Draft genome sequence of the Tremellomycetes yeast Papiliotrema laurentii 5307AH, isolated from aircraft.

Papiliotrema laurentii 5307AH was isolated from an aircraft polymer-coated surface. The genome size is 19,510,785 bp with a G + C content of 56%. The genome harbors genes encoding oxygenases, cutinases, lipases, and enzymes for styrene degradation, all of which could play a critical role in survival on xenobiotic surfaces.

LESM-YOLO: An Improved Aircraft Ducts Defect Detection Model.

Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model's parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts.

Inter hospital transfers in rotor wing aircraft. Patterns and challenges. Protocol for a scoping review.

BACKGROUND: Inter-hospital transfer is necessary for the transport of patients to specialized treatment. Rotor-wing aircraft may be used in lieu of ambulances in time-critical conditions over long distances and when specialist team skills are called for. The purpose of the review is to assess the current scientific literature that describes the scenario to develop a national guideline for inter-hospital transfers using rotor-wing aircraft. The aim is to describe the patterns and challenges. METHODS AND ANALYSIS: The authors will conduct a scoping review as per Joanna Briggs Institute guideline. The protocol for the scoping review will adhere to the Open Science Framework guideline for scoping reviews and we will report the findings of the scoping review as per PRISMA-ScR guideline. We have developed the search strategy with the help of a research librarian and will conduct search in relevant electronic databases and include gray literature as well, using the PRESS and PRISMA-S guidelines. Two authors will independently screen titles and abstracts for inclusion as per eligibility criteria and conflicts will be resolved by a third reviewer. Full text retrieval will be conducted accordingly. We will analyze the extracted data using validated statistical methods. ETHICS AND DISSEMINATION: According to Danish law, scoping reviews are exempt from ethics committee approval. The findings of this scoping review will provide the scientific foundation for a national guideline on rotor-wing aircraft conveyed inter-hospital transfers in Denmark. Furthermore, we will publish the results of the scoping review in a relevant scientific journal.

Over a decade of UAV incidents: A human factors analysis of causal factors.

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.

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line-Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

The Risk of Aircraft-Acquired SARS-CoV-2 Transmission during Commercial Flights: A Systematic Review.

The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission. From the 15 articles selected for in-depth review, 50 total flights were analyzed by flight duration both as a categorical variable-short (<3 h), medium (3-6 h), or long flights (>6 h)-and as a continuous variable with case counts modeled by negative binomial regression. Compared to short flights without masking, medium and long flights without masking were associated with 4.66-fold increase (95% CI: [1.01, 21.52]; p < 0.0001) and 25.93-fold increase in incidence rates (95% CI: [4.1, 164]; p < 0.0001), respectively; long flights with enforced masking had no transmission reported. A 1 h increase in flight duration was associated with 1.53-fold (95% CI: [1.19, 1.66]; p < 0.001) increase in the incidence rate ratio (IRR) of cases. Masking should be considered for long flights.

Association rule mining of aircraft event causes based on the Apriori algorithm.

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF-IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2-4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

Quantification of Methane Emissions from Cold Heavy Oil Production with Sand Extraction in Alberta and Saskatchewan, Canada.

Cold heavy oil production with sand (CHOPS) is an extraction process for heavy oil in Canada, with the potential to lead to higher CH4 venting than conventional oil sites, that have not been adequately characterized. In order to quantify CH4 emissions from CHOPS activities, a focused aerial measurement campaign was conducted in the Canadian provinces of Alberta and Saskatchewan in June 2018. Total CH4 emissions from each of 10 clusters of CHOPS wells (containing 22-167 well sites per cluster) were derived using a mass balance computation algorithm that uses in situ wind data measurement on board aircraft. Results show that there is no statistically significant difference in CH4 emissions from CHOPS wells between the two provinces. Cluster-aggregated emission factors (EF) were determined using correspondingly aggregated production volumes. The average CH4 EF was 70.4 ± 36.9 kg/m3 produced oil for the Alberta wells and 55.1 ± 13.7 kg/m3 produced oil for the Saskatchewan wells. Using these EF and heavy oil production volumes reported to provincial regulators, the annual CH4 emissions from CHOPS were estimated to be 121% larger than CHOPS emissions extracted from Canada's National Inventory Report (NIR) for Saskatchewan. The EF were found to be positively correlated with the percentage of nonpiped production volumes in each cluster, indicating higher emissions for nonpiped wells while suggesting an avenue for methane emission reductions. A comparison with recent measurements indicates relatively limited effectiveness of regulations for Saskatchewan compared to those in Alberta. The results of this study indicate the substantial contribution of CHOPS operations to the underreporting observed in the NIR and provide measurement-based EF that can be used to develop improved emissions inventories for this sector and mitigate CH4 emissions from CHOPS operations.

