Properties and Processing of Aviation Exhaust Aerosol at Cruise Altitude Observed from the IAGOS-CARIBIC Flying Laboratory.

The characteristics of aviation-induced aerosol, its processing, and effects on cirrus clouds and climate are still associated with large uncertainties. Properties of aviation-induced aerosol, however, are crucially needed for the assessment of aviation's climate impacts today and in the future. We identified more than 1100 aircraft plume encounters during passenger aircraft flights of the IAGOS-CARIBIC Flying Laboratory from July 2018 to March 2020. The aerosol properties inside aircraft plumes were similar, independent of the altitude (i.e., upper troposphere, tropopause region, and lowermost stratosphere). The exhaust aerosol was found to be mostly externally mixed compared to the internally mixed background aerosol, even at a plume age of 1 to 3 h. No enhancement of accumulation mode particles (diameter >250 nm) could be detected inside the aircraft plumes. Particle number emission indices (EIs) deduced from the observations in aged plumes are in the same range as values reported from engine certifications. This finding, together with the observed external mixing state inside the plumes, indicates that the aviation exhaust aerosol almost remains in its emission state during plume expansion. It also reveals that the particle number EIs used in global models are within the range of the EIs measured in aged plumes.

Relationships among barodontalgia prevalence, altitude, stress, dental care frequency, and barodontalgia awareness: a survey of Turkish pilots.

Background: Gas expansion in body cavities due to pressure changes at high altitudes can cause barodontalgia. This condition may compromise flight safety. Aim: To investigate relationships among barodontalgia awareness, dental visit frequency, and barodontalgia prevalence in civilian and military pilots operating at high altitudes. Materials and Methods: Civilian pilots from Turkish Airlines and military pilots from the Turkish Air Force, flying between November 2022 and January 2023, participated in this study. A 20-question survey was administered to 750 pilots, covering topics such as barodontalgia awareness, dental visit frequency, breaks after dental treatments, in-flight pain, and pain type and severity. The voluntary surveys were distributed by email. Results: Of the 750 pilots, 526 completed the survey; 61% were aware of barodontalgia, and 81% of pilots who had experienced it reported pain at altitudes <2000 feet. The study revealed higher barodontalgia awareness among pilots who had experienced it, with the highest prevalence among jet pilots. Pilots with barodontalgia also showed a higher frequency of dental visits (p < 0.001). Additionally, this group reported more frequent interruption of flight due to dental treatment (IFDT), more problems experienced in flights after treatment (PFAT), and higher instances of bruxism or teeth clenching during flight, suggesting stress and anxiety (p < 0.05). Conclusions: Barodontalgia, a type of pain linked to stress, significantly impacts pilot performance, and can threaten flight safety, even at lower altitudes. Thus, there is a need to educate pilots about stress management, barodontalgia awareness, and the importance of regular dental check-ups.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports.

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017-2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Indonesian air medical evacuation: Analyzing readiness and proposing an integrated standard procedure.

Indonesia still faces challenges in providing healthcare services, and it is crucial to develop an air medical evacuation services system for at least two reasons. Firstly, Indonesia is an archipelagic country and a popular tourist destination. Secondly, there are still significant disparities in the number and types of healthcare facilities and health workers nationwide. To respond to the current situation, the healthcare providers and government have made some efforts regarding air medical evacuation but are showing an unintegrated system. This qualitative study aimed to explore the current implementation of air medical evacuation in Indonesia and to propose an integrated standard procedure that all related stakeholders can adopt at the national and regional levels. The study used a multi-case design analysis, collecting both primary and secondary data. Secondary data was gathered through desk studies to learn related policies and previous studies. Primary data was collected through observation and in-depth interviews with relevant stakeholders, including regulators, service providers, practitioners, and non-governmental organizations. The study found that there is currently a regulatory gap for the implementation of air medical evacuation services in Indonesia. The readiness of the Health Human Resources (HHR) is limited in terms of qualification and competency, and the definitive infrastructure of air medical evacuation requires improvement since the providers continue to use the airport for civil transportation. Besides, the interaction pattern between stakeholders needs to be integrated into standardized procedures. Therefore, the study recommends proposing an integrated standard procedure and actionable recommendations to advocate for all stakeholders.

