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Musculoskeletal injuries (MSKIs) are ubiquitous in the U.S. military, especially among high-performing service members such as Marines. Given that female service members only started to be assigned to ground combat roles since December 2015, evaluation of sex on MSKI risk in ground combat occupations has not been possible until there was an ample population to study. The purpose of this population-level epidemiological study was to assess (1) if female sex was a salient risk factor for MSKI in Marines serving in different military occupations, including combat arms, and (2) the effects of integration period on MSKI risk among female Marines. A population-based epidemiological retrospective cohort study of all U.S. Marines was performed assessing female sex, occupation, and integration period on the prevalence of MSKI from 2011 through 2020. The Military Health System Data Repository was utilized to identify initial healthcare encounters for diagnosed ankle-foot, knee, lumbopelvic-hip, thoracocostal, cervicothoracic, shoulder, elbow, or wrist-hand complex injuries. Prevalence was calculated for female and male Marines in each occupational category (combat, combat support, aviators, aviation support, services) during the pre-integration (2011-2015) and post-integration (2016-2020) periods. During the pre-integration period, 520/1,000 female Marines (n = 13,985) and 299/1,000 male Marines (n = 142,158) incurred MSKIs. In the post-integration period, the prevalence increased to 565/1,000 female Marines (n = 17,608) and 348/1,000 male Marines (n = 161,429). In the multivariable evaluation of sex, occupation, integration period, and the interaction of sex and occupation on combined MSKIs, only female sex was a significant factor for injury (prevalence ratio [PR]=1.99), with service in ground combat and aviation occupations identified as protective factors when compared with services occupations (PR = 0.69). When these same factors were evaluated for specific MSKI outcomes, female sex remained a robust factor in all lower quarter (PR = 1.75-2.63) and upper quarter (PR = 1.38-2.36) injuries except for shoulder injuries. Service in ground combat and aviation occupations was protective for all lower quarter injuries (PR = 0.46-0.71). In the upper quarter, ground combat was protective for all injuries except for elbow injuries (PR = 0.67-0.77). Serving as an aviator was a risk factor for cervicothoracic (PR = 1.57) and thoracocostal (PR = 1.22) injuries and a protective factor for shoulder (PR = 0.73) and wrist-hand (PR = 0.46) injuries. Adjusted risk for lumbopelvic-hip (PR = 1.13), ankle-foot (PR = 1.53), cervicothoracic (PR = 1.19), thoracocostal (PR = 1.14), and elbow (PR = 1.48) injuries significantly increased during the post-integration period. There was a significant sex-by-period interaction for shoulder injuries alone, with female sex in the post-integration epoch found to be salient (PR = 1.26). Female sex was a significant factor for MSKI, with service in ground combat and aviation occupations identified as protective factors when compared with services occupations. In the evaluation of specific MSKIs, female sex remained a robust and significant factor in all lower quarter injuries and upper quarter injuries except for shoulder injuries. There was only a significant sex-by-period interaction for shoulder conditions, with an increased risk of these injuries in female Marines in the post-integration period.

