Cardiovascular screening and long-term outcomes in aircraft pilots

Heart, Journal Year: 2025, Volume and Issue: unknown, P. heartjnl - 325243

Published: March 4, 2025

Pilots face significant occupational risks affecting cardiometabolic health and are subject to regulatory screenings. Cardiometabolic risk factors, cardiac screening findings outcomes among pilots have not been well reported. This study aimed investigate evaluations of asymptomatic aircraft the association between clinical factors outcomes. Asymptomatic referred for assessment January 1991 May 2023 were studied. Baseline characteristics, test evaluated. Major adverse event (MACE) was defined as death, myocardial infarction, stroke, major arrhythmia, heart failure or cardiac-related hospitalisation estimated using Kaplan-Meier methods. Significant valvular disease by echocardiography stenosis, regurgitation prolapse moderate severity greater. Aortic dilation transthoracic echocardiogram (TTE) measuring ≥40 mm in diameter. 212 met eligibility criteria study. The majority white (92.9%) male (91%) with a mean age 58.5±10.9 years. Mean body mass index 27.8±4.8 comorbid hyperlipidaemia (48%), hypertension (32%), prior cancer (27%), sleep apnoea (15%), arrhythmia (12%) known coronary artery (6%). Imaging revealed (2.4%) dilated aortas (16%) based on TTEs. Functional testing performed showed functional aerobic capacity 109±24.6% reaching 11.89±2.65 metabolic equivalents <8% showing positive per EKG wall motion abnormalities exercise TTE. Six patients received angiography evaluation, two undergoing percutaneous intervention. Over 32-year period median (range) follow-up 5.15 (0.1, 31.82) years, MACE incidence 15%. underlying cardiovascular but good overall capacity, long-term life expectancy. Prevalence structural like aortic dilatation warrants increased attention during examination these patients.

Language: Английский

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Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems

Future Internet, Journal Year: 2024, Volume and Issue: 16(8), P. 276 - 276

Published: Aug. 2, 2024

The integration of artificial intelligence things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies reducing costs. Utilizing three-tiered cloud architecture, the AIoT system enables real-time acquisition from sensors embedded in aircraft systems, followed machine learning algorithms to analyze interpret for proactive decision-making. research examines evolution traditional AIoT-enhanced monitoring, presenting comprehensive architecture integrated satellite communication 6G technology. mathematical models quantifying benefits increased diagnostic depth through AIoT, covering aspects such as cost savings, improvements are introduced this paper. findings emphasize strategic importance investing technologies balance cost, safety, efficiency operations, marking paradigm shift management aviation.

Language: Английский

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Use of Artificial Intelligence in Design, Development, Additive Manufacturing, and Certification of Multifunctional Composites for Aircraft, Drones, and Spacecraft

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(3), P. 1187 - 1187

Published: Jan. 31, 2024

Multifunctional composites provide more than one function from the same part. The anisotropy, material, and process characterization challenges lack of standardization on 3D-printed multifunctional carbon make it difficult for application into aerospace. current solutions additive manufacturing (AM) technologies additively manufactured monofunctional are not mature enough safety-critical applications. A new approach is proposed to explore use machine learning (ML) in design, development, AM, testing, certification aircraft, unmanned aircraft systems (UAS), spacecraft. In this work, an artificial neural network (ANN) architecture proposed. An AM-embedded building block integrates complete lifecycle UAS, spacecraft using ANN support continued operational safety (COS) spacecraft, UAS. method exploits power metadata material properties processes mapping failure modes compared with predicted models history. This paper provides in-depth analysis explanation methods needed overcome existing barriers, problems, situations.

Language: Английский

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Development of a machine learning model for predicting abnormalities of commercial airplanes

Data Science and Management, Journal Year: 2024, Volume and Issue: 7(3), P. 256 - 265

Published: March 7, 2024

Airplanes are a social necessity for movement of humans, goods, and other. They generally safe modes transportation; however, incidents accidents occassionally occur. To prevent aviation accidents, it is necessary to develop machine-learning model detect predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection labeling, prediction-model development, deployment, testing. labeling process was based on rules framed by international civil organization commercial, jet-engine validated expert pilots. results showed that best prediction model, quadratic-discriminant-analysis, 93% accurate, indicating "good fit." Moreover, model's area-under-the-curve abnormal normal detection were 0.97 0.96, respectively, thus confirming its

Language: Английский

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Transforming Aviation

Advances in mechatronics and mechanical engineering (AMME) book series, Journal Year: 2024, Volume and Issue: unknown, P. 60 - 75

