Meson decay in an independent quark model

Leptonic decay widths and leptonic decay constants of light vector mesons and weak leptonic decay widths and weak decay constants of light and heavy pseudoscalar mesons have been studied in a field- theoretic framework based on the independent quark model with a scalar- vector power-law potential. The results are in very good agreement with the experimental data.Comment: 13 page

Capacity Utilization in Indian Paper Industry

The study estimates the rate of capacity utilization for the Indian paper industry for the period 1973-74 to 1997-98 on the basis of the theoretical framework of variable cost function. It is based on the basic premise that deviation from full utilization of capacity takes place as the levels of certain inputs, particularly capital, are fixed in the short-run and thus can be changed only in the long-run. In order to meet the increase (decrease) in demand, the industry puts the existing capital to more (less) intensive use. The study undertakes empirical estimation of a translog variable cost function by considering three variable inputs, viz., labour, energy and raw material and one quasi-fixed input, capital, on the basis of aggregate industry level data taken from annual survey of industries. It is found that under- utilization of capacity prevails in the Indian paper industry and there has been a decline in the rate of capacity utilization over time.economic capacity utilization, equilibrium capital stock, Indian paper industry, temporary equilibrium, translog variable cost function

Anharmonic quantum contribution to vibrational dephasing

Based on a quantum Langevin equation and its corresponding Hamiltonian within a c-number formalism we calculate the vibrational dephasing rate of a cubic oscillator. It is shown that leading order quantum correction due to anharmonicity of the potential makes a significant contribution to the rate and the frequency shift. We compare our theoretical estimates with those obtained from experiments for small diatomics $N_2$, $O_2$ and $CO$.Comment: 21 pages, 1 figure and 1 tabl

Risks and remedies in e-learning system

One of the most effective applications of Information and Communication Technology (ICT) is the emergence of E-Learning. Considering the importance and need of E-Learning, recent years have seen a drastic change of learning methodologies in Higher Education. Undoubtedly, the three main entities of E-Learning system can be considered as Student, Teacher & Controlling Authority and there will be different level, but a good E-Learning system needs total integrity among all entities in every level. Apart from integrity enforcement, security enforcement in the whole system is the other crucial way to organize the it. As internet is the backbone of the entire system which is inherently insecure, during transaction of message in E-Learning system, hackers attack by utilising different loopholes of technology. So different security measures are required to be imposed on the system. In this paper, emphasis is given on different risks called e-risks and their remedies called e-remedies to build trust in the minds of all participants of E-Learning system

Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity

In this paper, we analyze the information theoretic lower bound on the necessary number of samples needed for recovering a sparse signal under different compressed sensing settings. We focus on the weighted graph model, a model-based framework proposed by Hegde et al. (2015), for standard compressed sensing as well as for one-bit compressed sensing. We study both the noisy and noiseless regimes. Our analysis is general in the sense that it applies to any algorithm used to recover the signal. We carefully construct restricted ensembles for different settings and then apply Fano's inequality to establish the lower bound on the necessary number of samples. Furthermore, we show that our bound is tight for one-bit compressed sensing, while for standard compressed sensing, our bound is tight up to a logarithmic factor of the number of non-zero entries in the signal