To examine flight-related and aviation environmental changes in self-perceived vocal status among airline crew and to determine whether these differ between short-, medium-, and long-haul routes. Thirty-three airline crew members (n&#xa0;=&#xa0;11 short-, n&#xa0;=&#xa0;11 medium-, and n&#xa0;=&#xa0;11 long-haul) participated in this observational study. Self-evaluation was conducted before and after flight routine using the validated versions of the voice handicap index (VHI), vocal fatigue index (VFI) of cluster 1 (tiredness and avoidance plus physical discomfort), and vocal tract discomfort (VTD) scale. The study followed defined inclusion and exclusion criteria. Data were analyzed using the Shapiro-Wilk test to verify normal distribution, and depending on data characteristics, paired-sample t tests and Wilcoxon signed-rank tests compared pre and postflight results. Repeated-measures analysis of variance and Kruskal-Wallis tests examined differences among flight-route groups. Finally, effect size measures of Cohen's d and Cohen's f were used. Overall, airline crew members reported a significant increase with small to medium effect sizes in voice-related complaints after flight operations across VHI, VFI, and VTD (P values&#xa0;<&#xa0;0.01; d values&#xa0;&#x2265;&#xa0;0.41). The general perception of voice-related complaints, as measured by the VHI, was observed among the airline crew on medium- and long-haul flights (P values&#xa0;<&#xa0;0.05), with large effect sizes (f&#xa0;>&#xa0;0.82). Although perceived vocal problems did not consistently manifest as vocal fatigue (VFI-cluster 1) or laryngeal discomfort (VTD) across all flight distances, postflight measurements still showed a significant overall rise in these parameters (P values&#xa0;=&#xa0;0.002). Specifically, medium-haul flights were associated with marked vocal fatigue of cluster 1 (P&#xa0;=&#xa0;0.008), while long-haul flights showed significantly increased laryngeal discomfort (P&#xa0;=&#xa0;0.014). Airline crew members on long-haul routes were the most affected group, already exhibiting beginning pathological scores across all three preflight questionnaires compared with their short- and medium-haul counterparts. Flight operations are associated with increased self-reported vocal complaints, particularly in long-haul crew. These outcomes point to the impact of occupational vocal loading in aviation and highlight the importance of further investigation to obtain vocal health in aviation.

The transition to sustainable aviation fuel (SAF) requires efficient catalytic technologies to convert oxygenated lipid-derived feedstocks into hydrocarbon fuels that comply with stringent aviation fuel specifications. Motivated by the relatively low cost of nickel (Ni)-based catalysts and their suitability for large-scale commercial applications, a series of Ni-based catalysts supported on a modified beta zeolite (m-beta) were synthesized to investigate the effects of gallium (Ga) incorporation and the addition of a cerium (Ce) promoter on hydroconversion of palm oil-derived biodiesel. Based on catalyst characterization, the incorporation of Ga improved Ni dispersion and reduced Ni particle size, while Ce further modified Ni electronic states, enhanced H2 consumption, and introduced additional Br&#xf8;nsted acid sites through OH-bridging Ce species. Among the studied catalysts under a central condition, the bimetallic 5Ni5Ga/m-beta catalyst exhibited the highest liquid biofuel yield at 75.9 wt %, with 35.1 wt % biojet fuel-range hydrocarbons, and a high iso-/n-alkane ratio (1.14), attributable to its enhanced Ni active sites and alkane dehydrogenation and aromatization. For the effect of reaction parameters, both the reaction temperature and weight hourly space velocity (WHSV) played critical roles in overcoming limitations in oxygen-removal reactions. Although higher reaction temperatures and lower WHSV enhanced the biojet fuel fraction in the resulting liquid biofuels, these conditions should be carefully optimized to minimize undesirable aromatization. The addition of an appropriate Ce loading to the 5Ni5Ga/m-beta catalyst (5Ni5Ga-5Ce/m-beta) could further improve oxygen removal and alter the reaction pathway, thereby increasing iso-alkane selectivity over aromatic and cyclic compounds. A blend of oil between commercial Jet A-1 and the resulting liquid biofuel from 5Ni5Ga-5Ce/m-beta at 90/10 (v/v) had a heating value comparable to that of Jet A-1 (43.0 MJ/kg) and a freezing point of -54.9 &#xb0;C. These results highlight the potential of NiGaCe/m-beta catalysts as cost-effective systems for producing SAF-compatible hydrocarbons from biodiesel, expanding strategies for sustainable aviation applications.

