Comparative Study of Vocal Communication in Two Asian Leaf Monkeys, Presbytis johnii and Presbytis entellus

This paper presents comparative data on the vocal communication of two Asian leaf monkeys, the Nilgiri langur (Presbytis johnii) and South Indian common langur (Presbytis entellus), based on sound recordings and behavioural observations of free-ranging groups. Spectrographical analyses revealed a repertoire of 18 basic patterns for Nilgiri langurs, and 21 basic patterns for common langurs. The repertoires of the two langur species consist of both discretely structured vocal patterns, in which alterations of the physical parameters are restricted to intra-class variation, and those in which structural variations cause intergradation between different sections of the repertoire. Qualitative assessments of group scans indicate that in both species vocal behaviour is characterized by pronounced sex-differences in the use of the different elements of the vocal repertoire. Comparison of data available from different populations of P. entellus suggests population-specific modifications on both structural and behavioural levels. Moreover, characteristic elements of the vocal systems of the two Asian species demonstrate striking similarities to those described for the African black-and-white colobus.

Vocal communication of wild bonnet macaques (Macaca radiata)

Field observations and spectrographic analyses of sound recordings of South Indian bonnet macaques revealed a vocal repertoire of at least 25 basic patterns. The repertoire consists of well separated sound classes and acoustic categories connected by structural intergradation. Besides structural variations within and between different elements of the repertoire, the vocal system ofMacaca radiata is characterized by regular combinations of particular basic patterns. These combinations occurred not only between calls of similar structure and function but also between calls usually emitted in entirely different social contexts. According to the qualitative analysis, sex-specific asymmetries of the vocal behaviour were less pronounced than age-dependent characteristics. The comparison of clear call vocalizations ofMacaca radiata andM. fuscata revealed significant species-specific differences on the structural and the behavioural level. Evaluations of the structural features of alarm calls of various macaque species imply marked differences between members of thefascicularis group andsinica group on one hand and thesilenus group andarctoides

Electronic-Transitions In Hydrogen-Bonded Dimers Of Carboxylic-Acids In The Vapor-Phase - An Electron-Energy Loss Spectroscopic Study

An electron energy loss spectroscopic study of the formic acid dimer has shown bands centred around 7.2, 8.5, 9.8, and 11.1 eV, of which the first and the third bands are assigned to n- rc* transitions and the other two to n-n* transitions; similar transitions are found in the acetic acid dimer.

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.