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- SourceAvailable from: Cameron Freshwater[Show abstract] [Hide abstract]
ABSTRACT: Protecting and promoting recovery of species at risk of extinction is a critical component of biodiversity conservation. In Canada, the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) determines whether species are at risk of extinction or extirpation, and has conducted these assessments since 1977. We examined trends in COSEWIC assessments to identify whether at-risk species that have been assessed more than once tended to improve, remain constant, or deteriorate in status, as a way of assessing the effectiveness of biodiversity conservation in Canada. Of 369 species that met our criteria for examination, 115 deteriorated, 202 remained unchanged, and 52 improved in status. Only 20 species (5.4%) improved to the point where they were 'not at risk', and five of those were due to increased sampling efforts rather than an increase in population size. Species outcomes were also dependent on the severity of their initial assessment; for example, 47% of species that were initially listed as special concern deteriorated between assessments. After receiving an at-risk assessment by COSEWIC, a species is considered for listing under the federal Species at Risk Act (SARA), which is the primary national tool that mandates protection for at-risk species. We examined whether SARA-listing was associated with improved COSEWIC assessment outcomes relative to unlisted species. Of 305 species that had multiple assessments and were SARA-listed, 221 were listed at a level that required identification and protection of critical habitat; however, critical habitat was fully identified for only 56 of these species. We suggest that the Canadian government should formally identify and protect critical habitat, as is required by existing legislation. In addition, our finding that at-risk species in Canada rarely recover leads us to recommend that every effort be made to actively prevent species from becoming at-risk in the first place.
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ABSTRACT: Two studies, which took social categorization and right-wing authoritarianism into account two factors unexamined jointly in previous research, explored reporting of potential terrorist threats in cyberspace in a generally Muslim-intolerant ‘Eastern European’ setting. In Study 1, 92 white men observed a simulated online encounter between an anti-Muslim extremist and two of his apparent followers ‘planning to do something big and violent.’ The results show that participants took less time to respond to the terrorist threat if Muslims were referred to in terms of a broad European identity rather than a narrow national identity, but only when participants were primed with the broad identity. In Study 2, where a separate group of 100 white men observed the same encounter, the focus was put on the mono-cultural and multicultural primes of their national identity. The response time was shorter when participants were primed with the multicultural contents, but only when the Muslim outgroup was described in terms of national rather than European inclusion. Providing theoretical guidelines for facilitating the reporting of potential terrorist threats in cyberspace, this work complements the classical social identity model of helping by revealing new layers of complex interactive categorization and their potential application to mobilizing counter-terrorist community responses.
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ABSTRACT: In self-hosted environments it was feared that Business Intelligence (BI) will eventually face a resource crunch situation due to the never ending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on Cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the Cloud. The Cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a Cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results reflected that extensible parallel processing of database servers on the Cloud can efficiently process OLAP application demands on Cloud computing.
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