Exposure to traumatic events could increase post-traumatic stress disorder (PTSD) risk among enlisted U.S. Army drone operators. Published research on PTSD risk in this population is unavailable. We used a combined medical and administrative longitudinal dataset to examine adjusted associations between drone operator service among U.S. Army enlisted members and three PTSD indicators: whether screened via the PTSD Checklist - Civilian (PCL-C); PCL-C scores; and incident PTSD diagnoses. We compiled summary statistics for and conducted tests of differences in independent variable distributions when comparing drone operators and others. Two multivariable survival regression models and an ordinary least squares model were used to estimate adjusted associations. There were 1.68 million person-years of observed time in the study population ( = 678,548; drone operator = 2856). Compared to other servicemembers, the adjusted likelihood of undergoing PTSD screening was 35% lower [95% confidence interval (CI) for the adjusted hazard ratio (aHR): 0.56-0.76]. Among subjects who took the PCL-C, scores did not differ significantly on the basis of drone operator service (adjusted change: -1.26 points; CI: -3.41-0.89). The adjusted hazard of receiving a PTSD diagnosis was 34% lower among drone operators (CI: 0.54-0.80). These findings provide reassurance that enlisted U.S. Army drone operators are not at increased risk of PTSD. Further research is needed in order to identify the mechanisms of the decreased PTSD risk observed, and whether other or longer-term mental health risks are present among those in this occupation.
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http://dx.doi.org/10.3357/AMHP.6016.2022 | DOI Listing |
Pest Manag Sci
January 2025
Silsoe Spray Applications Unit Ltd, Bedford, UK.
Background: To use unmanned aerial vehicles (UAVs) to deliver pesticides, new data are needed to allow regulators to conduct risk assessments. A field trial was conducted to obtain spray drift data relating to ground deposits and airborne spray resulting from a spray application delivered by a small UAV.
Results: A 12 m width area was sprayed with four passes of the UAV and spray deposits were collected within the sprayed area and up to 50 m downwind.
HardwareX
March 2025
Instituto de Investigacion Astronomico y Aeroespacial Pedro Paulet, Universidad Nacional de San Agustin de Arequipa, 04000, Arequipa, Peru.
Inertial navigation systems (INS) are widely used in commercial aviation, maritime navigation, and unmanned vehicle guidance. However, these systems are often sensitive, costly, and challenging to access. To address these limitations, an open-source, low-cost platform named INS OpenNavSense has been developed.
View Article and Find Full Text PDFPLoS One
January 2025
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia.
The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Multirotor drones are widely used in fields such as environmental monitoring, agricultural inspection, and package delivery, but they still face numerous challenges in durability and aerial operation capabilities. To address these issues, this paper presents a biomimetic leg-claw mechanism (LCM) inspired by the biomechanics of birds. The claw of the LCM adopts a bistable gripper design that can rapidly close through external impact or actively close via the coordination of internal mechanisms.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Competence Center for Renewable Energies and Energy Efficiency, Hamburg University of Applied Sciences, Hamburg, Germany.
With the increasing height and rotor diameter of wind turbines, bat activity monitoring within the risk area becomes more challenging. This study investigates the impact of Unmanned Aerial Systems (UAS) on bat activity and explores acoustic bat detection via UAS as a new data collection method in the vicinity of wind turbines. We tested two types of UAS, a multicopter and a Lighter Than Air (LTA) UAS, to understand how they may affect acoustically recorded and analyzed bat activity level for three echolocation groups: Pipistrelloid, Myotini, and Nyctaloid.
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