Midair collisions (MACs), while rare for air carriers, are not infrequent for general aviation, partly reflecting the limitations of the see-and-avoid method. However, considering technological advances potentially offsetting ocular limitations and little research on the subject, we sought to determine 1) whether the MAC rate has declined over time and 2) the underlying causes. MACs' (1995-2023) injury severity, mission type, and ambient conditions were per the National Transportation Safety Board database. Statistics used Poisson distributions/Chi-square tests. There were 480 aircraft (90% and 8% fixed-/rotary-wing aircraft, respectively) involved in 257 midair events. Despite an overall decline (63% reduction) in MAC rates (2020-2023), the proportion of fatal events (44-58%) was unchanged. Aircraft engaged in personal and training missions represented 61% and 24% of MACs, respectively, with the training mission MAC rate declining 70%. Although traffic density is highest surrounding aerodromes, surprisingly, only half of the MACs were within this environment. MAC rates, adjusted for arrival/departure counts, at aerodromes with a control tower were 6.5-fold lower compared with airports lacking such a facility. However, some MACs were still evident for aircraft receiving traffic deconfliction services, and for such mishaps, 78% were due to pilots not maintaining visual separation. The following recommendations are advanced. General aviation authorities and organizations should update pilot training curricula and safety programs to include training on physiological limitations, e.g. field of vision deterioration with advancing age. Further, the findings herein warrant future research to determine whether over-reliance on electronic traffic displays and panel modernization negatively impact external visual scanning. Boyd D, Anderson C. Midair collisions over a period of technological advances targeting human performance deficits. Aerosp Med Hum Perform. 2026; 97(6):443-450.

Autoimmune limbic encephalitis is a rare condition. Autoimmune limbic encephalitis after COVID-19 infection is rarer. To date, a handful of cases in nonaviation settings have been reported and their serology were negative for antiprotein leucine-rich glioma inactivated 1 antibody (LGI-1), save two case reports. To the best of our knowledge, this is the first case in aviation environment where positive anti-LGI-1 antibody was detected in serum post-COVID-19 infection. 53-yr-old male commercial pilot presented with confabulations and faciobrachial dystonic seizures 6 mo after contracting COVID-19. He had lately failed his scheduled flight simulator check, despite prior uncheckered flying history and proficiency checks. His serum showed mild hyponatremia and was positive for anti-LGI-1 antibody. T2 MRI demonstrated intensity in his temporal lobes. The pilot was treated with high-dose intravenous and oral steroids, intravenous immunoglobulin, plasma exchange, and rituximab. He did not improve and remained grounded from flying duties. LGI-1 autoimmune limbic encephalitis is characterized by clinical manifestation of faciobrachial dystonic seizures and cognitive impairment, commonly affecting short-term memory or amnesia. Laboratory findings such as hyponatraemia and T2 MRI hyperintensity in the temporal lobes should assist in forming a provisional diagnosis which is confirmed by positive anti-LGI-1 serology. There is no cure. Prognosis is guarded with immunotherapy treatment and relapse rates are considerable. It is associated with seizures and tumor. All these factors render the condition not meeting the 1% aeromedical certification rule, and it is incompatible with safe operation of aircraft. Wong MGP. Post-COVID-19 leucine-rich glioma inactivated 1 autoimmune limbic encephalitis in an aviator. Aerosp Med Hum Perform. 2026; 97(6):464-467.

The demands of competing in the Olympic games is remarkably similar to that of surgeon in theatre. Both require sustained precision, high cognitive load, emotional regulation, physical endurance, and the weight of outcomes that matter profoundly.Other high-performance domains, such as elite sport, aviation, and the military, have long recognised that peak execution comes from deliberate investment in human performance science: training body and mind, integrating recovery, applying technology, and employing coaching and human performance strategy to optimise outcomes. Athletes systematically invest in programmed and periodised conditioning, physiological monitoring, recovery, nutrition, and psychological resilience. Their success depends on the integration of physiology, psychology, and environment.