Inflammatory Modifications in Paranasal Sinuses and Ostiomeatal Complex Anatomical Variations in Jet Aircraft Pilots: A Computed Tomography Study.

Introduction Jet aircraft pilots are exposed to huge pressure variation during flight, which affect physiological functions as systems, such as the respiratory system. Objectives The objective of the present investigation was to evaluate inflammatory changes of paranasal sinuses of jet aircraft pilots before and after a jet aircraft training program, using multislice computed tomography (CT), in comparison with a group of nonairborne individuals with the same age, sex, and physical health conditions. A second objective of the present study was to assess the association between the ostiomeatal complex obstruction and its anatomical variations. Methods The study group consisted of 15 jet aircraft pilots participating in the training program. The control group consisted of 41 nonairborne young adults. The 15 fighter pilots were evaluated before initiating the training program and after their final approval for the presence of inflammatory paranasal sinus disease. The ostiomeatal complex anatomical variations and obstructions were analyzed in pilots after the training program. Results Jet aircraft pilots presented higher incidence of mucosal thickening in maxillary sinus and anterior ethmoid cells than controls. Prominent ethmoidal bulla showed significant association with obstruction of the osteomeatal complex. Conclusions Jet aircraft pilots present increased inflammatory disease when compared with nonairborne individuals. The presence of a prominent ethmoidal bulla is associated with ostiomeatal complex obstruction.

Occupational Health Challenges for Aviation Workers Amid the Changing Climate: A Narrative Review.

Although there are many forecasts regarding the impact of climate change on the aviation sector, a critical but frequently neglected dimension is the occupational safety risks faced by aviation professionals. This narrative review explores the potential impacts of the changing climate on the health and safety of aviation personnel. Furthermore, we examine the significance of resilience in helping these workers adapt and effectively manage climate-related challenges in their professional lives. Climate change poses increasing threats to the well-being of flight personnel through elevated temperatures, heightened ultraviolet radiation exposure, increased mental workload from extreme weather events, and other psychological stressors. Building resilience through workforce training, planning, and adaptation can reduce vulnerability. In future research, the iterative process of selecting measurement components to gauge the impact of climate change should balance feasibility, relevance for stakeholders, and accurately capturing exposure effects. For instance, while salivary cortisol measures stress biologically, assessments of depression or burnout may provide more nuanced insights on pilot health for industry decision-makers managing climate impacts. In conclusion, a strategic emphasis on enhancing the physical and psychological well-being of the aviation workforce is imperative for facilitating a more efficient adaptation within the sector. This is of paramount importance, considering the critical function that aviation serves in fostering human connectivity. Consequently, it is essential for regulatory bodies and policymakers to prioritize the safeguarding of employee health in the face of climate change challenges.

Employee Grievance Redressal and Corporate Ethics: Lessons from the Boeing 737-MAX Crashes.

Two Boeing 737-MAX passenger planes crashed in October 2018 and March 2019, suspending all 737-MAX aircraft. The crashes put Boeing's corporate practices and culture under the spotlight. The main objective of this paper is to use the case of Boeing to highlight the importance of efficient employee grievance redressal mechanisms and an independent external regulator. The methodology adopted is a qualitative analysis of statements of various whistleblowers and Boeing and the Federal Aviation Administration (FAA) stakeholders. It suggests that employee feedback flowing up the chain of command should be more flexible and dealt with more seriousness. It recommends that companies adopt a cooling-off period or a lifetime restriction for employees who have gone through the revolving door between regulators and the industry. The Boeing 737-MAX case, which emphasizes the ethical obligations of the job, can offer value to engineers, engineering educators, managers, ombudsmen, and human resource professionals.

Perception, confidence, and willingness to respond to in-flight medical emergencies among medical students: a cross sectional study.

