Vibrational spectroscopic study of the antimonate mineral stibiconite Sb3+Sb5+2O6(OH)

The Raman and infrared spectrum of the antimonate mineral stibiconite Sb3+Sb5+2O6(OH) were used to define aspects of the molecular structure of the mineral. Bands attributable to water, OH stretching and bending and SbO stretching and bending were assigned. The mineral has been shown to contain both calcium and water and the formula is probably best written (Sb3+,Ca)ySb5+2-x(O,OH,H2O)6-7 where y approaches 1 and x varies from 0 to 1. Infrared spectroscopy complimented with thermogravimetric analysis proves the presence of water in the stibiconite structure. The mineral stibiconite is formed through replacement of the sulphur in stibnite. No Raman or infrared bands attributable to stibnite were identified in the spectra.

Raman spectroscopic study of the hydroxy-arsenate mineral geminite Cu(AsO3OH)•H2O

The mineral geminite, an hydrated hydroxy-arsenate mineral of formula Cu(AsO3OH)•H2O, has been studied by Raman and infrared spectroscopy. Two minerals from different origins were investigated and the spectra proved quite similar. In the Raman spectra of geminite, four bands are observed at 813, 843, 853 and 885 cm-1. The assignment of these bands is as follows: (a) The band at 853 cm-1 is assigned to the AsO43- ν1 symmetric stretching mode (b) the band at 885 cm-1 is assigned to the AsO3OH2- ν1 symmetric stretching mode (c) the band at 843 cm-1 is assigned to the AsO43- ν3 antisymmetric stretching mode (d) the band at 813 cm-1 is ascribed to the AsO3OH2- ν3 antisymmetric stretching mode. Two Raman bands at 333 and 345 cm-1 are attributed to the ν2 AsO4 3- bending mode and a set of higher wavenumber bands are assigned to the ν4 AsO43- bending mode. A very complex set of overlapping bands is observed in both the Raman and infrared spectra. Raman bands are observed at 2288, 2438, 2814, 3152, 3314, 3448 and 3521 cm-1. Two Raman bands at 2288 and 2438 cm-1 are ascribed to very strongly hydrogen bonded water. The broader Raman bands at 3152 and 3314 cm-1 may be assigned to adsorbed water and not so strongly hydrogen bonded water in the molecular structure of geminate. Two bands at 3448 and 3521 cm-1 are assigned to the OH stretching vibrations of the (AsO3OH)2- units. Raman spectroscopy identified Raman bands attributable to AsO43- and AsO3OH2- units.

The effects of L1 orthography on processing an artificial logographic script

To date, studies have focused on the acquisition of alphabetic second languages (L2s) in alphabetic first language (L1) users, demonstrating significant transfer effects. The present study examined the process from a reverse perspective, comparing logographic (Mandarin-Chinese) and alphabetic (English) L1 users in the acquisition of an artificial logographic script, in order to determine whether similar language-specific advantageous transfer effects occurred. English monolinguals, English-French bilinguals and Chinese-English bilinguals learned a small set of symbols in an artificial logographic script and were subsequently tested on their ability to process this script in regard to three main perspectives: L2 reading, L2 working memory (WM), and inner processing strategies. In terms of L2 reading, a lexical decision task on the artificial symbols revealed markedly faster response times in the Chinese-English bilinguals, indicating a logographic transfer effect suggestive of a visual processing advantage. A syntactic decision task evaluated the degree to which the new language was mastered beyond the single word level. No L1-specific transfer effects were found for artificial language strings. In order to investigate visual processing of the artificial logographs further, a series of WM experiments were conducted. Artificial logographs were recalled under concurrent auditory and visuo-spatial suppression conditions to disrupt phonological and visual processing, respectively. No L1-specific transfer effects were found, indicating no visual processing advantage of the Chinese-English bilinguals. However, a bilingual processing advantage was found indicative of a superior ability to control executive functions. In terms of L1 WM, the Chinese-English bilinguals outperformed the alphabetic L1 users when processing L1 words, indicating a language experience-specific advantage. Questionnaire data on the cognitive strategies that were deployed during the acquisition and processing of the artificial logographic script revealed that the Chinese-English bilinguals rated their inner speech as lower than the alphabetic L1 users, suggesting that they were transferring their phonological processing skill set to the acquisition and use of an artificial script. Overall, evidence was found to indicate that language learners transfer specific L1 orthographic processing skills to L2 logographic processing. Additionally, evidence was also found indicating that a bilingual history enhances cognitive performance in L2.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.