No description available.

No description available.

Signal detection from Adverse Event Reports (AERs) is important for identifying and analysing drug safety concern after a drug has been released into the market. A safety signal is defined as a possible causal relation between an adverse event and a drug. There are a number of safety signal detection algorithms available for detecting drug safety concern. They compare the ratio of observed count to expected count to find instances of disproportionate reportings of an event for a drug or combination of events for a drug. In this thesis, we present an algorithm to mine the AERs to identify drugs which show sudden and large changes in patterns of reporting of adverse events. Unlike other algorithms, the proposed algorithm creates time series for each drug and use it to identify start of a potential safety problem. A novel vectorized timeseries utilizing multiple attributes has been proposed here. First a time series with a small time period was created; then to remove local variations of the number of reports in a time period, a time-window based averaging was done. This method helped to keep a relatively long time-series, but eliminated local variations. The steps in the algorithm include partitioning the counts on attribute values, creating a vector out of the partitioned counts for each time period, use of a sliding time window, normalizing the vectors and computing vector differences to find the changes in reporting over time. Weights have been assigned to attributes to highlight changes in the more significant attributes. The algorithm was tested with Adverse Event Reporting System (AERS) datasets from Food and Drug Administation (FDA). From AERS datasets the proposed algorithm identified five drugs that may have safety concern. After searching literature and the Internet it was found that the five drugs the algorithm identified, two were recalled, one was suspended, one had to undergo label change and the other one has a lawsuit pending against it.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / DataProbeCLASSIC is the new PC-based version of the classic tool for telemetry data analysis and visualization. DataProbe was the brainchild of the Unites States Navy and its contractors. At a time when computer terminals were expensive and graphical visualization of data was cutting edge, this software product was specifically designed to process time-series data in an efficient manner. The primary strength of DataProbe is the capability to read specific data items for specific time slices from very large data files rather than reading the entire data file into memory. The efficiency and versatility of the product was quickly noted, and it gained widespread use within the testing community. This paper presents a brief history of the legacy product and discusses the features and strengths of new implementation.

In order to optimise the Resonance Enhanced Drilling (RED) performance in different rock formations, it is important to understand both the influence of the system parameters on the drilling dynamics, and other measures that are involved in the drilling operation. This work studies the dynamic behaviour of the drilling system. It also investigates the influence of various system parameters on the drilling module dynamics in order to identify in real-time the formation being drilled from the dynamical responses of the drilling assembly. It also aims to optimise the selection of the operating parameters for a drilled formation, resulting in an improvement in the Rate Of Penetration (ROP). A nonlinear time series analysis approach has been used to infer the changes in the system parameters from subtle changes in the system dynamics. Using the acceleration time-series as a measurement of simulated and experimental impact oscillators representing a model for drilling conditions with intermittent impacts, the systems attractors were reconstructed and characterised. It is shown that the stiffness correlates well with the topology of the reconstructed attractor. Nonimpacting trajectories formed an approximate plane within the three dimensional reconstructed phase-space. Contact with the constraint caused a systematic deviation from the linear subspace, the inclination of which, measured by statistics of the tangent vector, can be used to infer the stiffness. Based on the developed framework it is now possible to classify stiffness of the impacted material from a single variable in a simple way and in real-time. An experimental impact drilling rig was designed, built and used to study the influence of the system parameters on the high-frequency impact drilling. The newly designed rig is a smaller and simpler apparatus. It is designed to mimic the actual RED apparatus in terms of providing controlled axial vibration into a conventional rotary drilling whilst avoiding complications that might arise from including all RED rig elements. An instrumentation and sensing system was also developed to measure motions and forces resulting from the dynamic interactions between the drill-bit and the rock formations.

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published_or_final_version / Statistics / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy