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The existence of viable human pathogens in bioaerosols which can cause infection or affect human health has been the subject of little research. In this study, data provided by 10 tropospheric aircraft surveys over Japan in 2014 confirm the existence of a vast diversity of microbial species up to 3,000 m height, which can be dispersed above the planetary boundary layer over distances of up to 2,000 km, thanks to strong winds from an area covered with massive cereal croplands in Northeast (NE) Asia. Microbes attached to aerosols reveal the presence of diverse bacterial and fungal taxa, including potential human pathogens, originating from sewage, pesticides, or fertilizers. Over 266 different fungal and 305 bacterial genera appeared in the 10 aircraft transects. Actinobacteria, Bacillota, Proteobacteria, and Bacteroidetes phyla dominated the bacteria composition and, for fungi, Ascomycota prevailed over Basidiomycota. Among the pathogenic species identified, human pathogens include bacteria such as Escherichia coli, Serratia marcescens, Prevotella melaninogenica, Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus saprophyticus, Cutibacterium acnes, Clostridium difficile, Clostridium botulinum, Stenotrophomonas maltophilia, Shigella sonnei, Haemophillus parainfluenzae and Acinetobacter baumannii and health-relevant fungi such as Malassezia restricta, Malassezia globosa, Candida parapsilosis and Candida zeylanoides, Sarocladium kiliense, Cladosporium halotolerans, and Cladosporium herbarum. Diversity estimates were similar at heights and surface when entrainment of air from high altitudes occurred. Natural antimicrobial-resistant bacteria (ARB) cultured from air samples were found indicating long-distance spread of ARB and microbial viability. This would represent a novel way to disperse both viable human pathogens and resistance genes among distant geographical regions.

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Recurrent epidemics of respiratory infections have drawn attention from the academic community and the general public in recent years. Aircraft plays a pivotal role in facilitating the cross-regional transmission of pathogens. In this study, we initially utilized an Airbus A320 model for computational fluid dynamics (CFD) simulations, subsequently validating the model's efficacy in characterizing cabin airflow patterns through comparison with empirical data. Building upon this validated framework, we investigate the transport dynamics of droplets of varying sizes under three air supply velocities. The Euler-Lagrangian method is employed to meticulously track key parameters associated with droplet transport, enabling a comprehensive analysis of particle behavior within the cabin environment. This study integrates acquired data into a novel PCR (Personal Contamination Rate) equation to assess individual contamination rates. Numerical simulations demonstrate that increasing air supply velocity leads to enhanced stability in the movement of larger particles compared to smaller ones. Results show that the number of potential infections in the cabin decreases by 51.8% at the highest air supply velocity compared to the base air supply velocity, and the total exposure risk rate reduced by 26.4%. Thus, optimizing air supply velocity within a specific range effectively reduces the potential infection area. In contrast to previous research, this study provides a more comprehensive analysis of droplet movement dynamics across various particle sizes. We introduce an improved method for calculating the breathing zone, thereby enhancing droplet counting accuracy. These findings have significant implications for improving non-pharmacological public health interventions and optimizing cabin ventilation system design.

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Monkeypox virus (MPXV) can spread among humans through direct contact with lesions, scabs, or saliva; via respiratory secretions; and indirectly from fomites; via percutaneous injuries; and by crossing the placenta to the fetus during pregnancy. Since 2022, most patients with mpox in the United States have experienced painful skin lesions, and some have had severe illness. During 2021-2022, CDC initiated aircraft contact investigations after receiving reports of travelers on commercial flights with probable or confirmed mpox during their infectious period. Data were collected 1) during 2021, when two isolated clade II mpox cases not linked to an outbreak were imported into the United States by international travelers and 2) for flights arriving in or traveling within the United States during April 30-August 2, 2022, after a global clade II mpox outbreak was detected in May 2022. A total of 113 persons (100 passengers and 13 crew members) traveled on 221 flights while they were infectious with mpox. CDC developed definitions for aircraft contacts based on proximity to mpox cases and flight duration, sent information about these contacts to U.S. health departments, and received outcome information for 1,046 (68%) of 1,538 contacts. No traveler was found to have acquired mpox via a U.S. flight exposure. For persons with mpox and their contacts who had departed from the United States, CDC forwarded contact information as well as details about the exposure event to destination countries to facilitate their own public health investigations. Findings from these aircraft contact investigations suggest that traveling on a flight with a person with mpox does not appear to constitute an exposure risk or warrant routine contact tracing activities. Nonetheless, CDC recommends that persons with mpox isolate and delay travel until they are no longer infectious.

