Ectoparasites of brown rats (Rattus norvegicus) in Grenada, West Indies

Background and Aim: Arthropod ectoparasites (mites, lice, ticks, and fleas) on common house rats or brown rats (Rattus norvegicus) are known to transmit zoonotic pathogens and diminish rat health. To the best of our knowledge, there is no published information regarding the prevalence of ectoparasites on R. norvegicus in Grenada. This study aimed to determine the prevalence and types of ectoparasites present on brown rats from Grenada. Materials and Methods: One hundred sixty-eight rats were collected live from the parishes of St. George and St. David, Grenada, from May to July 2017. Following euthanasia, external parasites were collected using fine combs, thumb forceps, and paper tape. Tape samples and free specimens were placed in containers with 70% ethanol. External parasites were evaluated using dissection microscopy. Results: Over 2000 ectoparasites were collected from 149 of the 168 trapped rats (88.7%). Ectoparasites identified included mesostigmatid mites (found on 84.6% of infested rats), fur mites in the families Atopomelidae and Listrophoridae (67.1%), Polyplax spp. lice (6.7%), fleas (3.4%), an unidentified larval tick (0.7%), and a mite in the family Myobiidae (0.7%). Infestation rates were higher in St. David Parish (86/89; 96.6%) than in St. George Parish (63/79; 79.7%) (p=0.001). When comparing sex and age, males had a marginal increase in infestation rate (83/89; 93.3%) compared to females (66/79; 83.5%) (p=0.054), and adults had an infestation rate of 90.7% (97/107) compared to juvenile rats who had a 66.7% (14/21) infestation rate. Conclusion: Brown rats in Grenada are heavily infested with ectoparasites, including known vectors of pathogens. Future studies will examine the prevalence of zoonotic pathogens in these arthropods.

Fault Diagnosis for UAV Blades Using Artificial Neural Network

In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade.