Star formation in giant extragalactic H II regions

Star formation properties in Giant Extragalactic H II Regions (GEHRs) are investigated using optical photometry and evolutionary population synthesis models. Photometric data in $BVR$ bands and in the emission line of H-alpha are obtained by CCD imaging at Vainu Bappu Observatory, Kavalur. Aperture photometry is performed for 180 GEHRs in galaxies NGC 1365, 1566, 2366, 2903, 2997, 3351, 4303, 4449, 4656 and 5253. Thirty six of these GEHRs having published spectroscopic data are studied for star formation properties. The population synthesis model is constructed based on Maeder's stellar evolutionary and Kurucz stellar atmosphere models, to synthesize observational quantities of embedded clusters in GEHRs. The observed H-alpha luminosity is a measure of the number of massive stars while the contribution to BVR bands is from intermediate mass (5-15 solar mass) stars when the cluster is young and from evolving supergiants when the cluster is old (age >/= 6~Myr). Differential reddening between gas and embedded stars is essential to constrain the dereddened cluster colors within the range of youngest clusters. Obscuring dust closely associated with gas, which is distributed in filaments and clumps, as in the case of 30 Doradus, is the most likely configuration giving rise to net reduction of extinction towards stars. The fraction of the stellar photons escaping the nebula unattenuated is estimated to be 50%. GEHRs are rarely found to be simple systems containing stars from single generation. In the present sample such regions in addition to being older than 3~Myr, have their Lyman continuum luminosity reduced by as much as 60%, compared to the observed $B$ band luminosity for a normal IMF. The missing ionizing photons may be escaping the nebula, leading to the ionization of extra-H II region ionized medium. Co-existence of young (age

Monte-Carlo estimation of time-dependent statistical characteristics of random dynamical systems

The problem of estimating the time-dependent statistical characteristics of a random dynamical system is studied under two different settings. In the first, the system dynamics is governed by a differential equation parameterized by a random parameter, while in the second, this is governed by a differential equation with an underlying parameter sequence characterized by a continuous time Markov chain. We propose, for the first time in the literature, stochastic approximation algorithms for estimating various time-dependent process characteristics of the system. In particular, we provide efficient estimators for quantities such as the mean, variance and distribution of the process at any given time as well as the joint distribution and the autocorrelation coefficient at different times. A novel aspect of our approach is that we assume that information on the parameter model (i.e., its distribution in the first case and transition probabilities of the Markov chain in the second) is not available in either case. This is unlike most other work in the literature that assumes availability of such information. Also, most of the prior work in the literature is geared towards analyzing the steady-state system behavior of the random dynamical system while our focus is on analyzing the time-dependent statistical characteristics which are in general difficult to obtain. We prove the almost sure convergence of our stochastic approximation scheme in each case to the true value of the quantity being estimated. We provide a general class of strongly consistent estimators for the aforementioned statistical quantities with regular sample average estimators being a specific instance of these. We also present an application of the proposed scheme on a widely used model in population biology. Numerical experiments in this framework show that the time-dependent process characteristics as obtained using our algorithm in each case exhibit excellent agreement with exact results. (C) 2010 Elsevier Inc. All rights reserved.

An integrated model for optimal reservoir operation for irrigation of multiple crops

An integrated model is developed, based on seasonal inputs of reservoir inflow and rainfall in the irrigated area, to determine the optimal reservoir release policies and irrigation allocations to multiple crops. The model is conceptually made up of two modules, Module 1 is an intraseasonal allocation model to maximize the sum of relative yields of all crops, for a given state of the system, using linear programming (LP). The module takes into account reservoir storage continuity, soil moisture balance, and crop root growth with time. Module 2 is a seasonal allocation model to derive the steady state reservoir operating policy using stochastic dynamic programming (SDP). Reservoir storage, seasonal inflow, and seasonal rainfall are the state variables in the SDP. The objective in SDP is to maximize the expected sum of relative yields of all crops in a year. The results of module 1 and the transition probabilities of seasonal inflow and rainfall form the input for module 2. The use of seasonal inputs coupled with the LP-SDP solution strategy in the present formulation facilitates in relaxing the limitations of an earlier study, while affecting additional improvements. The model is applied to an existing reservoir in Karnataka State, India.

Cell versus message level performances in ATM networks

The goal of this paper is to provide some insight into the relations that exist between cell level and message level performance guarantees in the context of ATM networks. Cell level guarantees are typically what the network is capable of providing, while message level guarantees are the ones of interest to users. It is, therefore, important to understand how the two are related, and which factors influence this relation. There are many different performance measures that are of importance, and in this paper we try to touch on the (three) most relevant ones. This includes comparing cell and message loss probabilities, average cell and message delays, and cell and message jitter. Specifically, we show that cell and message loss probabilities can exhibit significant differences, which strongly depend on traffic characteristics such as peak rate and burst size, i.e., for a fixed cell loss probability, the message loss probability can greatly vary when peak rate and burst size change. One reason for this sensitivity, is that message loss depends on what happen to all the cells in a message. For delay and jitter, we also find that peak rate and burst size play a role in determining the relation between cell and message performance. However, this sensitivity is not as acute as with losses since message delay and jitter are typically determined by the performance seen by only one cell, the last cell in a message. In the paper, we provide quantitative examples that illustrate the range of behaviors and identify the impact of different parameters.

Electron donor-acceptor complexes of I-2 with diethyl ether and diethyl sulphide. An ab initio MO study

The charge-transfer complexes of I-2 with the n-donors diethyl ether and diethyl sulfide were studied at the Hartree-Fock and MP2 levels. The structures were fully optimized using the 3-21G((*)) basis set as well as with effective core potentials. The calculations consistently yield a C-2v structure for the ether-I-2 complex, but an unsymmetrical form for the sulfide-I-2 complex. A natural bond orbital analysis and the BSSE-corrected complexation energies reveal stronger interactions in the sulfide complex. The computed orbital energies of the monomers and complexes reproduce the trends in experimentally observed vertical ionization potentials.