A pilot study of the Social Water Assessment Protocol in a mining region of Ghana

The Social Water Assessment Protocol (SWAP) is a tool consisting of a series of questions on fourteen themes designed to capture the social context of water around a mine site. A pilot study of the SWAP, conducted in Prestea-Huni Valley, Ghana, showed that some communities were concerned about whether the groundwater was potable. The mining company’s concern was that there was a cycle of dependency amongst communities that received treated water from the mining company. The pilot identified potential data sources and stakeholder groups for each theme, gaps in themes and suggested refinements to questions to improve the SWAP.

Water management capacity building to support rapidly developing mining economies

Many developing countries are experiencing rapid expansion in mining with associated water impacts. In most cases mining expansion is outpacing the building of national capacity to ensure that sustainable water management practices are implemented. Since 2011, Australia's International Mining for Development Centre (IM4DC) has funded capacity building in such countries including a program of water projects. Five projects in particular (principally covering experiences from Peru, Colombia, Ghana, Zambia, Indonesia, Philippines and Mongolia) have provided insight into water capacity building priorities and opportunities. This paper reviews the challenges faced by water stakeholders, and proposes the associated capacity needs. The paper uses the evidence derived from the IM4DC projects to develop a set of specific capacity-building recommendations. Recommendations include: the incorporation of mine water management in engineering and environmental undergraduate courses; secondments of staff to suitable partner organisations; training to allow site staff to effectively monitor water including community impacts; leadership training to support a water stewardship culture; training of officials to support implementation of catchment management approaches; and the empowerment of communities to recognise and negotiate solutions to mine-related risks. New initiatives to fund the transfer of multi-disciplinary knowledge from nations with well-developed water management practices are called for.

What if tax reform was a fundamental human right?

As an election looms in Australia, the tax debate continues unabated. Self-interest abounds. When we remove self-interest, we are often reduced to standard design principles for a taxation system. Lost in this discussion is the fundamental purpose of tax, which is to finance government expenditure. Most would argue that tax revenue should be sufficient to meet basic economic and social needs of the community. But how does a community determine what these basic economic and social needs should be? One way is by using a human rights framework. This can provide guidance for both developing and developed countries considering tax reform.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

A hitchhiker's guide to a crowded syconium: how do fig nematodes find the right ride?

P>1Organisms with low mobility, living within ephemeral environments,need to find vehicles that can disperse them reliably to new environments. The requirement for specificity in this passenger-vehicle relationship is enhanced within a tritrophic interaction when the environment of passenger and vehicle is provided by a third organism. Such relationships pose many interesting questions about specificity within a tritrophic framework. 2. Central to understanding how these tritrophic systems have evolved, is knowing how they function now. Determining the proximal cues and sensory modalities used by passengers to find vehicles and to discriminate between reliable and non-reliable vehicles is, therefore, essential to this investigation. 3. The ancient, co-evolved and highly species-specific nursery pollination mutualism between figs and fig wasps is host to species-specific plant-parasitic nematodes which use fig wasps to travel between figs. Since individual globular fig inflorescences, i.e. syconia, serve as incubators for hundreds of developing pollinating and parasitic wasps, a dispersal-stage nematode within such a chemically,complex and physically crowded environment is faced with the dilemma of choosing the right vehicle for dispersal into a new fig. Such a system therefore affords excellent opportunities to investigate mechanisms that contribute to the evolution of specificity between the passenger and the vehicle. 4. In this study of fig-wasp-nematode tritrophic interactions in Ficus racemosa within which seven wasp species can breed, we demonstrate using two-choice as well as cafeteria assays that plant-parasitic nematodes (Schistonchus racemosa) do not hitch rides randomly on available eclosing wasps within the fig syconium, but are specifically attracted, at close range, i.e. 3 mm distance, to only that vehicle which can quickly, within a few hours, reliably transfer it to another fig. This vehicle is the female pollinating wasp. Male wasps and female parasitic wasps are inappropriate vehicles since the former are wingless and die within the fig, while the latter never enter another fig. Nematodes distinguished between female pollinating wasps and other female parasitic wasps using volatiles and cuticular hydrocarbons. Nematodes could not distinguish between cuticular hydrocarbons of male and female pollinators but used other cues, such as volatiles, at close range, to find female pollinating wasps with which they have probably had a long history of chemical adaptation. 5. This study opens up new questions and hypotheses about the evolution and maintenance of specificity in fig-wasp-nematode tritrophic interactions.

