Supplemental water sprays were placed along the sides of a continuous mining machine to improve suppression and confinement of the dust cloud under the cutting boom to reduce downwind dust levels. Using a full-scale mock-up of a continuous mining machine, preliminary work showed that these side sprays improved suppression of the dust cloud and redistributed this cloud under the cutting head. Th...

The goal of data mining is to discover hidden useful information in large databases. Mining frequent patterns from transaction databases is an important problem in data mining. As the database size increases, the computation time and required memory also increase. Base on this, we use the MapReduce programming mode which has parallel processing ability to analysis the large-scale network. All t...

The localisation of defects in computer programmes is essential in software engineering and is important in domain-specific data mining. Existing techniques which build on call-graph mining localise defects well, but do not scale for large software projects. This paper presents a hierarchical approach with good scalability characteristics. It makes use of novel call-graph representations, frequ...

Data repositories of interest in data mining applications can be very large. Many of the existing learning algorithms do not scale up to extremely large data set. One approach to deal with this problem is to apply the concept of incremental learning. However, incremental data mining is not the same as incremental machine learning. The former handles one subset of data at a time, whereas the lat...

When people are morally convicted regarding a specific issue, these convictions exert a powerful influence on their attitudes and behavior. In the current research we examined whether there are boundary conditions to the influence of this effect. Specifically, whether in the context of salient economic rewards, moral convictions may become weaker predictors of attitudes regarding resource use. ...

Frequent pattern mining is an essential data mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. In...

Rare earth elements (REEs) are becoming significant due to their huge applications in many industries, large-scale mining and refining activities. Increasing usage of such metals pose negative environmental impacts. In this research ICP-MS has been used to analyze soil samples collected from former ex-mining areas in the depths of 0-20 cm, 21-40 cm, and 41-60 cm of residential, mining, natural,...

Association rule mining is an important data mining problem. Since its inception, different variants of rules has been proposed in the literature. In each case, different attributes (e.g., weight and quantity) are considered to obtain more informative rules. To our knowledge, each proposal is based on the Apriori algorithm that is, in modern context, inefficient. Methods that outperform the Apr...

Mining chemical compounds in silico has drawn increasing attention from both academia and pharmaceutical industry due to its effectiveness in aiding the drug discovery process. Since graphs are the natural representation for chemical compounds, most of the mining algorithms focus on mining chemical graphs. Chemical graph mining approaches have many applications in the drug discovery process tha...

OBJECTIVES This cross-sectional study aimed at the environmental health hazards at work and cyanide exposure of small-scale gold miners engaged in gold extraction from ores in a mining area in the Philippines. METHODS Methods consisted of structured questionnaire-guided interviews, work process observation tools, physical health assessment by medical doctors, and laboratory examination and bl...