Microwave ECR Plasma Assisted MOCVD of Y(2)O(3) Thin Films Using Y(tod)(3) Precursor and Their Characterization

Yttrium oxide (Y(2)O(3)) thin films were deposited by microwave electron cyclotron resonance (ECR) plasma assisted metal organic chemical vapour deposition (MOCVD) process using indigenously developed metal organic precursors Yttrium 2,7,7-trimethyl-3,5-octanedionates, commonly known as Y(tod)(3) which were synthesized by an ultrasound method. A series of thin films were deposited by varying the oxygen flow rate from 1-9 sccm, keeping all other parameters constant. The deposited coatings were characterized by X-ray photoelectron spectroscopy, glancing angle X-ray diffraction and infrared spectroscopy. Thickness and roughness for the films were measured by stylus profilometry. Optical properties of the coatings were studied by the spectroscopic ellipsometry. Hardness and elastic modulus of the films were measured by nanoindentation technique. Being that microwave ECR CVD process is operating-pressure-sensitive, optimum oxygen activity is very essential for a fixed flow rate of precursor, in order to get a single phase cubic yttrium oxide in the films. To the best of our knowledge, this is the first effort that describes the use of Y(tod)(3) precursor for deposition of Y(2)O(3) films using plasma assisted CVD process.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

DINKAH KA GADHYA SAHITHYA-EK ADHYAYAN

HINDI