycle laser fields

We experimentally study the ionization, fragmentation and Coulomb explosion of CO using 6 fs laser pulses. Different from previous observations in tens or hundreds of femtoseconds laser pulses, strong charge asymmetric dissociation and CO²⁺ are observed in the current intense few-cycle laser field.     

Ruthenium and enzyme-catalyzed dynamic kinetic resolution of alcohols

Ruthenium acts as a good catalyst for the racemization reaction of secondary alcohols and amines. Ruthenium-catalyzed racemization is coupled with enzymatic kinetic resolution to prepare chiral compounds in 100% theoretical yield. Ten ruthenium complexes (1–10) act as a good catalyst the for racemization reaction and are also compatible with DKR process. Two other ruthenium complexes [RuCl₂(PPh₃)₃] and [Cp^*RuCl(COD)] are active for racemization reaction but their successful compatibility with DKR has not yet been reported. Ru/γ-Al₂O₃ and Ru–HAP are the heterogeneous catalysts used for the racemization reaction. They have also not been employed for DKR process. Polymer supported ruthenium is employed as a reusable racemization catalyst for aerobic DKR of alcohols.     

Intensional dynamic programming. A Rosetta stone for structured dynamic programming

We present intensional dynamic programming (IDP), a generic framework for structured dynamic programming over atomic, propositional and relational representations of states and actions. We first develop set-based dynamic programming and show its equivalence with classical dynamic programming. We then show how to describe state sets intensionally using any form of structured knowledge representation and obtain a generic algorithm that can optimally solve large, even infinite, MDPs without explicit state space enumeration. We derive two new Bellman backup operators and algorithms. In order to support the view of IDP as a Rosetta stone for structured dynamic programming, we review many existing techniques that employ either propositional or relational knowledge representation frameworks.     

Tracking error: a multistage portfolio model

We study multistage tracking error problems. Different tracking error measures, commonly used in static models, are discussed as well as some problems which arise when we move from static to dynamic models. We are interested in dynamically replicating a benchmark using only a small subset of assets, considering transaction costs due to rebalancing and introducing a liquidity component in the portfolio. We formulate and solve a multistage tracking error model in a stochastic programming framework. We numerically test our model by dynamically replicating the MSCI Euro index. We consider an increasing number of scenarios and assets and show the superior performance of the dynamically optimized tracking portfolio over static strategies.     

Dynamic modelling and simulation of a hot strip finishing mill

Hot rolling is an essential industrial process in the production of sheet steel, a widely used product in manufacturing and construction. A finishing mill performs a set of operations in a hot strip rolling mill, and is a complex unit including many processes and control loops. Its modelling is a challenging task due to the variety of phenomena that occur within the mill, and variable transport delays. Model validation is also challenging due to a scarcity of measurements. On the other hand, a dynamic model that adequately reflects the numerous interactions between the mill units can be very useful for tasks such as high performance control design or vibration analysis. In this study, a one-dimensional model has been developed and validated against real plant data. The end use of the model is intended to be looper control analysis, but the model is kept sufficiently general so that it can be used or easily extended for other applications.     

Wavelet-based dynamic time warping

Dynamic Time Warping (DTW), a pattern matching technique traditionally used for restricted vocabulary speech recognition, is based on a temporal alignment of the input signal with the template models. The principal drawback of DTW is its high computational cost as the lengths of the signals increase. This paper shows extended results over our previously published conference paper, which introduces an optimized version of the DTW that is based on the Discrete Wavelet Transform (DWT).     

Opportunistic timing and manipulation in Australian Federal Elections

In many parliamentary systems, election timing is an important decision made by governments in order to maximize their expected remaining life in power. Governments can also introduce policy or economic actions to enhance their popular standing and thus their chance of being re-elected. On the other hand, an oppositions’ natural objective is to gain power, and they will also apply controls through their own policies to reduce the governments’ chance of being re-elected. In this paper we employ a dynamic programming approach to determine the optimal timing for governments and oppositions to best utilize their limited resources. At each decision branch, the optimal control is interpreted as a Nash–Cournot equilibrium of a zero-sum political game which, in certain states, admits mixed strategy solutions. We perform a case study on the Australian Federal Election for House of Representatives.     

Dynamic pricing and warranty policies for products with fixed lifetime

We consider a repairable product with known market entry and departure times. A warranty policy is offered with product purchase, under which a customer can have a failed item repaired free of charge in the warranty period. It is assumed that customers are heterogeneous in their risk attitudes toward uncertain repair costs incurred after the warranty expires. The objective is to determine a joint dynamic pricing and warranty policy for the lifetime of the product, which maximizes the manufacturer’s expected profit. In the first part of the analysis, we consider a linearly decreasing price function and a constant warranty length. We first study customers’ purchase patterns under several different pricing strategies by the manufacturer and then discuss the optimal pricing and warranty strategy. In the second part, we assume that the warranty length can be altered once during the product lifetime in developing a joint pricing and warranty policy. Numerical studies show that a dynamic warranty policy can significantly outperform a fixed-length warranty policy.     

Multivariate Bayesian process control for a finite production run

Most industrial products and processes are characterized by several, typically correlated measurable variables, which jointly describe the product or process quality. Various control charts such as Hotelling’s T², EWMA and CUSUM charts have been developed for multivariate quality control, where the values of the chart parameters, namely the sample size, sampling interval and the control limits are determined to satisfy given economic and/or statistical requirements. It is well known that this traditional non-Bayesian approach to a control chart design is not optimal, but very few results regarding the form of the optimal Bayesian control policy have appeared in the literature, all limited to a univariate chart design. In this paper, we consider a multivariate Bayesian process mean control problem for a finite production run under the assumption that the observations are values of independent, normally distributed vectors of random variables. The problem is formulated in the POMDP (partially observable Markov decision process) framework and the objective is to determine a control policy minimizing the total expected cost. It is proved that under standard operating and cost assumptions the control limit policy is optimal. Cost comparisons with the benchmark chi-squared chart and the MEWMA chart show that the Bayesian chart is highly cost effective, the savings are larger for smaller values of the critical Mahalanobis distance between the in-control and out-of-control process mean.     

Dynamic pricing of multiple home delivery options

Online grocers accept delivery bookings and have to deliver groceries to consumers’ residences. Grocery stores operate on very thin margins. Therefore, a critical question that an online grocery store needs to address is the cost of home delivery operations. In this paper, we develop a Markov decision process-based pricing model that recognizes the need to balance utilization of delivery capacity by the grocer and the need to have the goods delivered at the most convenient time for the customer. The model dynamically adjusts delivery prices as customers arrive and make choices. The optimal prices have the following properties. First, the optimal prices are such that the online grocer gains the same expected payoff in the remaining booking horizon, regardless of the delivery option independently chosen by a consumer. Second, with unit order sizes, delivery prices can increase due to dynamic substitution effects as there is less time left in the booking horizon.