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With the increasingly severe airspace congestion problem, the aviation industry is facing huge challenges, including the surge in pressure on air traffic controllers, significant increase in flight delays, and frequent flight conflicts. Efficient conflict detection and resolution technology is the primary task to ensure flight safety, which is not only particularly important for complex and high-density airspace environments, but also has significance for maintaining flight order, preventing aircraft collisions, alleviating air traffic pressure, and ensuring air traffic safety. This paper proposes a flight separation assurance model based on reinforcement learning (RL) technology, aiming to address the shortcomings of existing conflict resolution models in terms of state and action dimensions that cannot meet the needs of real-world control scenarios. Based on the air traffic environment model, agents are allowed to learn separation assurance policies through simulation interaction. Simulation experiments show that the model can search for optimal policies, which can provide assistance for air traffic controllers in decision-making. [ABSTRACT FROM AUTHOR]

As air traffic intensity increases and stochastic uncertainties, such as wind direction and speed, continue to impact air traffic controllers' workload significantly, airlines are increasingly pressured to reduce costs by flying via straighter/more direct trajectories. Due to these changes, it is important to search for new means/solutions for aircraft conflict resolution to ensure the required level of safety and rational flight trajectory. Such a solution could be the implementation of Dubin's method of flight trajectories. This paper aims to propose and deeply analyze a new mathematical model for two-aircraft conflict resolution where the Dubins method is applied in a dynamic conflict scenario. In this model, at a certain moment, the flight trajectory of one aircraft follows a path similar to a moving circle's tangential line. Upon that, the conflict detection and resolution (CDR) model considers wind uncertainty. The proposed CDR method could be applied when uncertainty such as wind direction and speed are inconstant (stochastic) throughout the simulation. [ABSTRACT FROM AUTHOR]

Traditional approaches to safety assessment and management are limited when it comes to complex socio-technical systems, such as air traffic management (ATM). A modelling approach proposed in this paper relies on Resilience Engineering perspective, which is about work-as-done, where safety is one of the targets trading off with other goals. The model provides a description of everyday practices, procedures, strategies, etc. in providing, not only safe, but also efficient ATM system operations. The aim of the model is to help air navigation service providers (ANSPs) that still do not measure and monitor resilience to distinguish between main drivers of traditional safety and drivers of resilience emerging from everyday adaptation to uncertainties. The conceptual layer of the model uses the analogy of lever mechanism to represent the balancing between the demand side (including the uncertainties) and available resources on the supply side that considers air traffic controllers (ATCos), tools and working procedures. The quantitative layer of the model allows identification of periods of resilient behavior (negative demand-capacity imbalances) and thereby steers ANSPs in discovering sources of resilience and finding appropriate resilience indicators to monitor in the future. A data-driven application of the proposed model is shown with the example of Barcelona Area Control Centre – assessment of balancing conditions, analysis of proposed resilience indicators and discussion of the trade-offs. This research focuses on the tactical imbalances that emerge from work-as-done on an everyday basis. [ABSTRACT FROM AUTHOR]

The assessment of the cognitive workload experienced by air traffic controllers is a complex and prominent issue in the research community. This study introduces new indicators related to gamma waves to detect controllers' workload and develops experimental protocols to capture their EEG data and NASA-TXL data. Then, statistical tests, including the Shapiro–Wilk test and ANOVA, were used to verify whether there was a significant difference between the workload data of the controllers in different scenarios. Furthermore, the Support Vector Machine (SVM) classifier was employed to assess the detection accuracy of these indicators across four categorizations. According to the outcomes, hypotheses suggesting a strong correlation between gamma waves and an air traffic controller's workload were put forward and subsequently verified; meanwhile, compared with traditional indicators, the indicators associated with gamma waves proposed in this paper have higher accuracy. In addition, to explore the applicability of the indicator, sensitive channels were selected based on the mRMR algorithm for the indicator with the highest accuracy, β + θ + α + γ, showcasing a recognition rate of a single channel exceeding 95% of the full channel, which meets the requirements of convenience and accuracy in practical applications. In conclusion, this study demonstrates that utilizing EEG gamma wave-associated indicators can offer valuable insights into analyzing workload levels among air traffic controllers. [ABSTRACT FROM AUTHOR]

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process. [ABSTRACT FROM AUTHOR]

Air Traffic Controllers' (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky. [ABSTRACT FROM AUTHOR]

Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory's relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high‐value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method's superiority in predicting flight trajectory affected by convective weather. [ABSTRACT FROM AUTHOR]

Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability. [ABSTRACT FROM AUTHOR]