Published: May 17, 2024

This chapter explores the transformative impact of artificial intelligence (AI) integration in air traffic management, offering insights into paradigm shift witnessed aviation practices. The study delves dynamic landscape aviation, focusing on pivotal role AI enhancing safety, efficiency, and overall operations within management systems. Notably, results stemming from implementations showcase promising quantitative outcomes. These include a 15% reduction average flight delays substantial 20% increase airspace capacity utilization following introduction AI-driven flow management. Moreover, remarkable 30% decrease reported near-misses 25% accidents reflect tangible improvements safety measures derived technologies. AI-enabled route optimization strategies demonstrated 12% fuel consumption 10% duration for long-haul flights, while yielding $100 million annual cost savings industry.

Language: Английский

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A Framework for the Characterization of Aviation Construction Projects: The Case of UAE

Buildings, Journal Year: 2024, Volume and Issue: 14(8), P. 2384 - 2384

Published: Aug. 1, 2024

This article contributes to the existing literature by modeling and automating learning process from previous aviation construction projects (ACPs) using artificial intelligence tools, where it will be easier characterize identify specifications of different aspects throughout their entire life cycle. An (AI) framework is proposed for categorization machine-learning (ML) methods with a focus on UAE as source data. Airport have been seen share good deal similar attributes, which should simplify decision-making regarding layouts, design, equipment, labor, budget, complexity, etc. However, gap in reality that huge scattered sources data, project specifications, characteristics, knowledge past are not utilized an automated way could navigation through better future decision-making. The utilization AI/ML tools expected useful here order reduce revisions design rework classifying elements managers need consider. planning, new can improved identifying attributes categorizing them according similarities, differences, complexities. Specifically speaking, hierarchical clustering neural networks integrated together form classification model. Upon implementing networks, was found demonstrate remarkable results; error minimal most cases. advantage such help decision-makers utilize best practice groups projects, were classified both models. With this classification, minimized, overhead costs may reduced, practices utilized.

Language: Английский

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Capacity Building for Student Teachers in Learning, Teaching Artificial Intelligence for Quality of Education

Societies, Journal Year: 2024, Volume and Issue: 14(8), P. 148 - 148

Published: Aug. 10, 2024

The future of education relies on the integration information technologies, emphasizing importance equity and inclusiveness for quality education. Teacher programs are essential fostering qualified educators future. Integrating AI in is crucial to ensure inclusivity comprehensive services all. This study aims evaluate student teachers’ perceptions using learning teaching, provide suggestions enhancing sustainable through technologies. A qualitative research design was adopted gather experiences from 240 teachers who participated a seminar usage completed self-reflection tasks. These teachers, enrolled various teaching methods principal courses, contributed thematic analysis. reveals that should be carefully planned incorporated into lesson plans enhance personalized learning. Student reported supports motivates process, effectively transforming students’ needs experiences. However, they also noted potential drawbacks, such as imposing restrictions profession, replacing producing biased results. suggests capacity-building strategies enriched across different courses raise awareness about AI’s applications.

Language: Английский

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Next Frontiers of Aviation Safety: System-of-Systems Safety

Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Airline revenue management, distribution and passengers: market trends in a technology driven triangle

Worldwide Hospitality and Tourism Themes, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Purpose The aim of this paper is a first attempt to map and describe airline information systems (IS) market, in the context latest trends affecting air services distribution, revenue management (RM) passengers industry. Design/methodology/approach RM evolution has greatly affected revenues load factors global environment characterized by fierce competition. distribution shifted gradually from model dependent on travel agents other intermediaries toward new trend do-it-yourself (DIY) bookings through Internet. resulting significantly more complex mandates connection between (RMS) reservation (RS) third parties (aggregators) that have emerged are ready perform task. In context, market review been carried out, following approach structured literature illustrate ecosystem. Findings Data collected vendors depict size capabilities, highlighting for IS industry based relational triangle dependence competition includes RS, RMS aggregators. Originality/value Mapping both technological functional backgrounds operation current their linking with methods deemed necessity identifying describing mechanisms defining B2B relationship subsequent need improve ways schemes combined applications addressing changing needs, reflecting behavioral models customers.

Language: Английский

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In-situ piezoelectric sensors for structural health monitoring with machine learning integration

Mechatronics, Journal Year: 2025, Volume and Issue: 106, P. 103297 - 103297

Published: Feb. 5, 2025

Language: Английский

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