Air-source heat pumps (ASHPs) provide high-efficiency electric space heating and play an important role in residential decarbonization. In cold climates, low outdoor temperatures reduce ASHP efficiency, while frost formation necessitates periodic defrosting, which lowers indoor temperatures and is typically offset by energy-intensive auxiliary electric resistance heating. Antifrost coatings offer a pathway to suppress frost formation and mitigate these comfort and energy penalties, but their system-level implications for residential ASHP applications have not been quantified. Here, we simulate ASHP operation in &#x223c;7900 single-family homes across 25 U.S. cold-climate cities, comparing current practice, i.e., defrosting with auxiliary electric resistance heating, to defrosting operation without auxiliary heating, and to antifrost coatings assumed to suppress frost formation and thereby avoid defrost operations. Our calculations show that relative to current practice, antifrost coatings can reduce median winter emissions by &#x223c;40% (assuming defrost is triggered after each hour of frosting conditions and lasts 15 min). Disabling electric resistance heating yields the largest emissions reductions but causes 1-5 &#xb0;C indoor cooling during defrost, while a +2 &#xb0;C increase in temperature set point halves under-heating. At scale, widespread adoption of antifrost coatings could yield winter emissions reductions of up to 30% of U.S. commercial aviation emissions in 2022, while eliminating defrost-related thermal discomfort.

To compare left vs. right steep turns in terms of workload-related neurophysiological signatures using electroencephalogram (EEG) and machine learning. Thirty-seven flight cadets performed one left and one right steep turn in an SR20 desktop flight simulator while a 32-channel EEG (Emotiv EPOC Flex 32) was recorded. From 2-s sliding windows (50% overlap), 800 features per window were extracted (time-, frequency-, and non-linear domains). Six classifiers (XGBoost, LightGBM, GB, SVM, LR, and Linear SVC) were evaluated using cross-subject nested cross-validation with variance-ranked feature subsets (20%, 40%, 60%, 80%, and 100%), and an additional 10% subset was assessed to identify a more parsimonious feature set. LightGBM demonstrated superior performance across all feature proportions. NASA-TLX was significantly higher in right turns than in left turns (5.55 &#xb1; 1.13 vs. 4.98 &#xb1; 1.06, p < 0.001, and Cohen's d = 0.52). Post-hoc interpretation combined RF-based importance and variance-ranked top-feature analysis, showing convergent frontal/frontocentral dominance with complementary utility definitions (predictive contribution vs. signal dispersion). Physiologically, left turns were associated with relatively higher high-frequency activity/complexity, whereas right turns showed relatively stronger theta/alpha-related patterns. These findings support MWL-associated directional neurophysiological differences in steep turns and identify candidate EEG markers for lightweight real-time workload monitoring, facilitating optimized flight training and enhanced aviation safety.

ObjectiveTo identify eye movement patterns that are correlated with spatial disorientation (SD) events during flights in a flight simulator that induces SD.BackgroundSpatial Disorientation is one of the main causes for aviation mishaps. It can result from illusions caused by misinterpreted vestibular or visual sensory cues, leading to an incorrect perception of an aircraft's position, attitude, or motion. SD prevention is of great importance, as there is currently no objective tool to identify its occurrence.MethodEye movements of 45 participants (30 aircrew members, 15 cadets) were recorded using Tobii Pro Glasses 2 in a Gyro-IPT SD flight simulator. Illusions were either vestibular or visual. Gaze metrics such as fixations, saccades (rapid gaze shift between two points), and visits were compared between subjects who experienced SD and those who did not. Statistical analyses were conducted to identify significant differences.ResultsAmong 284 flight profiles, 136 SD occurrences were recorded (48%). During visual illusions the participants who more frequently checked the instrument panel had a higher chance of avoiding SD. In contrast, during vestibular illusions, participants who examined the head-up display (HUD) more frequently had a lower probability of SD occurrence.ConclusionMitigating SD requires distinct eye-movement strategies tailored to the illusion type. Our results suggest that to mitigate visual illusions, there is a need for greater instrument panel focus, whereas to mitigate vestibular illusions, increased HUD engagement is needed, as opposed to the current instructions.ApplicationOur findings may inform training programs to enhance performance in high-risk SD flight profiles. Additionally, results support the potential development of a real-time SD alert system for aircraft, aiming to mitigate or prevent SD-related incidents.