Conformational analysis of the right-hand twisted antiparallel β-Structure

The conformational analysis of a pair of two-linked peptide units in the anti-parallel arrangement is reported here with a view to study the effect of association of one chain with the other. The pair of two-linked peptide units were fixed in space through the hydrogen bonds between them, in accordance with certain hydrogen bond criteria. Model building was undertaken to ascertain whether the proximity of the side-chains could be used to eliminate any one of the right-hand twisted, left-hand twisted or regular β-structures. Stereochemically, it was found possible with all of them. The preference for a right-hand twisted β-structure, however, was indicated by the classical energy calculations. The relevance of the results thus obtained is discussed in the context of the preferential right-hand twist of the β-pleated sheets present in globular proteins. The agreement between the minimum energy conformations obtained for the pair of two-linked peptide units and the globular protein data is also indicated.

On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations

It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M-1 and M-2 as given by C-d*w = M1(d*t) x Q(t*w).Where d is the number of documents present in the corpus anti w is the size of the vocabulary. The quality of the split depends on ``t'', the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics - this is shown by a `dip' at the right value for `t'.

Simulation and graph mining tools for improving gene mapping efficiency

Gene mapping is a systematic search for genes that affect observable characteristics of an organism. In this thesis we offer computational tools to improve the efficiency of (disease) gene-mapping efforts. In the first part of the thesis we propose an efficient simulation procedure for generating realistic genetical data from isolated populations. Simulated data is useful for evaluating hypothesised gene-mapping study designs and computational analysis tools. As an example of such evaluation, we demonstrate how a population-based study design can be a powerful alternative to traditional family-based designs in association-based gene-mapping projects. In the second part of the thesis we consider a prioritisation of a (typically large) set of putative disease-associated genes acquired from an initial gene-mapping analysis. Prioritisation is necessary to be able to focus on the most promising candidates. We show how to harness the current biomedical knowledge for the prioritisation task by integrating various publicly available biological databases into a weighted biological graph. We then demonstrate how to find and evaluate connections between entities, such as genes and diseases, from this unified schema by graph mining techniques. Finally, in the last part of the thesis, we define the concept of reliable subgraph and the corresponding subgraph extraction problem. Reliable subgraphs concisely describe strong and independent connections between two given vertices in a random graph, and hence they are especially useful for visualising such connections. We propose novel algorithms for extracting reliable subgraphs from large random graphs. The efficiency and scalability of the proposed graph mining methods are backed by extensive experiments on real data. While our application focus is in genetics, the concepts and algorithms can be applied to other domains as well. We demonstrate this generality by considering coauthor graphs in addition to biological graphs in the experiments.

Compression of Weighted Graphs

We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplication, nodes and (unweighted) edges are grouped to supernodes and superedges, respectively, to obtain a smaller graph. We propose models and algorithms for weighted graphs. The interpretation (i.e. decompression) of a compressed, weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one, and that the weight of an edge is approximated to be the weight of the superedge. The compression problem now consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this paper, we formulate this task as the 'simple weighted graph compression problem'. We then propose a much wider class of tasks under the name of 'generalized weighted graph compression problem'. The generalized task extends the optimization to preserve longer-range connectivities between nodes, not just individual edge weights. We study the properties of these problems and propose a range of algorithms to solve them, with dierent balances between complexity and quality of the result. We evaluate the problems and algorithms experimentally on real networks. The results indicate that weighted graphs can be compressed efficiently with relatively little compression error.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).