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Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.

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Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.

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Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.

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Understanding the operator's cognitive workload is crucial for efficiency and safety in human-machine systems. This study investigated how cognitive workload modulates cardiac autonomic regulation during a standardized military simulator flight. Military student pilots completed simulated flight tasks in a Hawk flight simulator. Continuous electrocardiography was recorded to analyze time and frequency domain heart rate variability (HRV). After the simulation, a flight instructor used a standardized method to evaluate student pilot's individual cognitive workload from video-recorded flight simulator data. Results indicated that HRV was able to differentiate flight phases that induced varying levels of cognitive workload; an increasing level of cognitive workload caused significant decreases in many HRV variables, mainly reflecting parasympathetic deactivation of cardiac autonomic regulation. In conclusion, autonomic physiological responses can be used to examine reactions to increased cognitive workload during simulated military flights. HRV could be beneficial in assessing individual responses to cognitive workload and pilot performance during simulator training.

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INTRODUCTION: Unmanned aerial vehicles (UAVs), more commonly known as drones, have rapidly become more diverse in capabilities and utilization through technology advancements and affordability. While drones have had significant positive impact on healthcare and consumer delivery particularly in remote and austere environments, Violent Non-State Actors (VNSAs) have increasingly used drones as weapons in planning and executing terrorist attacks resulting in significant morbidity and mortality. We aim to analyze drone-related attacks globally against civilians and critical infrastructure for more effective hospital and prehospital care preparedness. METHODS: We retrospectively reviewed the Global Terrorism Database (GTD) from 1970 to 2020 to analyze the worldwide prevalence of drone-related attacks against civilians and critical infrastructure. Cases were excluded if they had insufficient information regarding a drone involvement, and if attacks were conducted by the government entities. The trends in the number of attacks per month, as well as the number of fatalities and injuries, were examined using time series and trend analysis. RESULTS: The database search yielded 253 drone-related incidents, 173 of which met inclusion criteria. These incidents resulted in 92 fatalities and 215 injuries with civilian targets most commonly attacked by drones (76 events, 43.9%), followed by military (46 events, 26.5-%). The Middle East region was most affected (168 events, 97% of attacks) and the Islamic state of Iraq was the most common perpetrator (106 events, 61.2%). Almost all attacks were by explosive devices attached to drones (172 events, 99.4%). Time series with linear trend analyses suggested an upward trends of drone attacks by VNSAs, resulting in a greater number of injuries and fatalities, that became more frequent over the years. CONCLUSIONS: Overtime, there were upward trends of drone attacks, with higher lethality and morbidity. There were more injuries compared to fatalities. Most common region affected was the Middle East, and most common type of weapon employed by drone technology was explosive weapon. Investment in medical personnel training, security, and research is crucial for an effective mass-casualty incident response after the drone attacks.

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BACKGROUND: Previous studies reported an association between transportation noise and self-reported health status (SRHS). They also suggested a mediating role of noise annoyance using conventional statistical methods. These methods are subject to bias in longitudinal studies with time-dependent exposure, mediator and confounding factors. This study aims to investigate the mediating role of aircraft noise annoyance in the effect of aircraft noise on SRHS using a causal inference approach to address time-dependent variables issues. METHODS: We used data from 881 participants in all three visits in the DEBATS longitudinal study conducted around three French airports. Participants over 18 years of age reported their self-perceived health status, aircraft noise annoyance, and noise sensitivity by completing a questionnaire at three visits in 2013, 2015 and 2017. Noise maps were used to estimate aircraft noise levels outside their homes. Marginal structural models with inverse probability weighting were used to estimate the total effect of aircraft noise levels on SRHS and its decomposition into direct and indirect effect through aircraft noise annoyance. RESULTS: This study showed a deleterious effect of aircraft noise on SRHS. The odds ratio (OR) corresponding to the total effect and comparing the highest aircraft noise category (≥60 dBA) to the reference category (<50 dBA) was significant (ORpoor/fair_SHRS = 1.25 (95%CI: 1.06 to 2.08)). It also showed no direct effect of aircraft noise levels on SRHS, but an indirect effect through annoyance. This indirect effect increased as aircraft noise levels increased, with a statistically significant OR when comparing the highest noise category (≥60 dBA) to the lowest (<50 dBA) (ORpoor/fair_SHRS = 1.16 (95%CI: 1.03 to 1.52)). Nearly 66% of aircraft noise's effect on SRHS was mediated by aircraft noise annoyance. CONCLUSION: This study supports the deleterious causal effect of aircraft noise on SRHS. The results highlight the important mediating role of aircraft noise annoyance in the causal pathway from exposure to aircraft noise to poor/fair SRHS.

