The molecular structure of the mineral sarmientite Fe2(AsO4,SO4)2(OH)6•5H2O : implications for arsenic accumulation and removal

Sarmientite is an environmental mineral; its formation in soils enables the entrapment and immobilisation of arsenic. The mineral sarmientite is often amorphous making the application of X-ray diffraction difficult. Vibrational spectroscopy has been applied to the study of sarmientite. Bands are attributed to the vibrational units of arsenate, sulphate, hydroxyl and water. Raman bands at 794, 814 and 831 cm−1 are assigned to the ν3 (AsO4)3− antisymmetric stretching modes and the ν1 symmetric stretching mode is observed at 891 cm−1. Raman bands at 1003 and 1106 cm−1 are attributed to vibrations. The Raman band at 484 cm−1 is assigned to the triply degenerate (AsO4)3− bending vibration. The high intensity Raman band observed at 355 cm−1 (both lower and upper) is considered to be due to the (AsO4)3−ν2 bending vibration. Bands attributed to water and OH stretching vibrations are observed.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Boundary weaving : the social structure and processes of organizational boundaries

Boundaries are an important field of study because they mediate almost every aspect of organizational life. They are becoming increasingly more important as organizations change more frequently and yet, despite the endemic use of the boundary metaphor in common organizational parlance, they are poorly understood. Organizational boundaries are under-theorized and researchers in related fields often simply assume their existence, without defining them. The literature on organizational boundaries is fragmented with no unifying theoretical basis. As a result, when it is recognized that an organizational boundary is "dysfunctional". there is little recourse to models on which to base remediating action. This research sets out to develop just such a theoretical model and is guided by the general question: "What is the nature of organizational boundaries?" It is argued that organizational boundaries can be conceptualised through elements of both social structure and of social process. Elements of structure include objects, coupling, properties and identity. Social processes include objectification, identification, interaction and emergence. All of these elements are integrated by a core category, or basic social process, called boundary weaving. An organizational boundary is a complex system of objects and emergent properties that are woven together by people as they interact together, objectifying the world around them, identifying with these objects and creating couplings of varying strength and polarity as well as their own fragmented identity. Organizational boundaries are characterised by the multiplicity of interconnections, a particular domain of objects, varying levels of embodiment and patterns of interaction. The theory developed in this research emerged from an exploratory, qualitative research design employing grounded theory methodology. The field data was collected from the training headquarters of the New Zealand Army using semi-structured interviews and follow up observations. The unit of analysis is an organizational boundary. Only one research context was used because of the richness and multiplicity of organizational boundaries that were present. The model arose, grounded in the data collected, through a process of theoretical memoing and constant comparative analysis. Academic literature was used as a source of data to aid theory development and the saturation of some central categories. The final theory is classified as middle range, being substantive rather than formal, and is generalizable across medium to large organizations in low-context societies. The main limitation of the research arose from the breadth of the research with multiple lines of inquiry spanning several academic disciplines, with some relevant areas such as the role of identity and complexity being addressed at a necessarily high level. The organizational boundary theory developed by this research replaces the typology approaches, typical of previous theory on organizational boundaries and reconceptualises the nature of groups in organizations as well as the role of "boundary spanners". It also has implications for any theory that relies on the concept of boundaries, such as general systems theory. The main contribution of this research is the development of a holistic model of organizational boundaries including an explanation of the multiplicity of boundaries . no organization has a single definable boundary. A significant aspect of this contribution is the integration of aspects of complexity theory and identity theory to explain the emergence of higher-order properties of organizational boundaries and of organizational identity. The core category of "boundary weaving". is a powerful new metaphor that significantly reconceptualises the way organizational boundaries may be understood in organizations. It invokes secondary metaphors such as the weaving of an organization's "boundary fabric". and provides managers with other metaphorical perspectives, such as the management of boundary friction, boundary tension, boundary permeability and boundary stability. Opportunities for future research reside in formalising and testing the theory as well as developing analytical tools that would enable managers in organizations to apply the theory in practice.