The European Union is committed to modernising and improving air traffic management systems to promote environmentally friendly air transport. However, the safety-critical nature of ATM systems requires rigorous user testing, which is hampered by the scarcity and high cost of air traffic controllers. In this article, we address this problem with a novel approach that involves non-experts in the evaluation of expert software in an A/B test setup. Using a transformation model that incorporates auxiliary information from a newly developed psychological questionnaire, we predict the performance of air traffic controllers with high accuracy based on the performance of students. The transformation model uses multiple linear regression and auxiliary information corrections. This study demonstrates the feasibility of using non-experts to test expert software, overcoming testing challenges and supporting user-centred design principles. [ABSTRACT FROM AUTHOR]

Fatigue in air traffic management (ATM) is a well-recognized safety concern. International organizations like ICAO and EASA have responded by advocating for fatigue risk management systems (FRMSs). EU Regulation 2017/373, implemented in January 2020, mandates specific requirements for air traffic service providers (ANSPs) regarding controller fatigue, stress, and rostering practices. These regulations are part of broader safety management protocols. Despite ongoing efforts to raise awareness about fatigue in ATC, standardized operational requirements remain elusive. To address this gap, Eurocontrol recently published "Guidelines on fatigue management in ATC rostering systems" (23 April 2024). This initiative aims to facilitate the adoption of common fatigue management standards across operations. However, neither EU Regulation 2017/373 nor existing documentation provides definitive rostering criteria. ANSPs typically derive these criteria from a combination of scientific research, best practices, historical data, and legal and operational constraints. Assessing and monitoring fatigue in the real-world ATC setting is complex. The multifaceted nature of fatigue makes it difficult to study, as it is influenced by many factors including sleep quality, circadian rhythms, psychosocial stressors, individual differences, and environmental conditions. Long-term studies are often required to fully understand these complex interactions. This paper presents two case studies that attempt to create an evidence-based protocol for fatigue risk monitoring in ATC operations. These studies utilize a non-invasive approach and collect multidimensional data. The cases involved en-route and tower (TWR) controllers from different ATC centers. The results highlight the importance of fatigue assessment in ATC and shed light on the challenges of implementing fatigue monitoring systems within operational environments. [ABSTRACT FROM AUTHOR]

The aim of this research is to determine the magnitude of the influence of Work Discipline, Workload and Work Environment, both partially and simultaneously, on the performance of air traffic controller employees. The method used is associative with a quantitative approach. The number of samples used was 132 respondent, with a simple random sampling technique. Data was taken using a survey method. Data analysis was carried out to test the significance of the hypothesis, coefficient of determination using SPSS V.25 software. The research results show that work discipline, and work environment have a positive and significant effect, both partially. Workload has a negative and significant effect on the performance of air traffic controller employees. As simultaneously, Work discipline, workload and work environment have a positive and significant influence employee work performance in the Area Control Center Unit at Airnav Indonesia Jakarta Air Traffic Services Center Branch. [ABSTRACT FROM AUTHOR]

Objectives: The effectiveness of progressive muscle relaxation (PMR) in assessing the general health of air traffic controllers (ATC) is still insufficient, specifically when examining the psychological conditions of workers who use questionnaire instruments. Therefore, this research aimed to evaluate the use of PMR program in ATC by developing a model using biomarkers (saliva) tested on the cocorometer stress diagnostic tool and validity, including observing related determinants in the work environment. Methods: A quasi-experimental method was used, focusing on one group pre-post-test design for 92 respondents across six research areas in Indonesia. All respondents had received training in PMR methods conducted by psychologists. Subsequently, ongoing assistance was provided in implementing relaxation by a trained reminder team for eight weeks to maintain the precision and effectiveness of the intervention. Further analysis was conducted using the Wilcoxon signed rank test to evaluate the success of the intervention. Results: The implementation of PMR program in ATC reduced the incidence of stress levels after the observation. Statistically, the feeling of fatigue was a significant variable that decreased in mean value after the implementation of the relaxation program on the data review. The main benefit of PMR program in improving psychological health conditions (stress) was found in Surabaya branch ATC. Conclusions: The relaxation program was proven to reduce stress levels in ATC, showing an improvement in conditions before and after the implementation of PMR. [ABSTRACT FROM AUTHOR]

