A study of the electronic structures of n---v addition compounds of BH3 by a combined use of ups and eels

Orbital energies and electronic transition energies of BH3·H2S and BH3·CO obtained from ultraviolet (HeI) photoelectron spectroscopy and electron energy loss spectroscopy are discussed in the light of quantum mechanical calculations. BH3·H2O has been characterized, for the first time, by means of the HeI spectrum and the ionization energies assigned to the various orbitals based on calculations.

Electronic structure of high-Tc Ba0.6K0.4BiO3 by x-ray photoelectron spectroscopy

We have investigated the electronic structure of Ba1-xKxBiO3 (0

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

A microspectroscopic study of the electronic homogeneity of ordered and disordered Sr2FeMoO6

Disordered Sr2FeMoO6 shows a drastic reduction in saturation magnetization compared to highly ordered samples, moreover magnetization as a function of the temperature for different disordered samples shows qualitatively different behaviours. We investigate the origin of such diversity by performing spatially resolved photoemission spectroscopy on various disordered samples. Our results establish that extensive electronic inhomogeneity, arising most probably from an underlying chemical inhomogeneity in disordered samples, is responsible for the observed magnetic inhomogeneity. It is further pointed out that these inhomogeneities are connected with composition fluctuations of the type Sr2Fe1+xMo1-xO6 with Fe-rich (x > 0) and Mo-rich (x < 0) regions.

Reducing the babel in plant volatile communication: using the forest to see the trees

While plants of a single species emit a diversity of volatile organic compounds (VOCs) to attract or repel interacting organisms, these specific messages may be lost in the midst of the hundreds of VOCs produced by sympatric plants of different species, many of which may have no signal content. Receivers must be able to reduce the babel or noise in these VOCs in order to correctly identify the message. For chemical ecologists faced with vast amounts of data on volatile signatures of plants in different ecological contexts, it is imperative to employ accurate methods of classifying messages, so that suitable bioassays may then be designed to understand message content. We demonstrate the utility of `Random Forests' (RF), a machine-learning algorithm, for the task of classifying volatile signatures and choosing the minimum set of volatiles for accurate discrimination, using datam from sympatric Ficus species as a case study. We demonstrate the advantages of RF over conventional classification methods such as principal component analysis (PCA), as well as data-mining algorithms such as support vector machines (SVM), diagonal linear discriminant analysis (DLDA) and k-nearest neighbour (KNN) analysis. We show why a tree-building method such as RF, which is increasingly being used by the bioinformatics, food technology and medical community, is particularly advantageous for the study of plant communication using volatiles, dealing, as it must, with abundant noise.

Communication: Evidence of dynamic heterogeneity in glassy polymer monolayers from interface microrheology measurements

We have developed a novel nanoparticle tracking based interface microrheology technique to perform in situ studies on confined complex fluids. To demonstrate the power of this technique, we show, for the first time, how in situ glass formation in polymers confined at air-water interface can be directly probed by monitoring variation of the mean square displacement of embedded nanoparticles as a function of surface density. We have further quantified the appearance of dynamic heterogeneity and hence vitrification in polymethyl methacrylate monolayers above a certain surface density, through the variation of non-Gaussian parameter of the probes. (C) 2010 American Institute of Physics. [doi:10.1063/1.3471584].