BACKGROUND: In-flight medical emergencies (IMEs) are expected to increase as air travel normalized in the post-COVID-19 era. However, few studies have examined health professions students' preparedness to respond to such emergencies. Therefore, this study aimed to investigate medical students' knowledge, confidence, and willingness to assist during an IME in their internship program. METHODS: This cross-sectional survey utilized an online, self-administered questionnaire-based survey targeted at medical students at two medical colleges in Saudi Arabia. The questionnaire comprised three parts: sociodemographic characteristics, knowledge about aviation medicine (10 items), and confidence (7 items)/willingness (4 items) to assist during an IME. Odds Ratios (OR) and 95% Confidence Intervals (95%CI) were computed to detect potential associations between the knowledge levels and the other independent variables. Responses to confidence and willingness questions were scored on a 5-point Likert scale. RESULTS: Overall, 61.4% of participants had inadequate knowledge scores for providing care during an IME, and the proportion of participants did not differ between those who had or had not attended life support courses (60.4% vs. 66.7%, p > 0.99). Only frequency of air travel ≥ two times per year was associated with higher odds of adequate knowledge score [OR = 1.89 (95%CI 1.14-3.17), p = 0.02]. In addition, 93.3% of the participants had low, 6.3% had moderate, and 0.8% had high willingness scores, while 86.3% had low, 12.2% had moderate, and 1.5% had high confidence scores. There were no differences in the proportion of participants with low, moderate, and high willingness or confidence scores by attendance in life support courses. CONCLUSION: Even though over 8 in 10 students in our study had previously attended life support courses, the overwhelming majority lacked the knowledge, confidence, and willingness to assist. Our study underscores the importance of teaching medical students about IMEs and their unique challenges before entering their 7th-year mandatory general internship.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases.

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SYNOPSIS: The utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.

Digital twin: Data exploration, architecture, implementation and future.

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.

An HPLC-ESI-QTOF method to analyze polar heteroatomic species in aviation turbine fuel via hydrophilic interaction chromatography.

Aviation turbine fuel is a complex mixture of thousands of compounds. An analytical method using hydrophilic interaction liquid chromatography (HILIC) coupled with electrospray ionization and quadrupole time-of-flight mass spectrometry (ESI-QTOF) was developed for the identification of heteroatomic, polar compounds in aviation turbine fuel. Although compounds containing oxygen, nitrogen, and sulfur functional groups are each found at low levels (<0.1 % by mass) in fuels, their presence can generate significant effects on fuel properties. The HILIC-ESI-QTOF method is a combined separation and detection technique that possesses many advantages including a fast and simple sample preparation-requiring no extraction step therefore ensuring no loss of compounds of interest-and the ability to acquire high-fidelity compound data for chemometric analysis of heteroatomic species in aviation turbine fuel. In the development of the method, it was found that the chromatographic conditions and nature of the injection sample had a significant effect on separation efficiency and repeatability. For a sample dataset optimized using a singular aviation turbine fuel, retention time shift was able to be reduced from 0.4 min to 2.0 % relative standard deviation (RSD) to approximately 0.1 min with RSD of 0.4 % using the newly developed method. In addition, a high number of untargeted molecular features (944) and targeted amines (121) were able to be identified when utilizing optimal method conditions. The specific benefits and limitations of utilizing HILIC techniques with HPLC-ESI-QTOF are also discussed herein. This new method is currently being expanded to include analysis of all heteroatoms and is being applied to real fuel sets. The results of these studies are forthcoming.

Effect of the Technological Parameters of Milling on Residual Stress in the Surface Layer of Thin-Walled Plates.

The production of thin-walled elements, especially those with large overall dimensions, poses numerous technological and operational problems. One of these problems relates to the machining-induced strain of such elements resulting from residual stress generated during the machining process. This study investigates the effect of the technological parameters of milling on residual stress in the surface layer of thin-walled plates made of aluminum alloy EN AW-2024 T351 for aerospace applications. The results have shown that residual stress increases with the cutting speed only to a certain point, reaching the maximum value at vc = 750 m/min. At a cutting speed vc = 900 m/min, residual stress significantly decreases, which probably results from the fact that the milling process has entered the High-Speed Cutting range, and this inference agrees with the results obtained for the cutting force component. Residual stress increases with the feed per tooth, while the relationship between residual stress and milling width is the same as that established for residual stress and variable cutting speed. Positive tensile stress is obtained in every tested case of the milling process. The results have also shown that the induced residual stress affects the strain of machined thin-walled parts, as proved by the strain results obtained for milled thin walls.

