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This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Transportation Systems & Applications is the property of Journal of Intelligent Transportation Systems & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents. [ABSTRACT FROM AUTHOR]

There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information. [ABSTRACT FROM AUTHOR]

Traditional flight simulation models often operate on the premise of a steady atmosphere, overlooking the complexities of actual atmospheric dynamics and the flight safety risks posed by wind disturbances, such as turbulence. To Address this oversight, the present study introduces a method for generating three-dimensional atmospheric turbulence based on spatial correlation functions. This method, rigorously validated against correlation and spectral benchmarks, guarantees isotropic properties in the synthesized turbulence fields. Through interpolation techniques, the model integrates the spatial atmospheric turbulence into the flight simulation framework effectively. The paper highlights the application of this model by examining the impact of atmospheric turbulence on the precise flight dynamics of quadcopter UAVs during aerial refueling operations. The findings demonstrate the model's pertinence not only to UAVs but also to the broader spectrum of aircraft and their operational procedures. [ABSTRACT FROM AUTHOR]

This paper investigates the usage of a systemic neutralized taxonomy for understanding human factors to foster a restorative culture when studying adverse events. The available studies supporting accident analysis from a system‐theoretic perspective with human factors‐based methods are grounded on error taxonomies and do not reinforce the non‐judgemental dimension that is encouraged by modern safety science. We propose a methodology to integrate the system‐theoretic accident analysis technique, Causal Analysis based on System Theory, with a neutralized taxonomy of human explanatory factors. The proposed methodology has been applied to an aviation accident involving a military aircraft and a glider. This case revealed various critical interactions among system components, which require dedicated safety recommendations that go beyond the identification of single points of failures and root causes, leading to a deeper understanding of socio‐technical orchestrations. Despite the use case in aviation, the proposed methodology remains suitable for various types of safety occurrences in diverse domains and industrial settings. It also represents the basis for supporting future proactive safety‐related decision‐making processes. [ABSTRACT FROM AUTHOR]

Abstract This study aims to offer aviation safety researchers, practitioners, and decision-makers a comprehensive exploration of integrating advanced technologies, such as artificial intelligence and machine learning, to inform and fortify future safety strategies. Focusing on systematic and bibliometric perspectives, the paper reviewed 224 articles in the Scopus database from 2004 to 2024 (January). Key findings highlight China’s notable contributions to aviation safety research, underscoring its leadership in international collaboration. The techniques employed encompass machine learning, time series models, deep learning, AI, neurophysiological modeling, and optimization algorithms. The analysis discerns prominent research trends, including aviation accident analysis, pilot behavior, aviation safety measures, and endeavors to enhance safety standards. The aviation industry’s steadfast commitment to safety, efficiency, and technological innovation is evident. By uncovering the main structures, foci, and trends in aviation safety research, this study equips researchers and practitioners with crucial insights into ongoing endeavors and potential future developments, fostering a more profound understanding of aviation safety.

Warnings about the risks of literal-minded automation—a system that can't tell if its model of the world is the world it is actually in—have been sounded for over 70 years. The risk is that a system will do the "right" thing—its actions are appropriate given its model of the world, but it is actually in a different world—producing unexpected/unintended behavior and potentially harmful effects. This risk—wrong, strong, and silent automation—looms larger today as our ability to deploy increasingly autonomous systems and delegate greater authority to such systems expands. It already produces incidents, outages of valued services, financial losses, and fatal accidents across different settings. This paper explores this general and out-of-control risk by examining a pair of fatal aviation accidents which revolved around wrong, strong and silent automation. [ABSTRACT FROM AUTHOR]

Controlling is considered as a versatile modern focus of management. It is widespread in various fields of human activity but has not found direct application in civil aviation yet. Meanwhile, any aviation organization is subject to the general laws of management, therefore, controlling can and should find its application in the management of an aviation organization. Due to the particular importance of safety management considerations, controlling, as a management concept that allows you to control processes rather than results, fits seamlessly into the procedures of safety management systems (SMS) of aviation service providers. In particular, the development and monitoring of safety performance indicators (SPI) can be considered as a key element of operational controlling. In the SMS, the procedure to deal with the SPI, in conjunction with the risks mitigation procedure for safety, is an essential component of the entire system. To ensure the effectiveness of this procedure in the Air Traffic Service (ATS) organization, it is necessary to develop a balanced overall SPI. As the analysis showed, the indicators applicable in ATS organizations are focused on considering only incidents of the same “weight” and do not objectively reflect a level of safety assurance at ATS and its dynamics. The article presents a variant of developing a revised balanced indicator, which considers less significant deviations from the proper ATS system operation, errors and violations of personnel. The indicator was developed on the basis of the expert survey of ATS specialists. Monitoring and predicting of indicators are also important tasks of operational controlling. These problems can be solved by various methods, the applicability and comparative effectiveness of some of them are discussed in this article. All calculations are based on real data of one of the major ATS organizations of the Russian Federation.