Unmanned Aerial Vehicles (UAVs) operate in navigation, sensing, and communication environments that are frequently degraded or adversarial. Their attack surface spans flight-control and payload software, radio links, and swarm coordination. This PRISMA-aligned systematic review synthesizes peer-reviewed studies published between 2015 and 2025 and organizes the evidence using an OSI-inspired threat taxonomy that maps spoofing, jamming, intrusion, and malware to system touchpoints and observable anomalies. We compare deep learning architectures, training targets, feature representations, evaluation practice, and deployment constraints relevant to single UAVs and swarms. Across the literature, convolutional and recurrent models dominate intrusion and anomaly detection pipelines, while attention-based, graph, and generative models appear in newer work targeting multi-agent settings and limited labels. Evidence most often relies on protocol traffic and onboard telemetry, whereas RF inputs are used less frequently and are typically represented as raw samples or spectrograms when datasets allow. Studies increasingly report efficiency-oriented deployment using pruning, quantization, distillation, or split inference to meet onboard compute and energy limits. Federated and multi-agent approaches are evaluated for scalability and robustness under poisoned updates, and blockchain-integrated designs are discussed under bandwidth and power constraints. Key gaps persist in shared datasets, repeatable adversarial stress testing, uncertainty and explainability reporting, privacy preservation, and certification-ready assurance cases for aviation regulation.

Deciding between multiple options in a split second is a crucial aspect in various domains, including traffic, aviation, policing, and sports. Both drift-diffusion modeling (DDM), a computational model that approaches decision-making as noisy evidence accumulation, and finger tracking have been suggested to capture the evolution of a decision over time. In this study, we comparatively applied DDM and finger tracking to examine the processes underlying split-second decision-making within an anticipatory handball penalty task. Participants were shown temporally occluded videos of handball penalties and predicted shot direction by either pointing or continuously swiping toward one of two target areas. We extended previous research by using an optical motion capture system to track trajectories of both pointing and swiping and also calculated drift-diffusion models grouped by response modality. Results indicate that the DDM robustly mirrors the decision-making process. The model reflects the movement differences between pointing and swiping accurately in the non-decision time and shows consistent correlations between response modalities. In contrast, the finger tracking parameters (i.e., area under the curve, velocity, x-flips, and entropy) did not show consistent correlations between pointing and swiping trials and were strongly dependent on response modality. Furthermore, the effect of the response modality manipulation could not be clearly identified by finger tracking parameters. We conclude that DDM when compared to finger tracking seems to provide more modality-invariant insights into the processes underlying decision-making across different response tasks (i.e., modalities).

RSR pattern in precordial electrocardiogram (ECG) leads may represent various underlying cardiac conditions. While generally benign, there is insufficient evidence precluding the need for further echocardiographic assessment. This retrospective cohort study aims to investigate the utility of transthoracic echocardiography (TTE) in diagnosing disqualifying cardiac conditions among Republic of Singapore Air Force (RSAF) applicants with isolated RSR on pre-employment medical screening (PEMS). Applicants who underwent PEMS from December 31, 2014-December 31, 2024, had isolated ECG RSR with subsequent TTE, complete medical records, and clearly documented selection outcomes were identified from RSAF PEMS data. Data pertaining to baseline demographics, cardiovascular risk factors, cardiac investigation findings, and PEMS outcomes were extracted. Of 102 eligible applicants, 95 accepted for all vocations and 7 disqualified from at least one vocation formed the accepted and disqualified cohorts, respectively. Overall, 18.6% (N = 19) of the cohort had abnormal TTE findings. Compared to the accepted cohort, the proportion of abnormal TTE (N = 6, 85.7% vs N = 13, 13.7%) was significantly higher in those disqualified. Regression analysis demonstrated significant correlation between abnormal TTE findings and QRS duration (OR = 1.12; CI = 1.03,1.20). This study highlights the potential value of integrating ECG-based risk stratification with echocardiographic evaluation to balance diagnostic accuracy, resource efficiency, and mitigation of aeromedical risks. Nonetheless, TTE continues to be of value in identifying disqualifying cardiac conditions among individuals with isolated RSR during ab-initio military aviation selection in the RSAF. Yang WYL, Cheok LJ, Ching K, Koh CH, See B, Low JW. Transthoracic echocardiography for evaluation of isolated RSR pattern in military aircrew applicants. Aerosp Med Hum Perform. 2026; 97(6):403-410.

