Raman spectroscopy of hydrogene-arsenate group (AsO3OH) in solid-state compounds: cobalt mineral phase burgessite Co2(H2O)4[AsO3OH]2•H2O

Raman spectrum of burgessite, Co2(H2O)4[AsO3OH]2.H2O was studied, interpreted and compared with its infrared spectrum. The stretching and bending vibrations of (AsO3) and As-OH units together with the stretching, bending and libration modes of water molecules and hydroxyl ions were assigned. The range of O-H...O hydrogen bond lengths was inferred from the Raman and infrared spectra of burgessite. The presence of (AsO3OH)2- units in the crystal structure of burgessite was proved in agreement with its recently solved crystal structure. Raman and infrared spectra of erythrite inferred from the RRUFF database are used for comparison.

Using Association Rules to Solve the Cold-Start Problem in Recommender Systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus it is difficult for a recommender system to make quality recommendations. This problem is known as the cold-start problem. Here we investigate using association rules as a source of information to expand a user profile and thus avoid this problem. Our experiments show that it is possible to use association rules to noticeably improve the performance of a recommender system under the cold-start situation. Furthermore, we also show that the improvement in performance obtained can be achieved while using non-redundant rule sets. This shows that non-redundant rules do not cause a loss of information and are just as informative as a set of association rules that contain redundancy.

Raman spectroscopy of selected borate minerals of the pinakiolite group

The Raman spectra of a series of related minerals of the pinakiolite group of minerals have been collected and the spectra related to the mineral structure. These minerals are based upon an isolated BO33- ion. The site symmetry is reduced from D3h to C1. Intense Raman bands are observed for the minerals takeuchiite, pinakiolite, fredrikssonite and azoproite at 1084, 1086, 1086 and 1086 cm-1. These bands are assigned to the ν1 BO33- symmetric stretching mode. Low intensity Raman bands are observed for the minerals at 1345, 1748; 1435, 1748; 1435, 1750; 1436, 1749 cm-1. One probable assignment is to ν3 BO33- antisymmetric stretching mode. Intense Raman bands of takeuchiite, pinakiolite, fredrikssonite and azoproite at 712 cm-1 attributed to the ν2 out-of-plane bending mode. Importantly, through the comparison of the Raman spectra, the molecular structure of borate minerals with ill-defined structures can be obtained.

Raman spectroscopy of hydrogen arsenate group (AsO3OH)2− in solid-state compounds: cobalt-containing zinc arsenate mineral, koritnigite (Zn,Co)(AsO3OH)·H2O

Raman spectra of two well-defined types of koritnigite crystals from the Jáchymov ore district, Czech Republic, were recorded and interpreted. No substantial differences were observed between both crystal types. Observed Raman bands were attributed to the (AsO3OH)2- stretching and bending vibrations, stretching and bending vibrations of water molecules and hydroxyl ions. Non-interpreted Raman spectra of koritnigite from the RRUFF database, and published infrared spectra of cobaltkoritnigite were used for comparison. The O-H...O hydrogen bond lengths in the crystal structure of koritnigite were inferred from the Raman spectra and compared with those derived from the X-ray single crystal refinement. The presence of (AsO3OH)2- units in the crystal structure of koritnigite was proved from the Raman spectra which supports the conclusions of the X-ray structure analysis.

Ureaplasma parvum and ureaplasma urealyticum detected in semen after washing prior to assisted reproductive technology procedures

Objective: This study investigated: (i) the prevalence of ureaplasmas in semen and washed semen and (ii) the effect of ureaplasmas on semen andrology parameters. Design: Prospective study. Setting: IVF unit -private hospital, Brisbane, Australia. Patient(s): Three hundred and forty three men participating in an assisted reproductive technology (ART) treatment cycle. Intervention(s): Semen and washed semen tested by culture, PCR assays and indirect immunofluorescent antibody assays. Statistical differences were determined by a t-test, Wilcoxon or Pearson’s Chi- square test where appropriate. Main Outcome Measure(s): The prevalence of ureaplasmas in semen and washed semen and the effect of these microorganisms on semen andrology parameters. Result(s): Ureaplasmas were detected in 73/343 (22%) semen samples and 29/343 (8.5%) washed semen samples. Ureaplasmas adherent to the surface of spermatozoa were demonstrated by indirect immunofluorescent antibody testing. U. parvum serovar 6 (36.6%) and U. urealyticum (30%) were the most prevalent isolates in washed semen. A comparison of the semen andrology parameters of washed semen ureaplasma positive and negative groups demonstrated a lower proportion of non-motile sperm in the washed semen ureaplasma positive group. Conclusion(s): Ureaplasmas are not always removed from semen by a standard ART washing procedure and can remain adherent to the surface of spermatozoa.