Modern aviation security systems are largely tied to the work of screening operators. Due to physical characteristics, they are prone to problems such as fatigue, loss of attention, etc. There are methods for recognizing such objects, but they face such difficulties as the specific structure of luggage X-ray images. Furthermore, such systems require significant computational resources when increasing the size of models. Overcoming the first and second disadvantage can largely lie in the hardware plane. It needs new introscopes and registration techniques, as well as more powerful computing devices. However, for processing, it is more preferable to improve quality without increasing the computational power requirements of the recognition system. This can be achieved on traditional neural network architectures, but with the more complex training process. A new training approach is proposed in this study. New ways of baggage X-ray image augmentation and advanced approaches to training convolutional neural networks and vision transformer networks are proposed. It is shown that the use of ArcFace loss function for the task of the items binary classification into forbidden and allowed classes provides a gain of about 3–5% for different architectures. At the same time, the use of softmax activation function with temperature allows one to obtain more flexible estimates of the probability of belonging, which, when the threshold is set, allows one to significantly increase the accuracy of recognition of forbidden items, and when it is reduced, provides high recall of recognition. The developed augmentations based on doubly stochastic image models allow one to increase the recall of recognizing dangerous items by 1–2%. On the basis of the developed classifier, the YOLO detector was modified and the mAP gain of 0.72% was obtained. Thus, the research results are matched to the goal of increasing efficiency in X-ray baggage image processing. [ABSTRACT FROM AUTHOR]

Recent pilot shortages have brought pilot training into focus as the industry attempts to rectify a compounding problem. The FAA has implemented some recent rule-making regarding pilot training that has left the General Aviation community questioning the motive or justification behind the rules. FAA incident data are inconsistent, specifically with aviation activity in general; flight activity that does not result in an accident or incident is not recorded for analysis. Despite the present shortcomings in the AIDS data, applying Machine Learning techniques make it possible to predict what characteristics mitigate aircraft damage during an incident (AUC=0.913, Accuracy=97%,Recall=84%, Precision=89%). Machine Learning analysis of incident data may assist in evidence-based rulemaking for pilot training. Such rule-making is more likely to impact aviation safety positively and resonate positively within the aviation community. The results highlight the importance of taking immediate steps to improve database quality through improved data governance. [ABSTRACT FROM AUTHOR]

The article is a review and focuses on the analysis and interpretation of Nassim Nicholas Taleb's 'Black Swan' theory in the context of aviation safety, considering various aspects of risk management and preparation for unforeseeable events in aviation. We present the thesis that the Black Swan theory has significant implications for the development of risk management strategies in aviation, emphasising the need to prepare for rare but catastrophic events that traditional risk analysis methods may overlook. The aim of the research is to understand how the Black Swan theory can be integrated into existing aviation safety frameworks and how it can contribute to the aviation industry being better prepared for extreme events. The article stands out for its original approach to the analysis of the Black Swan theory in the context of the specific nature of aviation safety, highlighting new perspectives in aviation risk management. It provides valuable insights into the practical aspects of applying the Black Swan theory to aviation, offering a novel approach to predicting and responding to unexpected aviation events. This is important for both aviation safety theorists and practitioners in this field. The research covers extensive literature analysis, including case studies, risk management theories and current aviation safety strategies, providing a comprehensive overview of the issue. [ABSTRACT FROM AUTHOR]

Safety in General Aviation has been a continuous concern. About 12% of all airplane accidents in General Aviation involve nose-overs and nose-down events. A total of 134 accidents reported by the National Transportation Safety Board that include nose-overs and nose-downs were analyzed for their main causes. It was found that 35% of the defining events involved a loss of control on the ground while 58% of the total dataset involved tailwheel-type aircraft. A relatively high proportion of aircraft built before 1950 were found, which are also aircraft that have tailwheel-type landing gear, and thereby a higher propensity for ground loops and nose-overs. It is shown that the high accident rate in General Aviation, especially for accidents that did not result in a fatality, was, to an important extent, explained by tailwheel and older aircraft in the US General Aviation airplane fleet struggling with controlling the aircraft on the ground. Attention to this group of aircraft in future studies may help to more effectively address the relatively high accident rates in General Aviation. [ABSTRACT FROM AUTHOR]

