Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

This is the post-print version of the final paper published in Advanced Engineering Informatics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.E

FRS 12: an inter-industry study of its impact on share prices

This paper assesses the impact of the publication of FRS No. 12, 'Provisions, Contingent Liabilities and Contingent Assets' in 1998 on the share prices of UK companies. Although the standard affects all UK companies (restricting "big bath" provisions), it specifically requires extractive firms to make provisions for abandonment costs at the outset of the project. This additional requirement may cause FRS 12 to have a larger impact on companies in extractive industries compared to other companies. Using event study methodology, we find a positive share price impact on the release of FRS 12 for both extractive and other affected firms, although the abnormal returns are substantially lower for extractive firms. This suggests that, while investors welcomed the increased disclosure requirements, the mandatory requirements set by FRS 12 may be onerous for extractive firms. The abnormal returns were significantly lower for those firms reporting significantly increased provisions after the introduction of the new standard, consistent with the new provision requirements being costly for the companies most directly affected

Hurricanes and Long-term GDP Growth: The Role of Institutional Quality

This paper compares the long-term effects on real per-capita GDP of two hurricanes in 1992, hurricane Andrew in Florida and hurricane Iniki in Hawaii. The literature suggests that the long-term effect on GDP of a natural disaster for a region with good pre-disaster institutional quality may be positive (i.e., GDP levels exceed those which would have materialized without the disaster) because the destruction of capital induces firms to investment in more technologically advanced structures and machines. In contrast, a region with bad pre-disaster institutional quality should experience a negative impact because it face severe limits in the amount it can borrow in international markets to replace the destroyed capital. If this claim holds, Florida, a state with poorer institutional quality, should not have performed as well as Hawaii, a state with stronger institutions, after each was hit by a hurricane in 1992. By analyzing twenty years of data for the two states using the synthetic control method, this paper shows that the pre-disaster institutional quality was not a powerful determinant of the long-term GDP growth in these two states. That is, Hawaii’s observed per-capita GDP values remained significantly lower than what Hawaii would have experienced without hurricane Iniki, while the gap between the observed values and the expected values was smaller for Florida. I speculate that other differences between these two economies, such as their size or proximity to the U.S. mainland, might explain why Hawaii was more adversely affected by hurricane Iniki