Understanding the factors influencing safe and unsafe motorcycle rider intentions

The increasing popularity of motorcycles in Australia is a significant concern as motorcycle riders represent 15% of all road fatalities and an even greater proportion of serious injuries. This study assessed the psychosocial factors influencing motorcycle riders’ intentions to perform both safe and risky riding behaviours. Using an extended theory of planned behaviour (TPB), motorcycle riders (n = 229) from Queensland, Australia were surveyed to assess their riding attitudes, subjective norm (general and specific), perceived behavioural control (PBC), group norm, self-identity, sensation seeking, and aggression, as well as their intentions, in relation to three safe (e.g., handle my motorcycle skilfully) and three risky (e.g., bend road rules to get through traffic) riding behaviours. Although there was variability in the predictors of intention across the behaviours, results revealed that safer rider intentions were most consistently predicted by PBC, while riskier intentions were predicted by attitudes and sensation seeking. The TPB was able to explain a greater proportion of the variance for intentions to perform risky behaviours. Overall, this study has provided insight into the complexity of factors contributing to rider intentions and suggests that different practical strategies need to be adopted to facilitate safer and reduce risky rider decisions.

Design for inspiration : children, personal connections and educational technology

The project is working towards building an understanding of the personal interests and experiences of children with the aim of designing appropriate, usable and, most importantly, inspirational educational technology. kidprobe, an adaptation of the technology probe concept, has been used as a lightweight method of gaining contextual information about children's interactions with 'fun' technology. kidprobe has produced design inspiration which focuses primarily on the social and emotional connections children made. The use of kidprobe has generated some important ideas for improving the use of probes with children. It is an important first step in understanding how to effectively adapt probing techniques to inspire the design of technology for children.

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Surface modification of alumina nanofibres for the selective adsorption of alachlor and imazaquin herbicides

The effective removal of pollutants using a thermally and chemically stable substrate that has controllable absorption properties is a goal of water treatment. In this study, the surfaces of thin alumina (γ-Al2O3) nanofibres were modified by the grafting either of two organosilane agents, 3-chloro-propyl-triethoxysilane (CPTES) and octyl-triethoxysilane (OTES). These modified materials were then trialed as absorbents for the removal of two herbicides, alachlor and imazaquin from water. The formation of organic groups during the functionalisation process established super hydrophobic sites on the surfaces of the nanofibres. This super hydrophobic group is a kind of protruding adsorption site which facilitates the intimate contact with the pollutants. OTES grafted substrate were shown to be more selective for alachlor while imazaquin selectivity is shown by the CPTES grafted substrate. Kinetics studies revealed that imazaquin was rapidly adsorbed on CPTES-modified surfaces. However, the adsorption of alachlor by OTES grafted surface was achieved more slowly.

A raman and infrared spectroscopic study of Ca2+ dominant members of the mixite group from the Czech Republic

Raman microscopy of two mixite minerals BiCu6(AsO4)3(OH)6.3H2O from Jáchymov and from Smrkovec (both Czech Republic) has been used to study their molecular structure, which is interpreted and the presence of (AsO4)3-, (AsO3OH)2-, (PO4)3- and (PO3OH)2- units, molecular water and hydroxyl ions were inferred. O-H…O hydrogen bond lengths were calculated from the Raman and infrared spectra using Libowitzky’s empirical relation. Small differences in the Raman spectra between both samples were observed and attributed to compositional and hydrogen bonding network differences.

Vibrational spectroscopic study of the mineral tsumebite Pb2Cu(PO4,SO4) (OH)

The mineral tsumebite Pb2Cu(PO4)(SO4)(OH), a copper phosphate-sulfate hydroxide of the brackebuschite group has been characterised by Raman and infrared spectroscopy. The brackebuschite mineral group are a series of monoclinic arsenates, phosphates and vanadates of the general formula A2B(XO4)(OH,H2O), where A may be Ba, Ca, Pb, Sr, while B may be Al, Cu2+,Fe2+, Fe3+, Mn2+, Mn3+, Zn and XO4 may be AsO4, PO4, SO4,VO4. Bands are assigned to the stretching and bending modes of PO43- and HOPO3 units. Hydrogen bond distances are calculated based upon the position of the OH stretching vibrations and range from 2.759 Å to 3.205 Å. This range of hydrogen bonding contributes to the stability of the mineral.

Reduction rules for YAWL workflows with cancellation regions and OR-joins

As the need for concepts such as cancellation and OR-joins occurs naturally in business scenarios, comprehensive support in a workflow language is desirable. However, there is a clear trade-off between the expressive power of a language (i.e., introducing complex constructs such as cancellation and OR-joins) and ease of verification. When a workflow contains a large number of tasks and involves complex control flow dependencies, verification can take too much time or it may even be impossible. There are a number of different approaches to deal with this complexity. Reducing the size of the workflow, while preserving its essential properties with respect to a particular analysis problem, is one such approach. In this paper, we present a set of reduction rules for workflows with cancellation regions and OR-joins and demonstrate how they can be used to improve the efficiency of verification. Our results are presented in the context of the YAWL workflow language.