Unusual effect of tellurium doping on the electrical-properties of bulk amorphous selenium

Abstract is not available.

Ways and Means for Disposal of Surplus Water in Storage Reservoirs and other Irrigation and Flood Protective Works

In the paper new way of classifying spillways have been suggested. The various types, merits and demerits or existing spillway devices have been discussed. The considerations governing the choice of a design of a spillway have been mention. A criteria for working out the economics of spillway design has been suggested. An efficient surplus sing device has next been described and compared with other devices. In conclusion it has been suggested that the most efficient and at the same time economical arrangement will be a combination of devices. In conclusion it has been suggested will be a combination of crest gate, volute siphons and high head gates. The appendix gives a list of devices used in dams in various parts of the world.

Proton Spin-Lattice Relaxation in Sodium Ammonium Selenate Dihydrate:NaNHaSeOa.2He0

Proton spin-lattice relaxation studies in sodium ammonium selenate dihydrate carried out in the temperature range 130 to 300 K at 10 MHz show a continuous change in T, at T, indicating a second order phase transition. This compound is a typical case of a highly hindered solid wherein the thermally activated reorientations of ammonium ions freeze well above 77 K, as seen by NMR.Untersuchimgen der Protonen-Spin-Gitter-Relaxation in Natriuni-Ammoniumselenat-Dihydrat bei 10 MHz im Temperaturbereich 130 bis 300 K zeigen eine kontinuierliche Andernng in TI bei T, und ergeben einen Phasenubergang zweiter Art. Diese Verbindung ist ein typischer Fall eines stark ,,behinderten" Festkarpers, in dein die thermisch aktivierten Reorientierungen der Ammoniumionen weit oberhalb 77 H einfrieren, wie die NMR-Ergebnisse zeigen.

Ellipsometry as a Sensitive Technique to Probe film -Substrate Interfaces: Al2O3 on Si(100)

An attempt has been made to study the film-substrate interface by using a sensitive, non- conventional tool. Because of the prospective use of gate oxide in MOSFET devices, we have chosen to study alumina films grown on silicon. Film-substrate interface of alumina grown by MOCVD on Si(100) was studied systematically using spectroscopic ellipsometry in the range 1.5-5.0 eV, supported by cross-sectional SEM, and SIMS. The (ε1,ε2) versus energy data obtained for films grown at 600°C, 700°C, and 750°C were modeled to fit a substrate/interface/film “sandwich”. The experimental results reveal (as may be expected) that the nature of the substrate -film interface depends strongly on the growth temperature. The simulated (ε1,ε2) patterns are in excellent agreement with observed ellipsometric data. The MOCVD precursors results the presence of carbon in the films. Theoretical simulation was able to account for the ellipsometry data by invoking the presence of “free” carbon in the alumina films.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.