Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Self-reported risk perception, mood and risk behaviour across the human menstrual cycle

Normally ovulating women exhibit a decline in risk behaviours that may lead to sexual assault during the fertile phase of the menstrual cycle, whereas women using the Pill do not. The current study tests two explanatory models: the mood and fertility models. Self-reported risk and non-risk behaviours, mood, and risk perception in sexual assault and physical risk domains were assessed by testing fiftyone women at menstruation and during their fertile period. Based on the decline in risk behaviours shown in past research, the fertility model predicts that normally ovulating women will display greater risk perception during the fertile phase of their cycle. The mood model predicts that at menstruation, when negative mood is highest, risk perception will be increased and risk behaviours correspondingly reduced. Risk behaviours did not vary over the cycle or between groups. Overall, results support the mood model. Negative mood was greater at menstruation and positive mood during the fertile period for both groups, rational risk perception was correspondingly greater at menstruation. The fertility model was not supported as risk perception ratings did not vary in the expected direction and ratings were not specific to the sexual assault domain.

Security and reliability of LPDC based public-key cryptosystems

Security and reliability of LDPC based public-key cryptosystems are discussed and analysed. We study attacks on the cryptosystem when partial knowledge of one or more of the private key components and/or of the plaintext have been acquired.

People, technology, processes and risk knowledge sharing

The present global economic crisis creates doubts about the good use of accumulated experience and knowledge in managing risk in financial services. Typically, risk management practice does not use knowledge management (KM) to improve and to develop new answers to the threats. A key reason is that it is not clear how to break down the “organizational silos” view of risk management (RM) that is commonly taken. As a result, there has been relatively little work on finding the relationships between RM and KM. We have been doing research for the last couple of years on the identification of relationships between these two disciplines. At ECKM 2007 we presented a general review of the literature(s) and some hypotheses for starting research on KM and its relationship to the perceived value of enterprise risk management. This article presents findings based on our preliminary analyses, concentrating on those factors affecting the perceived quality of risk knowledge sharing. These come from a questionnaire survey of RM employees in organisations in the financial services sector, which yielded 121 responses. We have included five explanatory variables for the perceived quality of risk knowledge sharing. These comprised two variables relating to people (organizational capacity for work coordination and perceived quality of communication among groups), one relating to process (perceived quality of risk control) and two related to technology (web channel functionality and RM information system functionality). Our findings so far are that four of these five variables have a significant positive association with the perceived quality of risk knowledge sharing: contrary to expectations, web channel functionality did not have a significant association. Indeed, in some of our exploratory regression studies its coefficient (although not significant) was negative. In stepwise regression, the variable organizational capacity for work coordination accounted for by far the largest part of the variation in the dependent variable perceived quality of risk knowledge sharing. The “people” variables thus appear to have the greatest influence on the perceived quality of risk knowledge sharing, even in a sector that relies heavily on technology and on quantitative approaches to decision making. We have also found similar results with the dependent variable perceived value of Enterprise Risk Management (ERM) implementation.