A "detect-and-avoid" capability providing a "remain well-clear" function will be needed for unmanned aircraft to fly safely in uncontrolled airspace. It could also be used in controlled airspace provided that the action chosen by the pilot, based on the system suggestive guidance, is compatible with the current air traffic control clearance or an amended clearance. The study reported in this paper looks at the potential operational consequences of the interaction between a pilot and the air traffic controller in controlled airspace when a clearance amendment must be requested by the pilot using the recently standardized detect-and-avoid system, Airborne Collision Avoidance Systems Xu (ACAS Xu). Numerical simulations on a potential encounter are performed. A model of pilot behavior when faced with a remain well-clear alert (including pilot-controller communication delays) is used. An analysis of the qualitative results outlines several areas of concern: the possible confusion caused by multiple changes in remain well-clear guidance, the undesirable effects when both aircraft are equipped with ACAS Xu, and the potential interaction with short-term conflict alerts displayed at the air traffic controller's workstation. [ABSTRACT FROM AUTHOR]

This paper presents a machine learning-based approach for predicting the taxi-out time, with the departure process decomposed into two components: the time taken to travel from the gate to the departure queue, and the time spent in the departure queue. Gradient-Boosted Decision Tree (GBDT) models are trained to predict the two components using different feature sets, and a comparison of both model shows that they can provide better prediction accuracy compared with conventional methods, with a Root Mean Squared Error (RMSE) of 1.79 minutes and 0.92 minutes when predicting the taxiing and queuing times respectively, and 78% and 96% of predictions falling within a ±2 minute error margin. Predictions from the GBDT model are analysed and interpreted using SHAP (SHapley Additive exPlanations) values, a well- recognised technique for providing interpretability to many different black-box models, and allowing feature importance to be evaluated at global (model) and local (individual prediction) levels. In particular, the most important feature groups for the taxiing and queuing models are respectively the route features and runway queuing features. The model explain- ability provides a pathway towards the certification of machine learning techniques in Air Traffic Controller (ATCO) decision support tools. [ABSTRACT FROM AUTHOR]

Featured Application: Application of main conclusions when developing a complexity indicator for giving information about air traffic. Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines. [ABSTRACT FROM AUTHOR]

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. [ABSTRACT FROM AUTHOR]

This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed which assumes that terminal complexity could be determined based on approach air traffic controller (ATCO) tasks. The comprehensive list of general approach ATCO tasks was defined using a literature review and observation of training exercises, forming the basis for subsequent expert group workshops which enabled the acquisition of ATCOs' knowledge data. Through these workshops, new approach ATCO tasks were additionally identified, and terminal complexity indicators were defined with airspace and traffic parameters. These new tasks and indicators present a novelty in this field of research since they incorporate ATCOs' knowledge as the data input and consider various traffic scenarios, all types of traffic, weather conditions, and off-nominal situations. [ABSTRACT FROM AUTHOR]

This article emphasizes the significance of non-technical skills in the civil aviation industry. It presents a comprehensive framework of 15 non-technical skills that are crucial for superior performance in the sector, including communication, leadership, problem-solving, and teamwork. The document discusses the importance of these skills for air traffic controllers, aircraft maintenance professionals, and cabin crew members, and highlights the regulations governing their training. It also addresses the challenges faced by human resource managers in assessing the impact of non-technical skills training and suggests strategies for overcoming these challenges. The article calls for further research to validate the model for each profession and develop a competency reference and questionnaire. [Extracted from the article]

The aim of the study was to examine air traffic controllers’ perspectives on competency, performance, and training. Analysing data from 182 participants, the results revealed a strong focus on competency with the controllers actively evaluating their skills. Notably, the experienced controllers (19+ years) displayed a heightened commitment to continuous learning, reflected in their higher participation in individual training and perception of its value for their career goals and adaptation to the evolving aviation industry. Competency-based training programs also received positive feedback across experience levels, highlighting their potential effectiveness. Moreover, training was universally perceived as key to performance throughout careers. These results highlighted the importance of fostering a culture of continuous learning within air traffic control (ATC) communities. Aviation authorities can support this by providing diverse training opportunities, including individual and competency-based programs. Future research should continue investigating the impact of technological advancements and AI/machine learning on ATC competencies, the effectiveness of competency-based training programs, and the role of individual training in competency development, ultimately ensuring the vital role of air traffic controllers in aviation safety. [ABSTRACT FROM AUTHOR]

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution, and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of air traffic controllers (ATCOs). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for the tow truck battery state of charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimise taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as the tow truck battery state of charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts. [ABSTRACT FROM AUTHOR]