Aviation Mutagenesis Alters the Content of Volatile Compounds in Dahongpao (Camellia sinensis) Leaves and Improves Tea Quality.

Aviation mutagenesis is a fast and efficient breeding method. In this study, we analyzed the effect of aviation mutagenesis on volatile compounds and odor characteristics in Dahongpao fresh leaves and gross tea for the first time. The results showed that aviation mutagenesis significantly increased the total volatile compounds of Dahongpao fresh leaves and gross tea. Aviation mutagenesis most critically significantly increased the content of beta-myrcene in Dahongpao fresh leaves, prompting its conversion to beta-pinene, cubebol, beta-phellandrene, zingiberene, (Z,Z)-3,6-nonadienal, and 6-pentyloxan-2-one after processing, which in turn enhanced the fruity, green, spicy, and woody odor characteristics of the gross tea. This study provided a reference for further exploration of aviation mutagenic breeding of Camellia sinensis.

Creating augmented reality-based experiences for aviation weather training: Challenges, opportunities, and design implications for 3D authoring.

This paper examines opportunities and challenges of integrating augmented reality (AR) into education and investigates requirements to enable instructors to author AR educational experiences. Although AR technology is recognised for its potential in educational enhancement, it poses challenges for instructors creating AR-based experiences due to their limited digital skills and the complexity of 3D authoring tools. Semi-structured interviews with 17 aviation instructors identified current pedagogical approaches, gaps, and potential applications of AR in aviation weather education. Additionally, results highlighted the benefits of AR and obstacles to its integration into education, followed by outlining design priorities and user needs for educational AR authoring. For AR authoring toolkit development, this study recommended incorporating interactive AR lesson modules, early development of user requirements, and prebuilt AR modules. Findings will guide the development of a 3D authoring toolkit for non-technologist instructors, enabling wider AR use in aviation weather education and other educational fields.

Research interviews with aviation instructors were conducted to derive design implications of AR authoring toolkits for non-technologist instructors. Key findings highlighted gaps in aviation weather education, potential AR applications, and barriers to AR in education. Design recommendations emphasised incorporating interactive AR lesson modules, initial user requirements, and prebuilt AR modules.

Physiological records-based situation awareness evaluation under aviation context: A comparative analysis.

Situational Awareness (SA) assessment is of paramount importance in various domains, with particular significance in the military for safe aviation decision-making. It involves encompassing perception, comprehension, and projection levels in human beings. Accurate evaluation of SA statuses across these three levels is crucial for mitigating human false-positive and false-negative rates in monitoring complex scenarios in the aviation context. This study proposes a comprehensive comparative analysis by involving two types of physiological records: electroencephalogram (EEG) signals and brain electrical activity mapping (BEAM) images. These two modalities are leveraged to automate precise SA evaluation using both conventional machine learning and advanced deep learning techniques. Benchmarking experiments reveal that the BEAM-based deep learning models attain state-of-the-art performance scores of 0.955 for both SA perception and comprehension levels, respectively. Conversely, the EEG signals-based manual feature extraction, selection, and classification approach achieved a superior accuracy of 0.929 for the projection level of SA. These findings collectively highlight the potential of deploying diverse physiological records as valuable computational tools for enhancing SA evaluation throughout aviation decision-making safety.

Aviation fuel based on wastewater-grown microalgae: Challenges and opportunities of hydrothermal liquefaction and hydrotreatment.

The current technical issues related to the conversion of algal biomass into aviation biofuel through hydrothermal liquefaction (HTL) and the upgrading of bio-oil through hydrotreatment have been reviewed and consolidated. HTL is a promising route for converting microalgae into sustainable aviation fuel (SAF). However, HTL must be followed by the hydrotreatment of bio-oil to ensure that its composition and properties are compatible with SAF standards. The fact that microalgae offer the possibility of recovering wastewater treatment resources not only makes them more attractive but also serves as an incentive for wastewater treatment, especially in countries where this service has not been universalized. The combination of SAF and wastewater treatment aligns with the Sustainable Development Goals of the United Nations, representing an advantageous opportunity for both aviation and sanitation. In this context, the utilization of HTL by-products in the concept of a biorefinery is essential for the sustainability of aviation biofuel production through this route. Another important aspect is the recovery and reuse of catalysts, which are generally heterogeneous, allowing for recycling. Additionally, discussions have focused on biomass pretreatment methods, the use of solvents and catalysts in HTL and hydrotreatment reactions, and the operational parameters of both processes. All these issues present opportunities to enhance the quantity and quality of bio-oil and aviation biofuel.