As communication and trust are both linked with safety, this paper aims to investigate their contribution to aviation maintenance incidents and accidents. For this purpose, the content analysis method is used to investigate whether trust and communication factors were present in aviation maintenance occurrences. Content analysis is used as a qualitative and quantitative tool. For the data analysis, apart from direct investigation for keywords throughout the material for analysis (aviation accident/incident reports with maintenance involvement), a survey tool was also employed. The items of this survey tool (Communication and Trust Question Set) were used to filter the reports’ text to indirectly identify the two factors under examination (communication and trust). As a qualitative tool, the content analysis yielded results via mapping and narrative techniques. As a quantitative tool, results were obtained and reported with the help of descriptive statistics (counts, frequencies). The results indicated that elements of trust and communication are indirectly detectable in aviation maintenance occurrences. Both ineffective communication and lack of trust were identified as a key accident/incident causal condition. Interpersonal trust is recommended to be included in the implementation requirements of any communication system. The limitations associated with the difference in the structure and consistency of the examined material are also discussed.

Aviation fuel with low conductivity will carry a certain amount of static charges when flowing through pipelines and other structures. These static charges will eventually flow into the fuel tank. It is inconvenient to carry out the numerical calculation and experimental measurement of the static electricity in the irregular fuel tank with large fuel load. The simulation software COMSOL Multiphysics is used to calculate the electrostatic potential distribution, electrostatic discharge potential and electrostatic ignition energy during the oil charging process. The effects of oil level height and initial charge density on the electrostatic discharge and ignition possibility of oil tank are studied. By calculating the minimum breakdown voltage required for generating electrostatic discharge during oil charging and the energy carried by electrostatic, we can analyze whether the potential in the oil tank will cause an electrostatic breakdown and ignite the mixed gas in the oil tank in this way. The study provides a new idea for the risk assessment of oil tank explosion caused by static electricity. [ABSTRACT FROM AUTHOR]

The comparison between air law and maritime law reveals both similarities and distinctions rooted in the unique frameworks of the International Civil Aviation Organization (ICAO) and The International Maritime Organization (IMO). While both entities were established through separate treaties, the Chicago Convention birthed ICAO, emphasizing the organization’s Assembly, Council, and auxiliary bodies. In contrast, the IMO Convention, also known as the Convention on the International Maritime Organization, forms the basis for IMO’s structure and functions as outlined in its preamble. The core objectives of IMO revolve around fostering collaboration among governments to enhance regulatory frameworks for international maritime trade. This encompasses advocating for elevated standards in maritime safety, navigation efficiency, and marine pollution prevention. Similarly, ICAO aims to establish principles and techniques for air navigation, promoting safe, regular, economical, and efficient air transport. The Chicago Convention primarily focuses on regulating international civil aviation, ensuring its orderly development and safety through defined principles and procedures, including standards for airspace sovereignty, aircraft registration, airworthiness, and aviation security. Conversely, the IMO Convention tackles various aspects of international maritime transportation, spanning safety, security, environmental protection, and shipping efficiency. Despite being specialized agencies of the United Nations, both ICAO and IMO face the challenge of accommodating diverse interests and viewpoints of their member states without the autonomy enjoyed by the private sector. Nonetheless, both organizations have consistently served the international community in facilitating world trade and commerce within their respective domains. This article discusses details of comparison and contrasts between ICAO, IMO, and air law and maritime law in their treaty settings. [ABSTRACT FROM AUTHOR]

Copyright of Filosofija, Sociologija is the property of Lithuanian Academy of Sciences Publishers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convective weather is often accompanied by precipitation and windshear, seriously endangering the safety of aircraft during takeoff and landing. However, under rainfall conditions, conventional wind lidars have a limited detection range due to significant signal attenuation. To solve this problem, a 200 mm temperature-controlled telescope coated with a hydrophobic film is applied in the coherent Doppler wind lidar system to improve the detection capability in rain. The maximum detection range of the lidar is extended to 30 km and demonstrated at Kunming Changshui International Airport at an altitude of 2102 m. Firstly, the detection accuracy and maximum detection range of the lidar are verified. Through the analysis of the horizontal wind field under two typical convective weather conditions, it is found that convective weather often accompanies low-level convergence and divergence structures, leading to headwind shear and crosswind shear on the airport runway. From the vertical profile, it is shown that the triggering of convective weather is accompanied by low-level southwest winds and high-altitude northeastern winds. According to the statistics of wind speed and direction on clear and rainy days over 9 months, rainy days are usually caused by the invasion of cold air from Northeast China, resulting in airport windshear. In summary, the enhanced lidar can effectively identify and analyze windshear during rainy days, which is very useful for aviation safety, especially for takeoff and landing in all weather conditions. [ABSTRACT FROM AUTHOR]

