Flightdeck Automation Problems (FLAP) Model for Safety Technology Portfolio Assessment

NASA's Aviation Safety Program (AvSP) develops and advances methodologies and technologies to improve air transportation safety. The Safety Analysis and Integration Team (SAIT) conducts a safety technology portfolio assessment (PA) to analyze the program content, to examine the benefits and risks of products with respect to program goals, and to support programmatic decision making. The PA process includes systematic identification of current and future safety risks as well as tracking several quantitative and qualitative metrics to ensure the program goals are addressing prominent safety risks accurately and effectively. One of the metrics within the PA process involves using quantitative aviation safety models to gauge the impact of the safety products. This paper demonstrates the role of aviation safety modeling by providing model outputs and evaluating a sample of portfolio elements using the Flightdeck Automation Problems (FLAP) model. The model enables not only ranking of the quantitative relative risk reduction impact of all portfolio elements, but also highlighting the areas with high potential impact via sensitivity and gap analyses in support of the program office. Although the model outputs are preliminary and products are notional, the process shown in this paper is essential to a comprehensive PA of NASA's safety products in the current program and future programs/projects

Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction

Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system failures and anomalies of avionic systems are also incorporated. The resultant model helps simulate the emergence of automation-related issues in today's modern airliners from a top-down, generalized approach, which serves as a platform to evaluate NASA developed technologie

Risk-Based Causal Modeling of Airborne Loss of Separation

Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP

Mucin glycosylation and sulphation in airway epithelial cells is not influenced by cystic fibrosis transmembrane conductance regulator expression

Abnormalities in mucus properties and clearance make a major contribution to the pathology of cystic fibrosis (CF). Our aim was to test the hypothesis that the defects in CF mucus are a direct result of mutations in the CF transmembrane conductance regulator (CFTR) protein. We evaluated a single mucin molecule MUC1F/5ACTR that carries tandem repeat sequence from MUC5AC, a major secreted airway mucin, in a MUC1 mucin vector. To establish whether the presence of mutant or normal CFTR directly influences the O-glycosylation and sulphation of mucins in airway epithelial cells, we used the CFT1-LC3 (DeltaF508 CFTR mutant) and CFT1-LCFSN (wild-type CFTR corrected) human airway epithelial cell lines. MUC1F/5ACTR mucin was immunoprecipitated, centricon purified, and O-glycosylation was evaluated by Matrix-assisted laser desorption ionization and electrospray tandem mass spectrometry to determine the composition of different carbohydrate structures. Mass spectrometry data showed the same O-glycans in both CFTR mutant and wild-type CFTR corrected cells. Metabolic labeling assays were performed to evaluate gross glycosylation and sulphation of the mucins and showed no significant difference in mucin synthesized in six independent clones of these cell lines. Our results show that the absence of functional CFTR protein causes neither an abnormality in mucin O-glycosylation nor an increase in mucin sulphation

Probabilistic Design Analysis (PDA) Approach to Determine the Probability of Cross-System Failures for a Space Launch Vehicle

Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I projec

Object-Oriented Bayesian Networks (OOBN) for Aviation Accident Modeling and Technology Portfolio Impact Assessment

The concern for reducing aviation safety risk is rising as the National Airspace System in the United States transforms to the Next Generation Air Transportation System (NextGen). The NASA Aviation Safety Program is committed to developing an effective aviation safety technology portfolio to meet the challenges of this transformation and to mitigate relevant safety risks. The paper focuses on the reasoning of selecting Object-Oriented Bayesian Networks (OOBN) as the technique and commercial software for the accident modeling and portfolio assessment. To illustrate the benefits of OOBN in a large and complex aviation accident model, the in-flight Loss-of-Control Accident Framework (LOCAF) constructed as an influence diagram is presented. An OOBN approach not only simplifies construction and maintenance of complex causal networks for the modelers, but also offers a well-organized hierarchical network that is easier for decision makers to exploit the model examining the effectiveness of risk mitigation strategies through technology insertions

Nitroprusside modulates pulmonary vein arrhythmogenic activity

<p>Abstract</p> <p>Background</p> <p>Pulmonary veins (PVs) are the most important sources of ectopic beats with the initiation of paroxysmal atrial fibrillation, or the foci of ectopic atrial tachycardia and focal atrial fibrillation. Elimination of nitric oxide (NO) enhances cardiac triggered activity, and NO can decrease PV arrhythmogensis through mechano-electrical feedback. However, it is not clear whether NO may have direct electrophysiological effects on PV cardiomyocytes. This study is aimed to study the effects of nitroprusside (NO donor), on the ionic currents and arrhythmogenic activity of single cardiomyocytes from the PVs.</p> <p>Methods</p> <p>Single PV cardiomyocytes were isolated from the canine PVs. The action potential and ionic currents were investigated in isolated single canine PV cardiomyocytes before and after sodium nitroprusside (80 μM,) using the whole-cell patch clamp technique.</p> <p>Results</p> <p>Nitroprusside decreased PV cardiomyocytes spontaneous beating rates from 1.7 ± 0.3 Hz to 0.5 ± 0.4 Hz in 9 cells (P < 0.05); suppressed delayed afterdepolarization in 4 (80%) of 5 PV cardiomyocytes. Nitroprusside inhibited L-type calcium currents, transient outward currents and transient inward current, but increased delayed rectified potassium currents.</p> <p>Conclusion</p> <p>Nitroprusside regulates the electrical activity of PV cardiomyocytes, which suggests that NO may play a role in PV arrhythmogenesis.</p