Eleven-Dimensional Supergravity in Light-Cone Superspace

We show that Supergravity in eleven dimensions can be described in terms of a constrained superfield on the light-cone, without the use of auxiliary fields. We build its action to first order in the gravitational coupling constant \kappa, by "oxidizing" (N=8,d=4) Supergravity. This is simply achieved, as for N=4 Yang-Mills, by extending the transverse derivatives into superspace. The eleven-dimensional SuperPoincare algebra is constructed and a fourth order interaction is conjectured.Comment: 18 page

Maximal supersymmetry and exceptional groups

The article is a tribute to my old mentor, collaborator and friend Murray Gell-Mann. In it I describe work by Pierre Ramond, Sung-Soo Kim and myself where we describe the N = 8 Supergravity in the light-cone formalism. We show how the Cremmer-Julia E7(7) non-linear symmetry is implemented and how the full supermultiplet is a representation of the E7(7) symmetry. I also show how the E7(7) symmetry is a key to understand the higher order couplings in the theory and is very useful when we discuss possible counterterms for this theory.Comment: Proceedings of Conference in Honour of Murray Gell-Mann's 80th Birthda

LC_2 formulation of supergravity

We formulate (N=1, d=11) supergravity in components in light-cone gauge (LC_2) to order $\kappa$. In this formulation, we use judicious gauge choices and the associated constraint relations to express the metric, three-form and gravitino entirely in terms of the physical degrees of freedom in the theory.Comment: 11 page

Information Deficiency: Implications for Information Systems Design

Determining the information needs of management has been an important area of MIS research. Yet most existing techniques focus only on user perceived importance of information. The notion of Information Deficiency integrates both user perceived importance and user perceived availability of an information category. This paper proposes that Information Deficiency is a more important indicator of how crucial a category is to the system designer in relation to other categories. A laboratory experiment that was used to test this notion is described and implications for systems design are discussed

INVESTIGATION OF LEAKAGE CURRENT OF INSULATOR USING ARTIFICIAL NEURAL NETWORK

In order to improve the reliability of power transmission lines, one of the key issues is to reduce the hazard of contamination flashovers. At the present time, the most efficient way is to clean (or replace) the heavily polluted insulators. This study laboratory based tests were carried out on the model under ac voltage at different pollution levels. A new model based on artificial neural network has been developed to predict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (Imean), maximum (Imax), and standard deviation (Is) of leakage current extracted along with the input voltage (V) and relative humidity (RH). The target obtained was used to evaluate the performance of the neural network model. The comparison of the simulated and actual (measured) results demonstrates that the ESDD prediction model from the stage characteristics