Urban Air Mobility (UAM) is an emerging air traffic system designed for passengers and cargo in and around urban environments. Both the Federal Aviation Administration of the United States and the European Union Aviation Safety Agency endorse a phased development approach for UAM, commencing with manned aviation and subsequently transitioning to remotely piloted and autonomous operations. This article focuses on legal considerations related to aviation safety, with a specific focus on pilot licensing and crew fatigue management. An analysis of existing aviation law provisions suggests that the International Civil Aviation Organization can work with local authorities to create regulations governing both on-board and remote pilots involved in UAM operations. Safety standards in air law can apply mutatis mutandis to on-board pilots until specific regulations are developed. In the longer term, there shall be domestic laws on both on-board and remote UAM pilots. [ABSTRACT FROM AUTHOR]

As a critical aviation support sensor, radar altimeters face imminent challenges due to the interference caused by the frequency overlap with the developing 5G telecommunications networks. This paper addresses the critical challenge posed by 5G interference with radar altimeter signals, which is crucial for maintaining aviation safety. It introduces a novel machine learning (ML) framework to preserve altimeter accuracy in the presence of 5G signals. This framework first classifies signals into pure or interfered categories, then applies regression models to predict altitudes when interference is detected, effectively quantifying and mitigating the interference impact. Distinguished by using real 5G signals from a Norman, Oklahoma base station, the approach offers a realistic evaluation and demonstrates the ML framework’s effectiveness in real-world conditions. Using various advanced ML models, the methodology showcases how critical aviation instruments can be safeguarded against the challenges posed by emerging telecommunications technologies, ensuring air travel safety and efficiency.

Pilot operational behaviour directly affects aviation safety, and exploring pilot behaviour can help decode, model, and predict pilot behavioural decisions. Therefore, it is necessary to recognize pilots’ states and predict the behaviours responsible for state changes. The purpose of this study was to investigate the relationship between pilot operating behaviour and brain activity. Using a flight simulator (DA42) and noninvasive functional near-infrared spectroscopy (fNIRS), changes in physiological signals were measured in 25 pilot cadets during different turning tasks. Twenty-five pilot cadets completed left and right turning tasks in a flight simulator, and the changes in blood oxygen concentration during different turning behaviours were analysed and modelled using machine learning models. Feature samples with significant differences ( $\text{p}< 0.05$ ) were extracted from the changes in oxyhaemoglobin concentration and selected for input into the SVM-linear classification model, the best results were achieved for the recognition of the pilot’s turning behaviour. After stratification based on feature importance, the SVM-RBF classification model obtained the highest accuracy (92.6%) after training with inputs containing a subset of features (60%) with the highest importance. In addition, the different turning behaviours of the pilots were closely related to activity in Brodmann area 17 (BA 17), BA 18 and BA 46. These findings provide insight into the neuroscientific mechanisms of pilot behaviour and provide an important physiological reference for improving pilot training.

In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.

General aviation has been developed as one of the main methods of transportation in small rural aerodromes in Indonesia. Surrounded by mountainous terrain and limited equipment for flight operation, flying unpressurised aircraft with many restrictions is quite challenging and may have a high risk of accidents. The number of incidents involving general aviation has not decreased, despite the numerous safety recommendations that have been generated as a direct result of accident reports. This research aims to identify the factors affecting the effectiveness of safety recommendations related to accidents and serious incidents of general aviation in Indonesia. Ten participants from the investigation agency, safety authority, and airline operators participated in semi-structured interviews. The interviews were transcribed and analyzed using the Grounded Theory approach. The findings revealed that three main considerations used in formulating safety recommendations following an investigation in Indonesia; to improve safety, to ensure comprehension, and to be accepted and implemented by addressees. In addition, in accepting and implementing safety recommendations, recipients consider organizational internal processes, decent communication towards addressees, enforcement and penalties, investigation processes and results, and the characteristics of the safety recommendation itself. It is suggested that installation of a monitoring and follow-up system to analyze the measurability and applicability of proposed actions.