COVID-19 passenger screening to reduce travel risk and translocation of disease.

Aviation passenger screening has been used worldwide to mitigate the translocation risk of SARS-CoV-2. We present a model that evaluates factors in screening strategies used in air travel and assess their relative sensitivity and importance in identifying infectious passengers. We use adapted Monte Carlo simulations to produce hypothetical disease timelines for the Omicron variant of SARS-CoV-2 for travelling passengers. Screening strategy factors assessed include having one or two RT-PCR and/or antigen tests prior to departure and/or post-arrival, and quarantine length and compliance upon arrival. One or more post-arrival tests and high quarantine compliance were the most important factors in reducing pathogen translocation. Screening that combines quarantine and post-arrival testing can shorten the length of quarantine for travelers, and variability and mean testing sensitivity in post-arrival RT-PCR and antigen tests decrease and increase with the greater time between the first and second post-arrival test, respectively. This study provides insight into the role various screening strategy factors have in preventing the translocation of infectious diseases and a flexible framework adaptable to other existing or emerging diseases. Such findings may help in public health policy and decision-making in present and future evidence-based practices for passenger screening and pandemic preparedness.

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Study on the Electrical Insulation Properties of Modified PTFE at High Temperatures.

During the operation of multi-electric aircraft, the polytetrafluoroethylene (PTFE) material used to insulate the aviation cable is subjected to a high electric field while working under the extreme conditions of high temperatures for a long time, which can easily cause a partial discharge and even flashover along the surface, which seriously threaten the safe operation of the aircraft. In this paper, the electrical insulation properties of PTFE were regulated via modification by the magnetron sputtering of TiO2 under high temperatures, and modified PTFE with different sputtering times was prepared. The direct current (DC) surface discharge, surface flashover, and electric aging characteristics of modified PTFE were studied under the condition of 20~200 °C, and the mechanisms by which modification by sputtering of TiO2 and high temperature influence the insulation properties were analyzed. The results show that the surface discharge intensity increases with the increase in temperature, the modification by sputtering of TiO2 can significantly inhibit the partial discharge of PTFE, and the flashover voltage first increases and then decreases with the increase in the modification time. The modification by magnetron sputtering can effectively increase the surface potential decay rate of the PTFE, increase the shallow trap energy density, effectively avoid charge accumulation, inhibit the partial discharge phenomenon, and improve the surface electrical insulation and anti-aging properties.

A hybrid machine learning-based model for predicting flight delay through aviation big data.

The prediction of flight delays is one of the important and challenging issues in the field of scheduling and planning flights by airports and airlines. Therefore, in recent years, we have witnessed various methods to solve this problem using machine learning techniques. In this article, a new method is proposed to address these issues. In the proposed method, a group of potential indicators related to flight delay is introduced, and a combination of ANOVA and the Forward Sequential Feature Selection (FSFS) algorithm is used to determine the most influential indicators on flight delays. To overcome the challenges related to large flight data volumes, a clustering strategy based on the DBSCAN algorithm is employed. In this approach, samples are clustered into similar groups, and a separate learning model is used to predict flight delays for each group. This strategy allows the problem to be decomposed into smaller sub-problems, leading to improved prediction system performance in terms of accuracy (by 2.49%) and processing speed (by 39.17%). The learning model used in each cluster is a novel structure based on a random forest, where each tree component is optimized and weighted using the Coyote Optimization Algorithm (COA). Optimizing the structure of each tree component and assigning weighted values to them results in a minimum 5.3% increase in accuracy compared to the conventional random forest model. The performance of the proposed method in predicting flight delays is tested and compared with previous research. The findings demonstrate that the proposed approach achieves an average accuracy of 97.2% which indicates a 4.7% improvement compared to previous efforts.