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INTRODUCTION: Pilots are frequently exposed to thrombotic risk as a result of immobility from air travel. As hypoxemia is associated with secondary polycythemia, and polycythemia increases the risk of thrombosis, intermittent exposure to high-altitude hypoxic environments could escalate the risk of thrombosis in pilots. Our objectives were to find the prevalence of polycythemia in airplane pilots (primary outcome) and to assess associated risk factors of polycythemia (secondary outcome).METHODS: This study is a cross-sectional descriptive study. Data was collected from paper-based and computerized medical records of airplane pilots who applied for Class 1 Aviation Medical Certificate renewal at the Institute of Aviation Medicine, Royal Thai Air Force, Bangkok, Thailand, in 2018. The data was sampled by a simple random sampling technique.RESULTS: A total of 386 paper-based records were sampled. Of those, 29 (7.5%) of the pilots met polycythemia criteria. Spearman's correlation analysis showed a significant positive correlation between Body Mass Index (BMI) and hemoglobin (correlation coefficient = 0.127) and between BMI and hematocrit (correlation coefficient = 0.105). In multivariate logistic regression of each variable on polycythemia as defined by hemoglobin alone, piloting a non-pressurized aircraft was found to be an independent predictor of polycythemia (odds ratio = 4.3).DISCUSSION: The prevalence of polycythemia in airplane pilots was 7.5%. Operating a non-pressurized aircraft was a significant risk factor of polycythemia, and pilots with higher BMI were more likely to have increased red blood cell parameters.Thanapaisan P, Plaingam M, Manyanont S. Polycythemia prevalence and risk factors in pilots. Aerosp Med Hum Perform. 2024; 95(9):683-687.

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Use of onboard commercial airline defibrillators began in 1997. At first, it was met with resistance but is now present on all planes. The first in-flight resuscitation of a passenger occurred in 1998 and is described here.

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INTRODUCTION: The European Air Transport Command (EATC) is a seven-nation integrated command. One of its core capabilities is strategic aeromedical evacuation (AE). During the global COVID-19 pandemic and Ukrainian crisis, EATC proved that acting in concert is a valuable, effective, and reliable option.METHODS: By pooling and sharing aircraft and personnel, EATC has privileged access to a diverse fleet and pool of experts. Cooperation is based on a common set of rules and regulations, which ensures that EATC can address any problem with expertise.RESULTS: During the COVID-19 pandemic, 1060 COVID-19-positive patients were transported in 198 missions, with neither death nor disease transmission reported during those strategic AE flights. EATC transferred 986 military cases, mostly routine priority (91.4%); the other 74 cases were civilians, who were transported in 17 missions, with 81.1% categorized as urgent. During the Ukrainian crisis, 251 patients were transported, 112 military and 139 civilians, including 30 children. Among the recorded injuries were cerebrocranial, abdominal, and chest injuries, as well as fractures (180) and amputations (48) of the extremities.DISCUSSION: EATC is recognized as a center of expertise within the AE community, where interoperability and harmonization of concepts are key to safety and success. Cross-national missions, where a patient is evacuated by an aircraft and medical crew provided by another nation, offer maximum flexibility. Complex situations, such as the COVID-19 pandemic and the Ukrainian crisis, have shown that multinational cooperation is not only achievable but also provides robust, effective, and reliable solutions for AE in particular.Fiorini A, Vermeltfoort R, Dulaurent E, Hove MG, Borsch M. Cross-national strategic aeromedical evacuation at the European Air Transport Command. Aerosp Med Hum Perform. 2024; 95(9):709-715.

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This study introduces a laser scattering system to protect a low-speed aircraft. Scattering was selected to reduce the laser's intensity targeting the sensor of an aircraft and simultaneously maintaining the functionality of aircraft optics. Mie scattering, known for effectively decreasing short-wave infrared light, was employed by utilizing water aerosols having a diameter of 1 to 5 µm. Experimental results regarding the decrease of the laser intensity via scattering confirmed that the theoretical and experimental values resulted in a similar decrease rate under static conditions. To validate the theoretical values, the path length, which the laser passing through water aerosols, was changed. To assess the system's feasibility in flow conditions, a low-speed wind tunnel was employed to generate two flow speeds: 5.5 m/s and 17.6 m/s. Remarkably, the reduction of laser intensity was only affected by the path length, and was somewhat unaffected regardless of flow speed and the uniformity of the flow, only to the path length. In all cases, the initial laser intensity was set to 10 mW. Under static conditions, the intensity dropped to 8.21 mW, showing a decrease of 17.9%. In flow conditions of 5.5 m/s, 17.6 m/s, and in distorted flow, the laser intensity decreased by 18.3%, 18.1%, and 18% respectively. As a preliminary study, these results demonstrate the system's capability to protect a low-speed aircraft targeted by lasers even under dynamic flow conditions, may suggest a possibility of providing a practical defence solution.