There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information.

In order to analyze the safety risks of aircraft windshield crack / shatter events, 1 629 windshield crack / shatter events were collected, and the characteristics of the sample events involving conditions, flight phase, failure causes and other characteristics were statistically analyzed. The possible causes, potential consequences, prevention, mitigation and control measures of windshield crack / shatter events were determined, and the Bow-tie model of windshield crack / shatter events was drawn. The results show that the main conditions of windshield damage include crack, shatter, smoke, ice, scratch, etc. The main faults of windshield components are aircraft heating system and windshield wiper systems. The flight phase is mainly cruise and climb. The main causes of windshield crack / shatter events are bird strikes, parts missing / failure, system failure, ice strikes, and finally put forward targeted preventive measures, including maintenance, crew and other relevant personnel should do pre-flight and post-flight inspections; Focus on the types of high incidence incidents such as missing / damaged / worn parts, system failures, bird strikes, etc, and improve the response ability of the crew to such emergencies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Health-related factors can impact aviation safety. This study investigated the published, historical aviation accidents that have been investigated by the U.S. National Transportation Safety Board (NTS B) to understand medical conditions and medication use that have been determined to be causal to mishaps. METHODS: A two-part approach was adopted for this study: 1) a scoping review was conducted to provide an overview of the current medical literature addressing medical factors and flight safety; and 2) a comprehensive review of aviation accident reports from the NTS B database from January 2013 to October 2022. RESULTS: The literature review demonstrated that psychiatric, cardiovascular, and neurological medications were the major categories of medications reported in fatal aircraft accidents. Age was not found to be a risk factor. Review of the NTS B database demonstrated that, among 15,654 mishaps, medical factors were associated in 703 (4.5%) accidents. NTS B data showed that cardiovascular (1.3%), psychiatric (0.23%), and endocrine diseases (0.17%) were the most commonly reported diseases among pilots. In accident reports, cardiovascular medications (1.49%), sedating antihistamines (0.91%), and alcohol (0.70%) were the most commonly used medications at the time of the accidents. DISCUSSION: H ealth-related factors were a small yet likely underestimated proportion of the factors associated with accidents. Cardiovascular diseases and treatment were the most important factors. It is important to continue to investigate the association between health-related factors and the commission of mishaps. [ABSTRACT FROM AUTHOR]

In the area of aviation safety, the importance of human factors is indisputable. This research endeavors to assess the importance of human factors in predicting fatalities during aviation mishaps. Utilizing reports from the Aviation Safety Network Database, encompassing 1105 accidents and incidents spanning from 2007 to 2016, neural networks were trained to forecast the probability of fatalities. Our findings underscore that the human factors involved, by themselves, can yield strong predictions. As a term of comparison, other variables (type of occurrence, flight phase, and aircraft fate) were used as predictors, with poorer results; by combining these variables with human factors, the prediction is only marginally better, if at all, than that based on human factors alone. So, although these supplementary variables can marginally benefit the predictive results derived from human factors, their contribution remains minimal. Consequently, this study illuminates the paramount importance of human factors in influencing aviation fatalities, guiding stakeholders on the immediate interventions and investments which are most warranted to prevent them. [ABSTRACT FROM AUTHOR]

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews' correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways. [ABSTRACT FROM AUTHOR]

Husein Sastranegara Bandung International Airport was previously an airport called Andir (Andir Airfield) during the Dutch East Indies era in 1920. As time progressed, Husein Sastranegara Bandung International Airport was managed by PT. Angkasa Pura II in 1994. Even though it has been processed by PT. Angkasa Pura II and many improvements have been made, but there are still facilities that are not available in accordance with Minister of Transportation Regulation Number KM 21 of 2005 concerning the Implementation of Indonesian National Standards (SNI) 03-7095-2005 concerning Markings and Signs in Aircraft Movement Areas at Airports as a Mandatory Standard, namely Service Road markings. The aim of this research is to provide an overview of the actualization (actual condition) of the Service Road in order to fulfill the Safety Management System at Husein Sastranegara Bandung International Airport. The research method used is qualitative, the informant validation tool uses NVivo Softwear, and data validation uses triangulation, and the tool to answer research questions uses fishbone to answer "why (epistemology)" and triangulation to answer "how (axiology)". The results of this research are that the Service Road is a marking on the apron which is mandatory due to ICAO (International Civil Aviation Organization) regulations. The Service Road at Husein Sastranegara Bandung International Airport has not yet been built so the Safety Management System is still not fulfilled, but the management of PT. Angkasa Pura II has created a Service Road design plan in accordance with KP 777 of 2016 concerning the Master Plan for Husein Sastranegara International Airport in Bandung City, West Java Province. The research recommends that Service Roads be immediately built and created in order to fulfill aviation safety according to the Safety Management System. [ABSTRACT FROM AUTHOR]

