Human monkeypox represents a relatively underexplored infection that has received increased attention since the reported outbreak in May 2022. Due to its clinical similarities with human smallpox, this virus represents a potentially tremendous health problem demanding further research in the context of host-pathogen interactions and vaccine development. Furthermore, the cross-continental spread of monkeypox has reaffirmed the need for devoting attention to human poxviruses in general, as they represent potential bioterrorism agents. Currently, smallpox vaccines are utilized in immunization efforts against monkeypox, an unsurprising fact considering their genomic and phenotypic similarities. Though it offers long-lasting protection against smallpox, its protective effects against human monkeypox continue to be explored, with encouraging results. Taking this into account, this works aims at utilizing in silico tools to identify potent peptide-based epitopes stemming from the variola virus and monkeypox virus proteomes, to devise a vaccine that would offer significant protection against smallpox and monkeypox. In theory, a vaccine that offers cross-protection against variola and monkeypox would also protect against related viruses, at least in severe clinical manifestation. Herein, we introduce a novel multi-epitope mRNA vaccine design that exploits these two viral proteomes to elicit long-lasting humoral and cellular immunity. Special consideration was taken in ensuring that the vaccine candidate elicits a Th1 immune response, correlated with protection against clinically severe disease for both viruses. Immune system simulations and physicochemical and safety analyses characterize our vaccine candidate as antigenically potent, safe, and overall stable. The protein product displays high binding affinity towards relevant immune receptors. Furthermore, the vaccine candidate is to elicit a protective, humoral and Th1-dominated cellular immune response that lasts over five years. Lastly, we build a case about the rapidity and convenience of circumventing the live attenuated vaccine platform using mRNA vaccine technology.

Drone simulators are often used to reduce training costs and prepare operators for various ad-hoc scenarios, as well as to test the quality of algorithmic and communication aspects in collaborative scenarios. An important aspect of drone missions in simulated (as well as real life) environments is the operational lifetime of a given drone, in both solo and collaborative fleet settings. Its importance stems from the fact that the capacity of the on-board batteries in untethered (i.e., free-flying) drones determines the range and/or the length of the trajectory that a drone can travel in the course of its surveilance or delivery missions. Most of the existing simulators incorporate some kind of a consumption model based on different parameters of the drone and its flight trajectory. However, to our knowledge, the existing simulators are not capable of incorporating data obtained from actual physical measurements/observations into the consumption model. In this work, we take a first step towards enabling the (users of) drones simulator to incorporate the speed and direction of the wind into the model and monitor its impact on the battery consumption as the direction of the flight changes relative to the wind. We have also developed a proof-of-concept implementation with DJI Mavic 3 and Parrot ANAFI drones.