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Rapid global population growth and urbanization have heightened the demand for emergency medical rescue, with helicopter medical rescue emerging as an effective solution. The advent of 5G communication technology, characterized by large bandwidth, low latency, and high reliability, offers substantial promise in enhancing the efficiency and quality of helicopter rescue operations. However, the full integration of 5G technology into helicopter emergency medical services is still in its nascent stages and requires further development. In this viewpoint, we present our experience from the Shenzhen University General Hospital of the application of 5G low-altitude network communication technology, body area network disease sensing technology, and 5G air-ground collaborative rapid diagnosis and treatment technology in aeromedical rescue. We consider that the 5G air-to-ground collaborative rapid diagnosis and treatment technology enables high-quality remote consultation, enhancing emergency medical rescue and providing strong support for future rescue operations.

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This study aimed to determine the possibility of COVID-19 transmission through in-flight contact during flights for many patients with confirmed COVID-19 during the COVID-19 pandemic and explore infection prevention and control (IPC) methods for overseas infectious diseases. A retrospective cohort study was conducted on flight with a large number of confirmed case among. Delhi- Incheon flights in 2020. This flight was selected to confirm transmission through close contact with the cabin, with a total of 14 confirmed cases out of 190 passengers (including 10 flight attendants). After confirming COVID-19 test results for those entering Korea, we conducted an epidemiological investigation on confirmed patients to determine their general characteristics and epidemiological relevance. We analyzed the epidemiological relevance, occupational information, incubation period, and COVID-19 variation and genotype among confirmed patients who were in close contact with confirmed cases, and analyzed the possibility of transmission according to the distance of close contact in the flight. One confirmed patient was found to be highly likely to be infected due to close contact with the cabin. However, it occurred within two rows, not within 1 meter. In addition, considering the aerodynamics in the cabin and local incidence rate, infection in an unspecified number of local people could not be excluded. It was analyzed that the reason for reducing infection from close contact on board for a long time in a flight with a large number of confirmed cases was the effective IPC method. In order to prevent overseas infectious diseases caused by flights, autonomous IPC management of airlines and passengers is necessary in addition to national quarantine management such as symptom screening before boarding, wearing passenger masks while boarding, food and beverage restrictions, disinfection of public spaces, distancing between passengers, close contact management after boarding, and self-quarantine.

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Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW & NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.

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Recently, Sustainable Aviation Fuel (SAF) blends and novel combustion technologies have been introduced to reduce aircraft engine emissions. However, there is limited knowledge about the impact of combustion technology and fuel composition on toxicity of primary Particulate Matter (PM) emissions, comparable to regulated non-volatile PM (nvPM). In this study, primary PM was collected on filters using a standardised approach, from both a Rich-Quench-Lean (RQL) combustion rig and a bespoke liquid fuelled Combustion Aerosol Standard (CAST) Generator burning 12 aviation fuels including conventional Jet-A, SAFs, and blends thereof. The fuels varied in aromatics (0-25.2%), sulphur (0-3000 ppm) and hydrogen (13.43-15.31%) contents. Toxicity of the collected primary PM was studied in vitro utilising Air-Liquid Interface (ALI) exposure of lung epithelial cells (Calu-3) in monoculture and co-culture with macrophages (differentiated THP-1 cells). Cells were exposed to PM extracted from filters and nebulised from suspensions using a cloud-based ALI exposure system. Toxicity readout parameters were analysed 24 h after exposure. Results showed presence of genotoxicity and changes in gene expression at dose levels which did not induce cytotoxicity. DNA damage was detected through Comet assay in cells exposed to CAST generated samples. Real-Time PCR performed to investigate the expression profile of genes involved in oxidative stress and DNA repair pathways showed different behaviours after exposure to the various PM samples. No differences were found in pro-inflammatory interleukin-8 secretion. This study indicates that primary PM toxicity is driven by wider factors than fuel composition, highlighting that further work is needed to substantiate the full toxicity of aircraft exhaust PM inclusive of secondary PM emanating from numerous engine technologies across the power range burning conventional Jet-A and SAF.

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This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations' databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

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Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.

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