Establishment of native ecosystems after mining - Australian experience across diverse biogeographic zones

The Australian minerals industry, which is dominated by coal, gold, bauxite, iron ore, base metals and mineral sand operations, is widely scattered across a continent which has a wide range of climatic zones ranging from moist temperate in the south through hot deserts in the centre to moist tropical in the north. There is an emphasis at most mines on establishing native ecosystems after mining, and technologies have had to be developed to ensure successful establishment and stability of these ecosystems under often adverse climatic conditions. This paper describes some of the innovative practices used to establish native ecosystenms in bauxite, mineral sand and coal operations across diverse biogeographic zones. Additionally, brief reference is made to an ecosystem function analysis, which has been developed to assess the success of establishment of these ecosystems. (C) 2001 Elsevier Science B.V. All rights reserved.

Wolbachia infections of phlebotomine sand flies (Diptera: Psychodidae)

Old and New World phlebotomine sand fly species were screened for infection with Wolbachia, intracellular bacterial endosymbionts found in many arthropods and filarial nematodes. Of 53 samples representing 15 species, nine samples of four species were found positive for Wolbachia by polymerase chain reaction amplification using primers for the Wolbachia surface protein (wsp) gene. Five of the wsp gene fragments from four species were cloned, sequenced, and used for phylogenetic analysis. These wsp sequences were placed in three different clades within the arthropod associated Wolbachia (groups A and B), suggesting that Wolbachia has infected sand flies on more than one occasion. Two distantly related sand fly species, Lutzomyia (Psanthyromyia) shannoni (Dyar) and Lutzomyia (Nyssomyia) whitmani (Antunes & Coutinho), infected with an identical Wolbachia strain suggest a very recent horizontal transmission.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Gypsum solubility in seawater, and its application to bauxite residue amelioration

The solubilities and dissolution rates of three gypsum sources (analytical grade (AG), phosphogypsum (PG) and mined gypsum (MG)) with six MG size fractions ((mm) > 2.0, 1.0-2.0, 0.5-1.0, 0.25-0.5, 0.125-0.25, and < 0.125) were investigated in triple deionised water (TDI) and seawater to examine their suitability for bauxite residue amelioration. Gypsum solubility was greater in seawater (3.8 g L 1) than TDI (2.9 g L 1) due to the ionic strength effect, with dissolution in both TDI and seawater following first order kinetics. Dissolution rate constants varied with gypsum source (AR > PG > MG) due to reactivity and surface area differences, with 1:20 gypsum:solution suspensions reaching saturation within 15 s (AR) to 30 min (MG > 2.0). The ability of bauxite residue to adsorb Ca from solution was also examined. The quantity of the total solution Ca adsorbed was found to be small (5 %). These low rates of solution Ca adsorption combined with the comparatively rapid dissolution rates preclude the application of gypsum to the residue sand/seawater slurry as a method for residue amelioration. Instead, direct field application to the residue would ensure more efficient gypsum use. In addition, the formation of a sparingly soluble CaCO3 coating around the gypsum particles after mixing in a highly alkaline seawater/supernatant liquor (SNL) solution greatly reduced the rate of gypsum dissolution.

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

A study of the interaction between operator style and machine capability for a hydraulic mining excavator

This paper presents a case study that explores how operator digging style juxtaposes with mechanical capability for a class of hydraulic mining excavators. The relationships between actuator and digging forces are developed and these are used to identify the excavator's capability to apply forces in various directions. Two distinct modes of operation are examined to see how they relate to the mechanical capabilities of the linkage and to establish if one has merit over the other. It is found that one of these styles results in lower loading of the machine.

Trialing wetlands to treat coal mining wastewaters in a low rainfall, high evaporation environment

The large number of wetlands treating mining wastewaters around the world have mostly been constructed in temperate environments. Wetlands have yet to be proven in low rainfall, high evaporation environments and such conditions are common in many parts of Australia. BHP Australia Coal is researching whether wetlands have potential in central Queensland to treat coal mining wastewaters. In this region, mean annual rainfall is < 650 mm and evaporation > 2 000 mm. A pilot-scale wetland system has been constructed at an open-cut coal mine. The system comprises six treatment cells, each 125 m long and 10 m wide. The system is described in the paper and some initial results presented. Results over the first fourteen months of operation have shown that although pH has not increased enough to enable reuse or release of the water, sulfate reduction has been observed in parts of the system, as shown by the characteristic black precipitate and smell of hydrogen sulfide emanating from the wetlands. These encouraging signs have led to experiments aimed at identifying the factors limiting sulfate reduction. The first experiment, described herein, included four treatments where straw was overlain by soil and the water level varied, being either at the top of the straw, at the top of the soil, or about 5 cm above the soil. The effect of inoculating with sulfate-reducing bacteria was investigated. Two controls were included, one covered and one open, to enable the effect of evaporation to be determined. The final treatment consisted of combined straw/cattle manure overlain with soil. Results showed that sulfate reduction did occur, as demonstrated by pH increases and lowering of sulfate levels. Mean pH of the water was significantly higher after 19 days; in the controls, pH was < 3.3, whereas in the treatments, pH ranged from 5.4 to 6.7. The best improvement in sulfate levels occurred in the straw/cattle manure treatment. (C) 1997 IAWQ. Published by Elsevier Science Ltd.