Safety is the lifeline of civil aviation. With the advancement of technology, human factors have become the primary factor affecting aviation safety. Among these, human errors in air traffic control (ATC) account for a significant proportion of aviation accidents. In order to investigate human errors in ATC, this paper introduces two common human factors conceptual models – the Reason model and the HFACS model, and analyzes the specific application of the HFACS model in aviation. It is found that the HFACS model can effectively establish a classification system for human errors in ATC. Moreover, this paper combines the HFACS model with AHP (Analytic Hierarchy Process) and uses the HFACS model to establish a first and second level indicator system for human errors in ATC. We calculated the weights of each specific factor using the Analytic Hierarchy Process. The results show that the most significant influencing factor in the first level indicators is organizational factors, while the most significant influencing factor in the second level indicators is Air Traffic Resource Management. Among the 13 second level indicators, the smallest weight is the controller's violation. Based on the calculated weight results, in order to improve the safety level of ATC, the first step should be to improve the level of ATC organizational management. This is also of great significance for improving the safety level of the entire aviation system. The findings of this study suggest that the HFACS model, combined with AHP, can be an effective tool for identifying and analyzing human factors in ATC and ultimately improving the safety of the aviation industry. [ABSTRACT FROM AUTHOR]

Safety is one of the most important issues in aviation. Aviation regulations provide significant information pertinent to the safety design and operation of aircraft; however, this information has not been effectively used. It is difficult to precisely identify and obtain the necessary information due to massive and unstructured provisions. In this study, a hybrid methodology is proposed to realize knowledge system construction using Chinese Civil Aviation Regulations as the object of study. To realize structured knowledge organization and intelligent knowledge application for aviation regulations, the hybrid methodology integrates a semantic cohesion model, a knowledge recognition model, a knowledge organization model, and a knowledge application model. A knowledge system of aviation regulations is built using the hybrid methodology comprising all knowledge necessary for aviation safety. The system provides intelligent knowledge support for the safety analysis of aircraft from the perspective of control system, including the accurate positioning of control elements and thorough acquisition of control conditions. Experiments were conducted to confirm the accuracy of the proposed method, and the 56,853 knowledge triples contained in the knowledge system supported its reliability. A few examples of knowledge retrieval are provided, focusing on the interaction processes of socio-technical elements during aircraft missions. It takes only a few seconds to acquire the knowledge required for safety analysis. The examples show how the hybrid methodology and knowledge system can be utilized to increase the efficiency of safety analysis for socio-technical systems while advancing intelligent knowledge applications in the aviation domain. [ABSTRACT FROM AUTHOR]

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.

Modern aviation security systems are largely tied to the work of screening operators. Due to physical characteristics, they are prone to problems such as fatigue, loss of attention, etc. There are methods for recognizing such objects, but they face such difficulties as the specific structure of luggage X-ray images. Furthermore, such systems require significant computational resources when increasing the size of models. Overcoming the first and second disadvantage can largely lie in the hardware plane. It needs new introscopes and registration techniques, as well as more powerful computing devices. However, for processing, it is more preferable to improve quality without increasing the computational power requirements of the recognition system. This can be achieved on traditional neural network architectures, but with the more complex training process. A new training approach is proposed in this study. New ways of baggage X-ray image augmentation and advanced approaches to training convolutional neural networks and vision transformer networks are proposed. It is shown that the use of ArcFace loss function for the task of the items binary classification into forbidden and allowed classes provides a gain of about 3–5% for different architectures. At the same time, the use of softmax activation function with temperature allows one to obtain more flexible estimates of the probability of belonging, which, when the threshold is set, allows one to significantly increase the accuracy of recognition of forbidden items, and when it is reduced, provides high recall of recognition. The developed augmentations based on doubly stochastic image models allow one to increase the recall of recognizing dangerous items by 1–2%. On the basis of the developed classifier, the YOLO detector was modified and the mAP gain of 0.72% was obtained. Thus, the research results are matched to the goal of increasing efficiency in X-ray baggage image processing.