Thermoluminescence and optically stimulated luminescence of gamma-irradiated mineral zircon

Thermoluminescence (TL) manifested by gamma-irradiated mineral zircon has shown a strong TL peak at about 165 °C which is due to recombination of electrons and Dy3+ related shallow hole traps. After they have been removed by a short preheat we have observed two TL peaks at 300-320 °C and ≈420 °C, which are mainly due to recombination of electrons and Tb3+ related hole traps centres yielding its characteristic luminescence. The experimental results indicate that optically stimulated luminescence (OSL) is due to luminescent emission of Tb3+ ions and [SiO4]4– groups. The deep traps related to the 420 °C TL peak contribute to the Tb3+ related OSL. The deep traps related to the 300-320 °C TL peak contribute to OSL associated with the luminescent emission of [SiO4]4– groups.

Combining kernel estimators in the uniform deconvolution problem

We construct a density estimator and an estimator of the distribution function in the uniform deconvolution model. The estimators are based on inversion formulas and kernel estimators of the density of the observations and its derivative. Asymptotic normality and the asymptotic biases are derived

Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance

Let $X_1,...,X_n$ be i.i.d. observations, where $X_i=Y_i+\sigma_n Z_i$ and the $Y$'s and $Z$'s are independent. Assume that the $Y$'s are unobservable and that they have the density $f$ and also that the $Z$'s have a known density $k.$ Furthermore, let $\sigma_n$ depend on $n$ and let $\sigma_n\to 0$ as $n\to\infty.$ We consider the deconvolution problem, i.e. the problem of estimation of the density $f$ based on the sample $X_1,...,X_n.$ A popular estimator of $f$ in this setting is the deconvolution kernel density estimator. We derive its asymptotic normality under two different assumptions on the relation between the sequence $\sigma_n$ and the sequence of bandwidths $h_n.$ We also consider several simulation examples which illustrate different types of asymptotics corresponding to the derived theoretical results and which show that there exist situations where models with $\sigma_n\to 0$ have to be preferred to the models with fixed $\sigma.$Comment: 22 pages, 8 figure

A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones

In this paper, an optimisation-based approach is proposed using a mixed integer quadratic programming model for the economic dispatch of electrical power generators with prohibited zones of operation. The main advantage of the proposed approach is its capability to solve case studies from the literature to global optimality quickly and without any targeting of solution procedures. (c) 2006 Elsevier B.V. All rights reserved

Linguistically informed and corpus informed morphological analysis of Arabic

Standard English PoS-taggers generally involve tag-assignment (via dictionary-lookup etc) followed by tag-disambiguation (via a context model, e.g. PoS-ngrams or Brill transformations). We want to PoS-tag our Arabic Corpus, but evaluation of existing PoS-taggers has highlighted shortcomings; in particular, about a quarter of all word tokens are not assigned a fully correct morphological analysis. Tag-assignment is significantly more complex for Arabic. An Arabic lemmatiser program can extract the stem or root, but this is not enough for full PoS-tagging; words should be decomposed into five parts: proclitics, prefixes, stem or root, suffixes and postclitics. The morphological analyser should then add the appropriate linguistic information to each of these parts of the word; in effect, instead of a tag for a word, we need a subtag for each part (and possibly multiple subtags if there are multiple proclitics, prefixes, suffixes and postclitics). Many challenges face the implementation of Arabic morphology, the rich “root-and-pattern” nonconcatenative (or nonlinear) morphology and the highly complex word formation process of root and patterns, especially if one or two long vowels are part of the root letters. Moreover, the orthographic issues of Arabic such as short vowels ( َ ُ ِ ), Hamzah (ء أ إ ؤ ئ), Taa’ Marboutah ( ة ) and Ha’ ( ه ), Ya’ ( ي ) and Alif Maksorah( ى ) , Shaddah ( ّ ) or gemination, and Maddah ( آ ) or extension which is a compound letter of Hamzah and Alif ( أا ). Our morphological analyzer uses linguistic knowledge of the language as well as corpora to verify the linguistic information. To understand the problem, we started by analyzing fifteen established Arabic language dictionaries, to build a broad-coverage lexicon which contains not only roots and single words but also multi-word expressions, idioms, collocations requiring special part-of-speech assignment, and words with special part-of-speech tags. The next stage of research was a detailed analysis and classification of Arabic language roots to address the “tail” of hard cases for existing morphological analyzers, and analysis of the roots, word-root combinations and the coverage of each root category of the Qur’an and the word-root information stored in our lexicon. From authoritative Arabic grammar books, we extracted and generated comprehensive lists of affixes, clitics and patterns. These lists were then cross-checked by analyzing words of three corpora: the Qur’an, the Corpus of Contemporary Arabic and Penn Arabic Treebank (as well as our Lexicon, considered as a fourth cross-check corpus). We also developed a novel algorithm that generates the correct pattern of the words, which deals with the orthographic issues of the Arabic language and other word derivation issues, such as the elimination or substitution of root letters