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Grand Innovation Challenge

The FORECAST Grand Innovation Challenge 2022 (GIC) is to develop a feasible innovation (prototype/product/concept) that will revolutionize the management, protection, and use of water resources in the Caribbean.

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Day 2
  —  
2:05 pm

BIOPROSPECTING AND BIOPIRACY IN THE CARIBBEAN: CHALLENGES AND OPPORTUNITIES

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.

Day 4
  —  
9:20 am

FUNCTIONAL GROUP GATING OF DIBENZO-18-CROWN-6 TOWARDS THE DEVELOPMENT OF LEAD (II) ION SENSING ELECTRODES

Lead, a well-known neurotoxin, remains environmentally abundant, arising from many natural and synthetic processes which encourage its environmental accumulation and hence, increased interactions with flora and fauna. Therefore, tremendous research efforts have been invested into developing various methods for its analysis and sequestration, however, affordability, sensitivity and selectivity still remain formidable challenges in this area and hence here is room for further exploration.

Day 4
  —  
10:00 am

AN ACO-BASED ALGORITHM FOR HETEROGENEOUS COMPUTING ENVIRONMRNTS USING PRIORITY QUEUES

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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