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The goal of harnessing our biodiversity to bring health and wealth to the people living in the Caribbean Region got a boost recently courtesy of a 2016 IUCN project entitled ‘Advancing the Nagoya Protocol in Countries of the Caribbean Region’ that had five components. This project was commissioned by eight governments (Antigua and Barbuda, Barbados, Grenada, Guyana, Jamaica, Saint Kitts and Nevis, Saint Lucia, Trinidad and Tobago) with GEF funding, had UNEP as its Implementing Agency and the International Union for the Conservation of Nature (IUCN) as the Executing Agency.
The control of invasive species in crops with low tolerance are seen as a public good. This makes it a collective responsibility led by government. This is done directly through public expenditure on control measures or indirectly through incentives to people whose actions may be a contributing factor to the problem. The risks associated with invasive species have been increasing especially with globalization but are changing in nature thus warranting novel strategies for their management.
At the inception of automated solar tracking in the 1970’s, geometric architectures with pair(/s) of solid-state photo-sensitive devices were constructed and used to detect the sun’s position. As an alternative in recent years, cameras have been used to capture and process live sky images to detect the sun’s position. When the sky is cloudy however, both approaches are prone to errors and sometimes require human intervention which tend to reduce the trackers’ economic viability [1].
Plant viruses are responsible for significant losses in crop production annually. Infections are often exacerbated by mixed infections. One strategy of combatting viral disease spread lies in swift diagnoses so that immediate interventions can be employed to slow or stop their spread. Sweet pepper, hot pepper, and tomato are among the most important cash crops in Jamaica and are constantly threatened by pathogens.
In parallel and distributed computing environments, task scheduling, where the basic idea is minimizing time loss and maximizing performance, is an absolutely critical component. Scheduling in these environments is NP-hard, so it is important that we continue to search and find the most efficient and effective ways of mapping tasks to processors. One such effective approach is known as Ant Colony Optimization (ACO). This popular optimization technique is inspired by the foraging behavior of ants in their colonies to find the shortest paths between their nests and food sources.
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