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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.
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].
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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