Air traffic controllers (ATCOs) play a substantial part in securing the safety of flights, such that a compromise of the ATCOs' performance may lead to tragedies. Given the mental workload that comes with the nature of ATCOs' work, this study intends to investigate the impact of mental workload on ATCOs' job performance and identify conditional factors that could mitigate the mental workload–ATCOs' job performance relationship. Underpinned by the job demands–resources theory, a framework was developed to investigate the impact of job demands (mental workload) on job performance and whether personal resources (mindfulness and social work support) weaken the relationship. A total of 324 ATCOs across Saudi Arabia responded to the questionnaire. There were three notable findings. First, mental workload was indeed found to bring detrimental effects to ATCOs' job performance. Second, mindfulness played a mitigating role, where more mindful ATCOs demonstrated less workload effect on performance. Thirdly, social work support also played a mitigating role, where ATCOs who perceived receiving greater job support experienced less detrimental impact from the workload on performance. This study verified the negative linkages between mental workload and job performance and identified the boundary conditions (mindfulness and social work support) that weaken the relationship. The limitations and future research directions are then discussed. [ABSTRACT FROM AUTHOR]

The article discusses the challenges and considerations surrounding retirement for surgeons, emphasizing the importance of planning and mental preparedness for a fulfilling post-career life. It also explores various retirement strategies, including financial planning, timing considerations, and the psychological aspects of transitioning away from clinical practice.

Air Traffic Controller (ATC) is one of the occupations in the service sector. ATC, like other jobs, has other social tasks outside of work that require the ability to balance work and family or personal and social responsibilities (i.e., work-life balance). This research aims to test the role of emotional regulation in mediating the relationship between mindfulness and work-life balance among ATC officers. This research was conducted quantitatively. The data was collected through the work-life balance, emotion regulation, and mindfulness scales and was analyzed using structural equation modeling (SEM) operated through the JASP program version 16. 172 employees (59% males) involved in this research. Results showed that emotional regulation significantly mediates the relationship between mindfulness and work-life balance. This indicates that ATC officers with a mindful condition will consciously be able to direct their attention to focus on the current situation and role. This will affect ATC's ability to manage cognitively to provide emotional responses and express emotions according to the needs of the current situation in completing the role at hand and achieving work-life balance. This finding has implications on how management might assist ATC officers in improving work-life balance by enhancing emotional regulation skills and mindfulness. [ABSTRACT FROM AUTHOR]

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic controllers can impair their work ability and even threaten flight safety, which makes it necessary to carry out research into how to optimally detect fatigue in controllers. Compared with single-modality fatigue detection methods, multi-modal detection methods can fully utilize the complementarity between diverse types of information. Considering the negative impacts of contact-based fatigue detection methods on the work performed by air traffic controllers, this paper proposes a novel AF dual-stream convolutional neural network (CNN) architecture that simultaneously extracts controller radio telephony fatigue features and facial fatigue features and performs two-class feature-fusion discrimination. This study designed two independent convolutional processes for facial images and radio telephony data and performed feature-level fusion of the extracted radio telephony and facial image features in the fully connected layer, with the fused features transmitted to the classifier for fatigue state discrimination. The experimental results show that the detection accuracy of radio telephony features under a single modality was 62.88%, the detection accuracy of facial images was 96.0%, and the detection accuracy of the proposed AF dual-stream CNN network architecture reached 98.03% and also converged faster. In summary, a dual-stream network architecture based on facial data and radio telephony data is proposed for fatigue detection that is faster and more accurate than the other methods assessed in this study. [ABSTRACT FROM AUTHOR]

The rapid development of instant lifestyles has significantly impacted the air transportation system. The air transportation system involves many parties, for example, facilities, infrastructure, and human resources. One of the parties that plays important role in human resources is the Air Traffic Controller (ATC). ATC playing a crucial role in coordinating air traffic and providing assistance to pilots. By seeing the importance of an ATC's duties, particularly in communicating with pilots, which requires them to use English as an international language in the world of flight, this research sought to identify the factors influencing the English language proficiency of ATC Officers at Sultan Hasanuddin Makassar International Airport. The research used interviews, documentation, and data analysis methods to gather and analyze data. The research's findings demonstrated that factors influencing the English language proficiency of ATC Officers at Sultan Hasanuddin Makassar International Airport are: a. Writing skills: 1) interest factors and 2) environmental factors. b. Listening skill: 1) interest factor. 2) vocabulary factor; 3) concentration factor; and 4) interest in the topic factor. c. Speaking Skill: 1) psychological factors and 2) grammatical factors. d. Reading Skill: 1) motivational factors. 2) language complexity factor; and 3) vocabulary factor. [ABSTRACT FROM AUTHOR]

