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The malaria epidemic was responsible for about 241 million infectious cases and 627,000 deaths worldwide in 2020.[1] This infectious disease, transmitted by the female Anopheles mosquito, is caused by parasites of the genus Plasmodium namely P. falciparum, P. vivax, P. malariae, P. knowlesi, P. ovale curtisi and P. ovale wallikeri.[2,3] Also, malaria is found predominantly in the highlands of Africa which accounts for more than 90% of infections worldwide. While there has been some success in the treatment of malaria, its eradication has been negatively impacted by insecticide and drug resistance. With emergence of thiosemicarbazone as antimalarial agents, the combination of pyridine and amide or thioamide moieties into one scaffold makes for an interesting target.[4]
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.
Invasive alien species (IAS) are implicated in the extinction or decline of numerous native aquatic species worldwide. Their negative impacts occur through mechanisms including habitat alteration, competition, predation, hybridisation, and the spread of disease (Strayer et al. 2006). Small island ecosystems are most susceptible to the impacts of IAS. Once established, freshwater IAS are difficult to eradicate without negatively impacting native species.
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