Effects of aircraft operating fluids and environmental thermal fatigue on fly ash and steel slag based cementitious composites.

This paper investigates the performance of concrete incorporating high-volume fly ash (HVFA) and steel slag aggregates against the detrimental effects of combined cycles of environmental thermal fatigue and exposure to leaked aircraft fluids. A total of 128 cubes and 90 prisms were cast for five mixes and exposed to 60, 120, 180, 240 and 300 combined cycles. The results demonstrate the positive effect of utilization of HVFA which reduces the total amount of portlandite available in the system. The SS aggregates demonstrate a strong interlocking with the surrounding matrix and supply the necessary portlandite for continued pozzolanic reaction. However, their reaction with aircraft fluids causes significant degradation to flexural strength initially, which is redeemed by pozzolanic reaction at a later stage. Hybrid basalt and polypropylene fibres were successful in enhancing the flexural strength and reducing the cracking. The mercury intrusion porosimetry revealed a reduction in pore volume because of HVFA. Scanning electron microscopy and differential scanning calorimetry were also employed to uncover the underlying mechanisms of damage and assess the performance of the cementitious composite.

Highly conductive and durable nanocomposite hard coatings of carbon fiber reinforced thermoplastic composites against lightning strikes.

The growing use of thermoplastic composites (TPCs) like low-melting polyaryletherketone (LM-PAEK) matrices reinforced with unidirectional carbon fiber (CF) in aircraft structures presents a significant challenge in terms of lightning strikes and electromagnetic interference shielding during aircraft operations. This is due to the weak electrical conductivity of TPC structures, which results in widespread damage when struck by lightning. The repair and maintenance of these extended damaged sites can increase operational costs and loss of flights. Several lightning strike protection (LSP) systems have been developed and implemented to address these concerns. This study evaluated a highly conductive coating with a low filler rate for its effectiveness as an LSP solution for TPCs on exterior aircraft surfaces. The TPC panel without any coatings was first studied. Subsequently, the level of conductivity was increased by incorporating the nanoscale conductive fillers, silver-coated copper (Ag/Cu) nanoflakes, with a silver content of 20 wt.% (Ag20/Cu) and 30 wt.% (Ag30/Cu), correspondingly, into the coating at two loadings of 55 wt.% and 70 wt.% in an epoxy carrier for the surface coatings. The behavior of electrical and surface conductivity was thoroughly examined to understand the impact of Ag/Cu with a high aspect ratio and the effectiveness of the LSP solution. In addition, the spray-coated TPC panels underwent rigorous Zone 2A lightning strike testing using simulated lightning current, in agreement with the industry standard of Society of Automotive Engineers (SAE) Aerospace Recommended Practice (ARP) 5412B. Despite the higher resistance due to the lower conductive coating weight, the TPC panels with Ag30/Cu at loading of 70 wt.% achieved better results than those with Ag30/Cu at loading of 55 wt.%. This is evidenced by the minor structural delamination and CF breakage on the front surface, which proposes a new economic route for a sustainable post-processed LSP system in the aviation industry.

Enhancing aircraft crack repair efficiency through novel optimization of piezoelectric actuator parameters: A design of experiments and adaptive neuro-fuzzy inference system approach.

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips-a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Long-term exposure to transportation noise and diabetes mellitus mortality: a national cohort study and updated meta-analysis.

BACKGROUND: Long-term exposure to transportation noise is related to cardio-metabolic diseases, with more recent evidence also showing associations with diabetes mellitus (DM) incidence. This study aimed to evaluate the association between transportation noise and DM mortality within the Swiss National Cohort. METHODS: During 15 years of follow-up (2001-2015; 4.14 million adults), over 72,000 DM deaths were accrued. Source-specific noise was calculated at residential locations, considering moving history. Multi-exposure, time-varying Cox regression was used to derive hazard ratios (HR, and 95%-confidence intervals). Models included road traffic, railway and aircraft noise, air pollution, and individual and area-level covariates including socio-economic position. Analyses included exposure-response modelling, effect modification, and a subset analysis around airports. The main findings were integrated into meta-analyses with published studies on mortality and incidence (separately and combined). RESULTS: HRs were 1.06 (1.05, 1.07), 1.02 (1.01, 1.03) and 1.01 (0.99, 1.02) per 10 dB day evening-night level (Lden) road traffic, railway and aircraft noise, respectively (adjusted model, including NO2). Splines suggested a threshold for road traffic noise (~ 46 dB Lden, well below the 53 dB Lden WHO guideline level), but not railway noise. Substituting for PM2.5, or including deaths with type 1 DM hardly changed the associations. HRs were higher for males compared to females, and in younger compared to older adults. Focusing only on type 1 DM showed an independent association with road traffic noise. Meta-analysis was only possible for road traffic noise in relation to mortality (1.08 [0.99, 1.18] per 10 dB, n = 4), with the point estimate broadly similar to that for incidence (1.07 [1.05, 1.09] per 10 dB, n = 10). Combining incidence and mortality studies indicated positive associations for each source, strongest for road traffic noise (1.07 [1.05, 1.08], 1.02 [1.01, 1.03], and 1.02 [1.00, 1.03] per 10 dB road traffic [n = 14], railway [n = 5] and aircraft noise [n = 5], respectively). CONCLUSIONS: This study provides new evidence that transportation noise is associated with diabetes mortality. With the growing evidence and large disease burden, DM should be viewed as an important outcome in the noise and health discussion.

