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.

Effect of Cu Toxicity on Growth of Cowpea (Vigna unguiculata)

Accurate determination of the rhizotoxicity of Cu in dilute nutrient solutions is hindered by the difficulty of maintaining constant, pre-determined concentrations of Cu (micromolar) in solution. The critical Cu2+ activity associated with a reduction in the growth of solution-grown cowpea (Vigna unguiculata (L.) Walp. cv Caloona) was determined in a system in which Cu was maintained constant through the use of a cation exchange resin. The growth of roots and shoots was found to be reduced at solution Cu2+ activities ≥ 1.7 µM (corresponding to 90 % maximum growth). Although root growth was most likely reduced due to a direct Cu2+ toxicity, it is considered that the shoot growth reduction is attributable to a decrease in tissue concentrations of K, Ca, Mg, and Fe and the formation of interveinal chlorosis. At high Cu2+ activities, roots were brown in color, short and thick, had bent root tips with cracking of the epidermis and outer cortex, and had local swellings behind the roots tips due to a reduction in cell elongation. Root hair growth was reduced at concentrations lower than that which caused a significant reduction in overall root fresh weight.

Catalytic activities and coking characteristics of oxides-supported Ni catalysts for CH4 reforming with carbon dioxide

Catalytic activities and deactivation characteristics of oxides-supported nickel catalysts for the reaction of methane reforming with carbon dioxide were investigated. The dynamic carbon deposition on various nickel catalysts was also studied by a thermogravimetric method. Among the catalysts prepared, Ni/La2O3, Ni/alpha-Al2O3, Ni/SiO2, and Ni/CeO2 showed very high CH4 and CO2 conversions and moderate deactivation whereas Ni/MgO and Ni/TiO2 had lower conversions when the Ni reduction was conducted at 500 degrees C. When Ni/MgO catalyst was reduced at 800 degrees C, it exhibited not only comparable conversions of CH4 and CO2 with other active catalysts but also much longer period of stability without deactivation. The amount of carbon deposited in Ni-based catalysts varied depending on the nature of support and followed the order of Ni/La2O3 > Ni/alpha-Al2O3 > Ni/SiO2 > Ni/MgO > Ni/CeO2 at 700 degrees C. The carbons formed on the catalyst surface showed different structural and chemical properties, and these in turn affected the catalytic activity of the catalysts.

Inactive Australian college students' preferred activities, sources of assistance, and motivators

Participation in regular physical activity reduces the risk of cardiovascular disease and all-cause mortality as well as providing numerous health benefits.' The steepest decline in physical activity occurs during adolescence (approximately 15 to 18 years of age) and young adulthood (20 to 25 years).(2) Australian population studies have found that levels of physical inactivity are twice as high for those 20 to 29 years old as they are for those under 20 years old.(3,4) As college students move through this period of changing roles within family and peer groups, they may be expected to have specific preferences and expected outcomes for physical activity participation that are different from those they had previously as high school students.(5) Studies of physical activity determinants suggest that while there are some similarities between males and females, there are differences in preferences for specific types of activity.(6) Calfas et al.(5) found that women reported body image factors (weight loss, dissatisfaction with body) to be more motivating, while young men rated strength (muscle gain, muscle tone) and social aspects (organized competition, meeting people) of physical activity more highly than did young women. We examined preferred physical activities, sources of assistance to be more active, and perceived motivators for activity in a sample of inactive college students. Differences between males and females were examined, and the implications for campus-based physical activity promotion strategies are considered.

Participation in physical activities by older Australians: a review of the social psychological benefits and constraints

Participation in physical activities has been found to be an important factor in contributing to a healthy lifestyle. Research has found strong relationships between participation in regular physical activity and the prevention of disease, while its relationship to the psychological and social dimensions have been neglected. Recently however, several studies have found causal relationships between physical activity and improved mood state, reduced anxiety, reduced depression, and increased social support. Despite this, surveys indicate that participation levels in physical activities are declining among older Australians, with the exceptions of walking and gardening. This paper also examines constraints to participation in leisure programs, such as lack of time, poor health, fear of crime, the financial cost and the lack of a partner to participate with. A number of strategies have been suggested to overcome these constraints.

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.