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The electrons of a pure electron plasma may be in thermal equilibrium with each other and still be confined by static magnetic and electric fields. Since the electrons make a significant contribution to the electric field, only certain density profiles are consistent with Poisson’s equation. The class of such distributions for a finite length cylindrical column is investigated. In the limit where the Debye length is small compared with the dimensions of the column, the density is essentially constant out to some surface of revolution and then falls off abruptly. The falloff in density is a universal function when measured along the local normal to the surface of revolution and scaled in terms of the Debye length. The solution for the shape of the surface of revolution is simplified by passage to the limit of zero Debye length.

Abstract-In this paper, we present a method of parameter estimation for a class of problems where the desired signal is embedded in colored noise with unknown covariance. The new algorithm is a variation of the covariance differencing scheme proposed by Paulraj and Kailath. Unlike the previous method, however, the proposed algorithm does not require multiple estimates of the signal covariance matrix. Instead, it uses a priori knowledge of the structure of the noise covariance matrix to transform the array covariance matrix in a way that leaves its noise component unchanged while transforming the signal component in some appropriate manner. We can then eliminate the noise component by forming the difference of the original and transformed covariance matrices. This unique feature of the proposed method allows covariance differencing methods to be applied to a wider class of problems than was previously possible. To illustrate this, we apply the new covariance differencing algorithm to the problems of bearing estimation, resolution of overlapping echos, and transient response analysis. Simulation results are presented for each problem, and the new method's performance is compared to that of conventional methods for solving each respective problem. It is interesting to note that our algorithm eliminates the need for array rotation or translation which is often required for the conventional covariance differencing technique.

A theory for low-frequency electrostatic modes of a finite length pure electron plasma column is presented. The plasma is modeled as a cold uniform density cylinder with flat ends. An interesting result is that the diocotron mode can have an axial wavelength that is much larger than the plasma length. Also, for particular values of the plasma density, the axial magnetic field, and the dimensions of the plasma, the diocotron mode is degenerate with a plasma mode and this results in a strong mixing of the modes.

The effect of different extracts of leaves and fruits of Moringa oleifera Lam. (Moringaceae) on gastric and duodenal ulcers was evaluated by using different gastric ulcer models and cysteamine-induced duodenal ulcer method. The leaf extracts (500 mg=kg, p.o.) of Moringa oleifera showed gastric ulcer healing effect in acetic acid-induced chronic gastric ulcers. The acetone extract and methanol extract of the leaves produced gastric antisecretory effect in pylorus-ligated rats and showed gastric cytoprotective effect in ethanol-induced and indomethacin-induced gastric ulcers. The leaf extracts also produced a significant reduction of stress-induced gastric ulcers and cysteamine-induced duodenal ulcers. None of the extracts of the fruits showed any significant antiulcer effect. It was concluded that leaves of Moringa oleifera increase healing of gastric ulcers and also prevent the development of experimentally induced gastric ulcers and duodenal ulcers in rats.

Latent semantic analysis (LSA) has been used in several intelligent tutoring systems(ITS's) for assessing students' learning by evaluating their answers to questions in the tutoring domain. It is based on word-document cooccurrence statistics in the training corpus and a dimensionality reduction technique. However, it doesn't consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance of an ITS. We present here an approach called Syntactically Enhanced LSA (SELSA) which generalizes LSA by considering a word along with its syntactic neighborhood given by the part-of-speech tag of its preceding word, as a unit of knowledge representation. The experimental results on Auto-Tutor task to evaluate students' answers to basic computer science questions by SELSA and its comparison with LSA are presented in terms of several cognitive measures. SELSA is able to correctly evaluate a few more answers than LSA but is having less correlation with human evaluators than LSA has. It also provides better discrimination of syntactic-semantic knowledge representation than LSA.

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