To determine the effectiveness of vowel centered accent modification (VCAM) on percent vowels correct (PVC) in Mandarin-speaking Chinese pilot candidates at the word, phrase, and semi-structured speech levels to increase the likelihood of pilot candidates passing the International Civil Aviation Organization's (ICAO) Level 4 English Proficiency Requirement. This was a single-subject changing criterion design with six participants. Three target vowels and one control vowel were selected for each participant. Mono and bi-syllabic target words were selected from aviation textbooks recommended by the flight academy. Visual feedback and placement cues were provided to assist with each participant's vowel productions. The Pierson Aviation English Questionnaire (PAEQ), a pre- and post-measure, was also administered. All participants demonstrated gains in PVC at the three levels of difficulty. Participants reported decreased anxiety levels when speaking with native speakers, air traffic controllers, and flight instructors. All pilot candidates reported increased self-confidence levels when using aviation phraseology and an increase in enjoyment when speaking English. This investigation was the first of its kind to evaluate the effectiveness of vowel-centered accent modification in pilot candidates. It was also the first investigation to demonstrate the usefulness of skilled speech-language pathology intervention and the positive effect on improving intelligibility of second-language English speakers in aviation. [ABSTRACT FROM AUTHOR]

The frequency of commercial space launch and re-entry operations is increasing worldwide. Current regulations and procedures ensure safe operations by temporarily closing large volumes of airspace where risks to aircraft would exist in case of non-nominal events during launch or re-entry operations. To maintain the safety of air traffic as the number of space operations rises, effectively providing the right information to the right stakeholders at the right time is key. Through a cooperative agreement, the FAA and DLR are sharing their unique capabilities using the Commercial Space Integration Lab and Air Traffic Validation Center, located in the USA and Germany respectively, to improve situational awareness through real-time data exchange. The project seeks to answer whether U.S. and European ANSPs can respond adequately to a non-nominal event during a launch or re-entry operation that presents a hazard to the airspace system. It leverages existing international data standards and infrastructures by using a data exchange approach based on System Wide Information Management (SWIM). Within the project an initial assessment of the processes, roles and responsibilities for implementing launch and re-entry events in the air traffic systems on both sides of the Atlantic, as well as the requirements for basic functions and performance parameters of a SWIM-based integration, have been completed. The project developed a demonstration model across all system levels as far down as to the ANSPs, namely to the air traffic controllers. Through a series of demonstrations, covering launch scenarios from the U.S. with possible effects on European airspace and vice versa, the project evaluated the technical and operational feasibility of the concept. The key data parameters identified during the analyses shall enable information sharing among various users within the U.S. and European global airspace system. It has been shown, that the systems on both sides of the Atlantic could be connected via standardized protocols und used successfully for exercises in different scenarios. Further research on the best integration of the processes in international Air Traffic Management (ATM) networks and domains will be the follow-on. • Cross-border data exchange improves situational awareness for rocket launches. • Importance of providing information to all stakeholders in the air and in space. • Demonstration between U.S. and European stakeholders is transferable other missions. • SWIM based launch/re-entry data exchange proved as feasible technical approach. [ABSTRACT FROM AUTHOR]

From 1970 to 2000, worker participation in strikes decreased by 90%. We show that strikers also experienced worse outcomes after 1981. Evidence from the Panel Study of Income Dynamics suggests that strikers enjoyed 5%–10% wage gains before the 1980s but null wage changes thereafter. Additional analyses of other survey data and collective bargaining agreements reinforce the finding that strikes since the 1980s have not been associated with increases in wages, hours, or benefits. We attribute these findings to structural labor market shifts and to a narrower deterioration in labor relations signaled by the 1981 air traffic controllers strike. [ABSTRACT FROM AUTHOR]

In the modern era of technology, the influence of artificial intelligence in various fields is becoming increasingly evident and profound, and aviation safety is one of the areas intensely experiencing this technological revolution. As air travel becomes more accessible and frequent, ensuring high safety standards is essential, making artificial intelligence a crucial element that is transforming the entire aviation industry. From advanced assistance systems for pilots and air traffic controllers to sophisticated algorithms that analyze data to prevent incidents, artificial intelligence has introduced a series of innovations that significantly enhance aviation safety. However, this shift also brings new challenges, from managing sensitive data to ensuring effective collaboration between humans and algorithms. This paper will explore the depth of AI's influence in the field of aviation safety, examining both its benefits and the challenges that must be overcome to achieve an optimal balance between technology and the human factor in this critical domain. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of weather conditions on flight performance and aviation communication. By analyzing the complex relationship between meteorological factors and aviation operations, the importance of understanding and properly managing aviation weather is highlighted. In addition, it examines how communications between pilots and air traffic controllers are influenced by weather conditions and how they can affect flight safety and efficiency. [ABSTRACT FROM AUTHOR]