Frequency-Selective Radar-Absorbing Composites Using Hybrid Core-Shell Spheres.

Radar-absorbing materials (RAMs) covering the exterior surfaces of installed parts and assembled devices are crucial in absorbing most incident electromagnetic (EM) waves. This absorption minimizes reflected energy, thereby enhancing pilot safety and the stability of operating electronic devices without interference. Particularly, active stealth aircraft require effective protection from near- and far-field EM radiation across a wide spectrum of frequencies from both highly integrated electronic components and advanced enemy radars. Studies of RAMs often prioritize absorption over crucial tunability in frequency selectivity, revealing a research gap. In this study, we propose smart RAMs with frequency-selective absorption capabilities. Our approach involves incorporating two types of core-shell spheres in a polymer matrix, which feature shells of either wave-diffuse reflecting metal or wave-absorbing graphene. The key innovation lies in the ability to tailor absorption frequencies in the X-band range (8.2-12.4 GHz) by adjusting the interstitial spaces between the metallic spheres while the scattered waves are efficiently attenuated by graphene networks in the composites. On a metal substrate, a 2 mm-thick composite with an optimized structural composition and ratio of the two types of spheres exhibits a maximum absorption efficiency of 99.3%, effectively trapping and extinguishing incident waves. Combined with the structural tunability and frequency-selective properties of spherical fillers, our approach provides a scalable and effective method for creating functional isotropic coverings on various metallic surfaces.

Usefulness of aircraft and airport wastewater for monitoring multiple pathogens including SARS-CoV-2 variants.

BACKGROUND: As global travel resumed in coronavirus disease 2019 (COVID-19) endemicity, the potential of aircraft wastewater monitoring to provide early warning of disease trends for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and other infectious diseases, particularly at international air travel hubs, was recognized. We therefore assessed and compared the feasibility of testing wastewater from inbound aircraft and airport terminals for 18 pathogens including SARS-CoV-2 in Singapore, a popular travel hub in Asia. METHODS: Wastewater samples collected from inbound medium- and long-haul flights and airport terminals were tested for SARS-CoV-2. Next Generation Sequencing was carried out on positive samples to identify SARS-CoV-2 variants. Airport and aircraft samples were further tested for 17 other pathogens through quantitative reverse transcription polymerase chain reaction. RESULTS: The proportion of SARS-CoV-2-positive samples and the average virus load was higher for wastewater samples from aircraft as compared with airport terminals. Cross-correlation analyses indicated that viral load trends from airport wastewater led local COVID-19 case trends by 2-5 days. A total of 10 variants (44 sub-lineages) were successfully identified from aircraft wastewater and airport terminals, and four variants of interest and one variant under monitoring were detected in aircraft and airport wastewater 18-31 days prior to detection in local clinical cases. The detection of five respiratory and four enteric viruses in aircraft wastewater samples further underscores the potential to expand aircraft wastewater to monitoring pathogens beyond SARS-CoV-2. CONCLUSION: Our findings demonstrate the feasibility of aircraft wastewater testing for monitoring infectious diseases threats, potentially detecting signals before clinical cases are reported. The triangulation of similar datapoints from aircraft wastewater of international travel nodes could therefore serve as a useful early warning system for global health threats.

Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers.

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

Evaluation of methods for characterizing the fine particulate matter emissions from aircraft and other diffusion flame combustion aerosol sources.

