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https://viurrspace.ca/bitstream/handle/10613/18174/Apr29-1898.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/17011/May18-1900.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/19132/Jun26-1875.pdf?sequence=2&isAllowed=y

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

https://viurrspace.ca/bitstream/handle/10613/18192/May19-1898.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/19123/May22-1875.pdf?sequence=2&isAllowed=y

In PIMENTO we advocate a novel approach to XML search that leverages user information to return more relevant query answers. This approach is based on formalizing {em user profiles} in terms of {em scoping rules} which are used to rewrite an input query, and of {em ordering rules} which are combined with query scoring to customize the ranking of query answers to specific users.

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. At this scenario, self-optimizing arise as the ability of the agent to monitor its state and performance and proactively tune itself to respond to environmental stimuli.

https://viurrspace.ca/bitstream/handle/10613/21398/Mar09-1891.pdf?sequence=2&isAllowed=y

https://viurrspace.ca/bitstream/handle/10613/19145/Aug18-1875.pdf?sequence=2&isAllowed=y