In this study, a location and altitude-based warning and collision awareness and advisory prototype was designed and two functional prototypes were developed to reduce the risk of collisions that may occur when hot air balloons cannot see each other in the air. Each prototype used a Raspberry Pi-4, 7-inch touch screen, LoRa communication module, BMP180 pressure sensor, MPU6050 accelerometer, and a GY-GPSV3 NEO-8&#xa0;M Global Navigation Satellite Systems (GNSS) module. The balloons' location, altitude, and speed information were mutually broadcast via the LoRa module; the data of the intruder balloon located within a horizontal 500&#xa0;m radius was visualized with an interface similar to the Traffic Alert and Collision Avoidance System (TCAS) in aircraft. The system offers a resolution advisory (RA) at distances of 100&#xa0;m and closer. It produces a traffic advisory (TA) at distances between 100 and 200&#xa0;m. In the tests carried out on the ground and in the hot air balloon, the system's location detection accuracy, the functionality of the warning algorithms, the detection of flight phases, and the information levels in the designed interface were analyzed. The findings have shown that this system, which leverages low-cost GNSS technology for cooperative awareness, is open to development and represents a novel application of positioning technology to fill a gap in preventing accidents that may occur after balloons come into contact with each other, making a contribution to the hot air balloon industry.

Chaotic dynamics has been the subject of both theoretical and empirical research in epidemiology, with the most recent research strongly focusing on SARS-CoV-2. However, few empirical studies have been undertaken with respect to influenza, even though evidence of chaos has also been found in influenza surveillance data. Furthermore, empirical studies on chaos are focused on reconstructing hidden attractors in epidemiological time series to filter out noise; however, dynamical noise affecting chaotic dynamics can have relevant epidemiological features that are, in this way, left unresearched and that can be used for epidemiological surveillance and risk analysis by capturing the main underlying nonlinear processes associated with epidemiological dynamics. This study aimed to reinforce empirical research on chaotic dynamics in influenza surveillance and the study of the dynamical noise affecting that chaotic dynamics, addressing the consequences for epidemiological risk analysis and surveillance. Working with the weekly share of positive influenza tests for the Northern Hemisphere from January 2009 to March 2025 compiled by Our World in Data using FluNet data from the World Health Organization, we applied a recent method based on topological data analysis for reconstructing underlying attractors from time series and decomposing the dynamics down to independent and identically distributed noise. We adapted the method to the epidemiological context so that it can be used for predictive decomposition with direct application to epidemiological risk analysis and surveillance. We found evidence of a low-dimensional chaotic attractor in the researched surveillance data, with a fractal dimension between 1 and 2, and a positive statistically significant largest Lyapunov exponent. The chaotic dynamics had power law scaling associated with long-wave influenza outbreaks, and it is affected by a stochastic component that is nonstationary in variance, leading to turbulent bursts in the outbreak dynamics. Testing different machine learning algorithms using the attractor as input for prediction and a 10-week rolling window, we found the following largest R2 scores for the prediction of the target series: 92.11% (1 week ahead), 85.95% (2 weeks ahead), 81.75% (3 weeks ahead), 77.59% (4 weeks ahead), and 73.35% (5 weeks ahead). The main results reinforce previous theoretical and empirical studies on chaos in epidemiology. Our findings showed that there is a 2-dimensional chaotic attractor that can support up to a 1-month prediction of the target surveillance series with high prediction scores and that the attractor plus noise can be modeled in a way that supports uncertainty quantification and epidemiological risk analysis.

[This corrects the article DOI: 10.3389/fped.2026.1717012.].