Reactions of polyhalogenated aromatic compounds and related ethers with metal/ammonia solutions

This work contains the results of a series of reduction studies on polyhalogenated aromatic compounds and related ethers using alkali metals in liquid ammonia. In general, polychlorobenzenes were reduced to t he parent aromatic hydrocarbon or to 1 ,4-cyc1ohexadiene, and dipheny1ethers were cleaved to the aroma tic hydrocarbon and a phenol. Chlorinated dipheny1ethers were r eductive1y dechlorinated in the process. For example, 4-chlorodipheny1- ether gave benzene and phenol. Pentach1orobenzene and certain tetrachlorobenzenes disproportionated to a fair degree during the reduction process if no added proton source was present. The disproportionation was attributed to a build-up of amide ion. Addition of ethanol completely suppressed the formation of any disproportionation products. In the reductions of certain dipheny1ethers , the reduction of one or both of the dipheny1ether rings occurred, along with the normal cleavage. This was more prevalent when lithium was the metal used . As a Sidelight, certain chloropheno1s were readily dechlorinated. In light of these results, the reductive detoxification of the chlorinated dibenzo-1,4-dioxins seems possible with alkali metals in l iquid ammonia.

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research.

Process Improvement in Phlebotomy

This study has two main objectives. First, the phlebotomy process at the St. Catharines Site of the Niagara Health System is investigated, which starts when an order for a blood test is placed, and ends when the specimen arrives at the lab. The performance measurement is the flow time of the process, which reflects concerns and interests of both the hospital and the patients. Three popular operational methodologies are applied to reduce the flow time and improve the process: DMAIC from Six Sigma, lean principles and simulation modeling. Potential suggestions are provided for the St. Catharines Site, which could result in an average of seven minutes reduction in the flow time. The second objective addresses the fact that these three methodologies have not been combined before in a process improvement effort. A structured framework combining them is developed to benefit future study of phlebotomy and other hospital processes.