The molecular structure of the multianion mineral hidalgoite PbAl3(AsO4)(SO4)(OH)6 : implications for arsenic removal from soils

The objective of this research is to determine the molecular structure of the mineral hidalgoite PbAl3(AsO4)(SO4)(OH)6 using vibrational spectroscopy. The mineral is found in old mine sites. Observed bands are assigned to the stretching and bending vibrations of (SO4)2- and (AsO4)3- units, stretching and bending vibrations of hydrogen bonded (OH)- ions and Al3+-(O,OH) units. The approximate range of O-H...O hydrogen bond lengths is inferred from the Raman and infrared spectra. Values of 2.6989 Å, 2.7682 Å, 2.8659 Å were obtained. The formation of hidalgoite may offer a mechanism for the removal of arsenic from the environment.

An evaluation of the pattern and structure mathematics awareness program in the early school years

This paper reports a 2-year longitudinal study on the effectiveness of the Pattern and Structure Mathematical Awareness Program (PASMAP) on students’ mathematical development. The study involved 316 Kindergarten students in 17 classes from four schools in Sydney and Brisbane. The development of the PASA assessment interview and scale are presented. The intervention program provided explicit instruction in mathematical pattern and structure that enhanced the development of students’ spatial structuring, multiplicative reasoning, and emergent generalisations. This paper presents the initial findings of the impact of the PASMAP and illustrates students’ structural development.

Developing the Pattern and Structure Assessment (PASA) interview to inform early mathematics learning

The Pattern and Structure Mathematical Awareness Program(PASMAP) stems from a 2-year longitudinal study on students’ early mathematical development. The paper outlines the interview assessment the Pattern and Structure Assessment(PASA) designed to describe students’ awareness of mathematical pattern and structure across a range of concepts. An overview of students’ performance across items and descriptions of their structural development are described.

The motivation and structure of corrupt police networks : theorising the dark side of the ‘thin blue line’

In recent times considerable research attention has been directed to understanding dark networks, especially criminal and terrorist networks. Dark networks are those in which member motivations are self rather than public interested, achievements come at the cost of other individuals, groups or societies and, in addition, their activities are both ‘covert and illegal’ (Raab & Milward, 2003: 415). This ‘darkness’ has implications for the way in which these networks are structured, the strategies adopted and their recruitment methods. Such entities exhibit distinctive operating characteristics including most notably the tension between creating an efficient network structure while retaining the ability to hide from public view while avoiding catastrophic collapse should one member cooperate with authorities (Bouchard 2007). While theoretical emphasis has been on criminal and terrorist networks, recent work has demonstrated that corrupt police networks exhibit some distinctive characteristics. In particular, these entities operate within the shadows of a host organisation - the Police Force and distort the functioning of the ‘Thin Blue Line’ as the interface between the law abiding citizenry and the criminal society. Drawing on data derived from the Queensland Fitzgerald Commission of Enquiry into Police Misconduct and related documents, this paper examines the motivations, structural properties and operational practices of corrupt police networks and compares and contrasts these with other dark networks with ‘bright’ public service networks. The paper confirms the structural differences between dark corrupt police networks and bright networks and suggests. However, structural embeddedness alone is found to be an insufficient theoretical explanation for member involvement in networks and that a set of elements combine to impact decision-making. Although offering important insights into network participation, the paper’s findings are especially pertinent in identifying additional points of intervention for police corruption networks.

Atomistic exploration of deformation properties of copper nanowires with pre-existing defects

Based on the embedded atom method (EAM) and molecular dynamics (MD) method, in this paper, the tensile deformation properties of Cu nanowires (NWs) with different pre-existing defects, including single surface defects, surface bi-defects and single internal defects, are systematically studied. In-depth deformation mechanisms of NWs with pre-existing defects are also explored. It is found that Young's modulus is insensitive to different pre-existing defects, but yield strength shows an obvious decrease. Defects are observed influencing greatly on NWs' tensile deformation mechanisms, and playing a role of dislocation sources. Besides of the traditional deformation process dominated by the nucleation and propagation of partial dislocations, the generations of twins, grain boundaries, fivefold deformation twins, hexagonal close-packed (HCP) structure and phase transformation from face-centred cubic (FCC) structure to HCP structure have been triggered by pre-existing defects. It is found that surface defect intends to induce larger influence to yield strength than internal defect. Most importantly, the defect that lies on slip planes exerts larger influence than other defects. As expected, it is also found that the more or longer of the defect, the bigger influence will be induced.

