published_or_final_version / Chemistry / Master / Master of Philosophy

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No description available.

No description available.

Hypotheses regarding the distribution, texture and trace element concentrations of lake sediments were tested by empirical analyses of multi-lake data sets (52 to 83 lakes). Sediment distribution was best characterized by the deposition boundary depth (DBD), the abrupt transition from coarse- to fine-grained sediments. The DBD can now be predicted from either empirical models or empirical-theoretical simplifications of wave of sediment threshold theory, both in terms of exposure (or fetch) and bottom slope. The texture (organic content, water content and bulk density) of profundal sediments was related to the inorganic sedimentation rate and exposure, but not to the lake trophic status or the net organic matter sedimentation rate. The relationships between sediment texture and intra- and inter-site variability, together with the models that predict the DBD and sediment texture, were used to develop an algorithm that should greatly reduce sampling effort in lake sediment surveys. Finally, sediment trace element concentrations were predicted from sediment texture, site depth and simple geologic classifications. The models developed here, provide a framework in which to sample lake sediments and interpret their properties.

About 25,000 acres of flax for fiber was grown during the second world war in the Province of Québec when the traditional sources of supply in Europe were cut off (Anonymous). With the armistice, the prices dropped and farmers abandoned this crop because of competition from European countries. In the 1950's a few farmers became interested in growing flax for linseed oil, chiefly in the Montreal area. The acreage bas increased steadily, so that by 1964 flax was being grown on about 35,000 acres in this Province (Shiller 1964). [...]

Software maintenance is a significant phase of a software life-cycle. Once a system is developed the main focus shifts to maintenance to keep the system up to date. A system may be changed for various reasons such as fulfilling customer requirements, fixing bugs or optimizing existing code. Code needs to be studied and understood before any modification is done to it. Understanding code is a time intensive and often complicated part of software maintenance that is supported by documentation and various tools such as profilers, debuggers and source code analysis techniques. However, most of the tools fail to assist in locating the portions of the code that implement the functionality the software developer is focusing. Mining execution traces can help developers identify parts of the source code specific to the functionality of interest and at the same time help them understand the behaviour of the code. We propose a use-driven hybrid framework of static and dynamic analyses to mine and manage execution traces to support software developers in understanding how the system's functionality is implemented through feature analysis. We express a system's use as a set of tests. In our approach, we develop a set of uses that represents how a system is used or how a user uses some specific functionality. Each use set describes a user's interaction with the system. To manage large and complex traces we organize them by system use and segment them by user interface events. The segmented traces are also clustered based on internal and external method types. The clusters are further categorized into groups based on application programming interfaces and active clones. To further support comprehension we propose a taxonomy of metrics which are used to quantify the trace. To validate the framework we built a tool called TrAM that implements trace mining and provides visualization features. It can quantify the trace method information, mine similar code fragments called active clones, cluster methods based on types, categorise them based on groups and quantify their behavioural aspects using a set of metrics. The tool also lets the users visualize the design and implementation of a system using images, filtering, grouping, event and system use, and present them with values calculated using trace, group, clone and method metrics. We also conducted a case study on five different subject systems using the tool to determine the dynamic properties of the source code clones at runtime and answer three research questions using our findings. We compared our tool with trace mining tools and profilers in terms of features, and scenarios. Finally, we evaluated TrAM by conducting a user study on its effectiveness, usability and information management.

This work investigates the problem of location methods putting load on GSM network. The work carried out covers analysis of various location methods, identification of network nodes and datapaths that are involved in getting location data from GSM network. It is shown that methods can generally be divided into two groups: those that use special devices to locate and then transfer results via GSM network and those that use GSM network for positioning. The impact of first group on GSM network load is limited to the time of data transfer time that depends on the coding scheme used, data transfer channels allocated and size of location data. The impact of second group depends on algorithm used. Analysis and experiments carried out using custom GSM network emulator show that standard “Provide Subscriber Info” based algorithm requires mandatory update of location information within GSM network using either SMS or preempted call setup that both require radio resources. Usage of “Customer Trace” algorithm for self-location applications is proposed to save radio resources. Additional benefits of this algorithm include usage of timing advance and signal strength information from “Customer Trace” data to increase the accuracy of location within current GSM network infrastructure.

No description available.

No description available.