The U. S. Environmental Protection Agency in collaboration with the U. S. Air Force Arnold Engineering Development Complex conducted the VAriable Response In Aircraft nvPM Testing (VARIAnT) 3 and 4 test campaigns to compare nonvolatile particulate matter (nvPM) emissions measurements from a variety of diffusion flame combustion aerosol sources (DFCASs), including a Cummins diesel engine, a diesel powered generator, two gas turbine start carts, a J85-GE-5 turbojet engine burning multiple fuels, and a Mini-CAST soot generator. The VARIAnT research program was devised to understand reported variability in the ARP6320A sampling system nvPM measurements. The VARIAnT research program has conducted four test campaigns to date with the VARIAnT 3 and 4 campaigns devoted to: (1) assessing the response of three different black carbon mass analyzers to particles of different size, morphology, and chemical composition; (2) characterizing the particles generated by 6 different combustion sources according to morphology, effective density, and chemical composition; and (3) assessing any significant difference between black carbon as determined by the 3 mass analyzers and the total PM determined via other techniques. Results from VARIAnT 3 and 4 campaigns revealed agreement of about 20% between the Micro-Soot Sensor, the Cavity Attenuated Phase Shift (CAPS PMSSA) monitor and the thermal-optical reference method for elemental carbon (EC) mass, independent of the calibration source used. For the LII-300, the measured mass concentrations in VARIAnT 3 fall within 18% and in VARIAnT 4 fall within 27% of the reference EC mass concentration when calibrated on a combustor rig in VARIAnT 3 and on an LGT-60 start cart in VARIAnT 4, respectively. It was also found that the three mass instrument types (MSS, CAPS PMSSA, and LII-300) can exhibit different BC to reference EC ratios depending on the emission source that appear to correlate to particle geometric mean mobility diameter, morphology, or some other parameter associated with particle geometric mean diameter (GMD) with the LII-300 showing a slightly stronger apparent trend with GMD. Systematic differences in LII-300 measured mass concentrations have been reduced by calibrating with a turbine combustion as a particle source (combustor or turbine engine). With respect to the particle size measurements, the sizing instruments (TSI SMPS, TSI EEPS, and Cambustion DMS 500) were found to be in general agreement in terms of size distributions and concentrations with some exceptions. Gravimetric measurements of the total aerosol mass produced by the various DFCAs differed from the reference EC, BC and integrated particle size distribution measured aerosol masses. The measurements of particle size distributions and single particle analysis performed using the miniSPLAT indicated the presence of larger particles (≳150 nm) having more compact morphologies, higher effective density, and a composition dominated by OC and containing ash. This increased large particle fraction is also associated with higher values of single scattering albedo measured by the CAPS PMSSA instrument and higher OC measurements. These measurements indicate gas turbine engine emissions can be a more heterogeneous mix of particle types beyond the original E-31 assumption that engine exit exhaust particles are mainly composed of black carbon.

Contraction of Respiratory Viral Infection During air Travel: An Under-Recognized Health Risk for Athletes.

Air travel has an important role in the spread of viral acute respiratory infections (ARIs). Aircraft offer an ideal setting for the transmission of ARI because of a closed environment, crowded conditions, and close-contact setting. Numerous studies have shown that influenza and COVID-19 spread readily in an aircraft with one virus-positive symptomatic or asymptomatic index case. The numbers of secondary cases differ markedly in different studies most probably because of the wide variation of the infectiousness of the infector as well as the susceptibility of the infectees. The primary risk factor is sitting within two rows of an infectious passenger. Elite athletes travel frequently and are thus prone to contracting an ARI during travel. It is anecdotally known in the sport and exercise medicine community that athletes often contract ARI during air travel. The degree to which athletes are infected in an aircraft by respiratory viruses is unclear. Two recent studies suggest that 8% of Team Finland members traveling to major winter sports events contracted the common cold most probably during air travel. Further prospective clinical studies with viral diagnostics are needed to understand the transmission dynamics and to develop effective and socially acceptable preventive measures during air travel.

The usability analysis of software loading tools in a commercial airline.

Background: Aircraft software loading tools evolved as enhanced floppy disks and different types of portable data loaders emerged into the modern world. However, there was a lack of academic research centered on the usability of those tools. Objective: This study analyzed the usability of three aircraft software loading tools: floppy disks, Teledyne PMAT, and MBS mini PDL. Methods: A total of 20 avionics technicians answered 10 System Usability Scale (SUS) indicators and performed the software loading task. These professionals completed three sets of SUS questionnaires, one set for each software tool. Results: Analysis of Variance (ANOVA) indicated that there were statistical differences in SUS score and completion time. The comparable SUS score and completion time pertained to the following tools: floppy disks & MBS mini PDL and Teledyne PMAT & MBS mini PDL. Pearson correlation analysis noted a significant positive relationship between SUS score & software tool. Moreover, there was a significant negative relationship between SUS score & completion time and software tool & completion time. However, avionics technicians' years of experience lacked a significant impact on SUS score and completion time. Ultimately, it was recommended to utilize MBS mini PDL. This aircraft loading tool had the most significant impact, highest SUS score, and fastest completion time. Conclusions: The researchers presented managerial implications if MBS mini PDL was utilized, including sales increase and overhead cost savings. Finally, this research was the first study that analyzed the usability of the commonly used aircraft software loading tools.