Complexity, evolution, and the structure of demand

This chapter argues that evolutionary economics should be founded upon complex systems theory rather than neo-Darwinian analogies concerning natural selection, which focus on supply side considerations and competition amongst firms and technologies. It suggests that conceptions such as production and consumption functions should be replaced by network representations, in which the preferences or, more correctly, the aspirations of consumers are fundamental and, as such, the primary drivers of economic growth. Technological innovation is viewed as a process that is intermediate between these aspirational networks, and the organizational networks in which goods and services are produced. Consumer knowledge becomes at least as important as producer knowledge in determining how economic value is generated. It becomes clear that the stability afforded by connective systems of rules is essential for economic flexibility to exist, but that too many rules result in inert and structurally unstable states. In contrast, too few rules result in a more stable state, but at a low level of ordered complexity. Economic evolution from this perspective is explored using random and scale free network representations of complex systems.

Graph rigidity for near-coplanar structure from motion

Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Reduced variance in monozygous twins for multiple MR parameters : implications for disease studies and the genetic basis of brain structure

Twin studies offer the opportunity to determine the relative contribution of genes versus environment in traits of interest. Here, we investigate the extent to which variance in brain structure is reduced in monozygous twins with identical genetic make-up. We investigate whether using twins as compared to a control population reduces variability in a number of common magnetic resonance (MR) structural measures, and we investigate the location of areas under major genetic influences. This is fundamental to understanding the benefit of using twins in studies where structure is the phenotype of interest. Twenty-three pairs of healthy MZ twins were compared to matched control pairs. Volume, T2 and diffusion MR imaging were performed as well as spectroscopy (MRS). Images were compared using (i) global measures of standard deviation and effect size, (ii) voxel-based analysis of similarity and (iii) intra-pair correlation. Global measures indicated a consistent increase in structural similarity in twins. The voxel-based and correlation analyses indicated a widespread pattern of increased similarity in twin pairs, particularly in frontal and temporal regions. The areas of increased similarity were most widespread for the diffusion trace and least widespread for T2. MRS showed consistent reduction in metabolite variation that was significant in the temporal lobe N-acetylaspartate (NAA). This study has shown the distribution and magnitude of reduced variability in brain volume, diffusion, T2 and metabolites in twins. The data suggest that evaluation of twins discordant for disease is indeed a valid way to attribute genetic or environmental influences to observed abnormalities in patients since evidence is provided for the underlying assumption of decreased variability in twins.

A phenomenological model for the structure-composition relationship of the high Tc cuprates based on simple chemical principles

A simple phenomenological model for the relationship between structure and composition of the high Tc cuprates is presented. The model is based on two simple crystal chemistry principles: unit cell doping and charge balance within unit cells. These principles are inspired by key experimental observations of how the materials accommodate large deviations from stoichiometry. Consistent explanations for significant HTSC properties can be explained without any additional assumptions while retaining valuable insight for geometric interpretation. Combining these two chemical principles with a review of Crystal Field Theory (CFT) or Ligand Field Theory (LFT), it becomes clear that the two oxidation states in the conduction planes (typically d8 and d9) belong to the most strongly divergent d-levels as a function of deformation from regular octahedral coordination. This observation offers a link to a range of coupling effects relating vibrations and spin waves through application of Hund’s rules. An indication of this model’s capacity to predict physical properties for HTSC is provided and will be elaborated in subsequent publications. Simple criteria for the relationship between structure and composition in HTSC systems may guide chemical syntheses within new material systems.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.