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A week before the ride takes off, organizers are aiming for a Guinness World Record attempt for the most participants in a 30 minute Swiss Ball exercise class on June 8 at the Canadore College gymnasium. In the same location.Volunteers and representatives from Cor Maximus, All State Insurance and Rebuilt Resources will be on hand.There is no charge to participate and those taking part do not have to bring a Swiss Ball. All will be provided.The exercise class is extremely low impact and anyone from eight to 80 can participate.The current world record is 454 held by the Greater Toronto YMCA.All funds raised from the attempt go toward raising awareness and research in the fight against prostate cancer in Nipissing.Article content “Prostate cancer is a needless killer for men,” says Ray Yelle, co chair of the Nipissing chapter.”The more the community is aware and educated about prostate cancer, the less chance there is to lose the men in our lives to this terrible disease.
It has been widely acknowledged that collaboration across disciplines is required in order to develop innovative, sustainable textile solutions that address complex societal problems (Kane Philpott, 2013; Igoe, 2010). Potential to develop life changing innovations in the field of advanced textiles for medical and healthcare has been identified as a key growth sector within Scotland, with collaborative cross disciplinary user focused design approaches recognised as central to developing new concepts that address human needs (Malins et al. 2012).
Furthermore, this approach does not place any constraints on the length of the Arabic composite name. The results of our experiments show that there are improvement in recognizing Arabic composite names entity in the Arabic language text.Our research also contributes a novel, knowledge based approach to relation extraction from unstructured Arabic text, which is based on the principles of Functional Discourse Grammar (FDG). We further improve the approach by integrating it with Machine Learning relation classification, resulting in a hybrid relation extraction algorithm that can handle especially complex Arabic sentence structures.