Human Factors Flight Testing of an ADS-B Based Traffic Alerting System for General Aviation

Mid-air collisions are a concern for general aviation. Current traffic alerting systems have limited usability in the airport environment where a majority of mid-air collisions occur. A Traffic Situation Awareness with Alerting Application (TSAA) has been developed which uses Automatic Dependent Surveillance – Broadcast (ADS-B), a Global Positioning System (GPS) based surveillance system, to provide reliable alerts in a condensed environment. TSAA was designed to be compatible with general aviation operations. It was specifically designed to enhance traffic situation awareness and provide traffic alerting. The system does not include guidance or resolution advisories. In addition, the design was consistent with established standards, previous traffic alerting system precedents, as well as air traffic control precedent. Taking into account the potential financial burden associated with installation of a multi-function display (MFD), an audio based TSAA system was also designed to account for constrained cockpit space and the added cost of a MFD.This project was funded by the U.S. Department of Transportation - Federal Aviation Administration through the University of Maryland (NEXTOR II) Contract #Z988401

Understanding the Impact of Potential Best-Equipped, Best-Served Policies on the En-Route Air Traffic Controller Performance and Workload

New capabilities of Air Traffic Control (ATC) under development in Next Generation Air Transportation system (NextGen) will increase the system capacity to accommodate the expected growth in the air traffic. One of the key enablers of the NextGen capabilities is advanced onboard equipage of the aircraft. During the transition to NextGen, aircraft with different equipage levels will coexist in the same airspace: mixed-equipage. To reduce the mixed-equipage period, the Federal Aviation Administration (FAA) proposed “best-equipped, best-served policy” as a governing principle for accelerating NextGen equipage, offering incentives to the early adopters of NextGen avionics. However, the policy may introduce new tasks to the air traffic controllers, increasing the cognitive workload and decreasing the controller performance. The policy may be implemented at the strategic or the tactical level. This thesis identified two representative tactical level policies that may increase the difficulty and workload of the en-route air traffic controllers: best-equipped, first-served (BEFS) policy and best-equipped, exclusively served (BEES) policy. To investigate the impact of the potential tactical best-equipped, best-served policies on en-route controller performance and workload, a human-in-the-loop simulation was developed to compare the impacts of the two identified potential policies and the current first-come, first-served policy. The two potential tactical best-equipped, best-served policies provided marginal operational incentives to the NextGen equipage aircraft; however, the policies significantly increased the controller errors and reduced the total system efficiency with considerable delays to the less equipped aircraft compared to the current policy. In addition, higher subjective workload rating with the potential policies, especially during heavy traffic loads, indicated an increase in the controller workload and a reduction of the controller capacity. The analysis suggests that caution needs to be exercised when considering implementation of best-equipped best-served policy at the tactical level. Therefore, a strategic level implantation of the best-equipped, best-served policy is recommended; however, this study did not address impact of the strategic level implementation of the policy.This work was supported by the National Aeronautics and Space Administration (NASA) under grant NNA06CN23A

Prediction of novel synthetic pathways for the production of desired chemicals

<p>Abstract</p> <p>Background</p> <p>There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.</p> <p>Results</p> <p>In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates.</p> <p>Conclusions</p> <p>It is expected that the system framework developed in this study would be useful for the <it>in silico </it>design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.</p

ITDetect: a method to detect internal tandem duplication of FMS-like tyrosine kinase (FLT3) from next-generation sequencing data with high sensitivity and clinical application

Abstract Internal tandem duplication (ITD) of the FMS-like tyrosine kinase (FLT3) gene is associated with poor clinical outcomes in patients with acute myeloid leukemia. Although recent methods for detecting FLT3-ITD from next-generation sequencing (NGS) data have replaced traditional ITD detection approaches such as conventional PCR or fragment analysis, their use in the clinical field is still limited and requires further information. Here, we introduce ITDetect, an efficient FLT3-ITD detection approach that uses NGS data. Our proposed method allows for more precise detection and provides more detailed information than existing in silico methods. Further, it enables FLT3-ITD detection from exome sequencing or targeted panel sequencing data, thereby improving its clinical application. We validated the performance of ITDetect using NGS-based and experimental ITD detection methods and successfully demonstrated that ITDetect provides the highest concordance with the experimental methods. The program and data underlying this study are available in a public repository

Operational Design Domain (ODD) framework for driver-automation integrated systems

Thesis: E.A.A., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, September, 2020Cataloged from student-submitted PDF of thesis.Includes bibliographical references (pages 77-80).Current driving automation systems have technical limitations which restricts their capability. Because of these limitations, the Society of Automotive Engineers (SAE) proposed the concept of the Operational Design Domain (ODD), which defines conditions under which a given driving automation system is designed to function. However, there is not yet a clear standard and a systematic process to evaluate an automation system to determine its ODD. In addition, inappropriate use of the automation outside the ODD may result in accidents. This thesis had following research objectives: 1) to develop a framework and methodology to define the ODD with a principled basis to determine the ODD of driving automation systems, and 2) to understand how the human operators make use decisions in order to support adequate management of the ODD.A risk-based framework was developed, leveraging on the traditional risk theory, to formally provide a threshold to define the ODD in terms of risk related to an automation system's failure to perform its intended function. Based on this framework, the thesis developed a methodology to determine the ODD by identifying the key dimensions of the conditional hyperspace and the boundary that demarcates the sets of conditions with an acceptable level of risk. The method identifies the relevant conditions that becomes the dimensions of the conditional hyperspace in which the ODD boundary is determined. The methodology was applied to a simple forward collision avoidance system in order to demonstrate the use of the proposed framework. Once an ODD is determined for a driving automation system, it must be adequately managed so that the use of the automation outside the ODD is avoided.The key role of ODD management is to observe available information and assess whether the observed conditions are inside or outside the ODD. This role may be performed automatically by the system (for SAE Level 3 or 4 systems) or performed by the human operator (for SAE Level 1 or 2 systems). An analysis of recent accidents involving driving automation system's failure and the literature on human cognition process were used to investigate how human operators perform ODD management. A model was developed and used to identify potential failure modes of human ODD management. These include: 1) inability to observe relevant states indicating an ODD violation, 2) failure to observe relevant states indicating an ODD violation and 3) ignorance of the ODD states resulting in an ODD violation.These failure categories were explored to identify potential causes including: lack of observability of the ODD conditions, drivers over-trust in automation, lack of understanding of the automation or ODD, and inaccurate projection of the future conditions. Based on this, a set of recommendations for improving the driver-automation integrated systems that would support to improve the ODD management are suggested.by HongSeok Cho.E.A.A.E.A.A. Massachusetts Institute of Technology, Department of Aeronautics and Astronautic

Framework for Understanding the Driver's Trust in Automation and Its Implications on Driver's Decision and Behavior

The aim of this paper is to understand how designs of the automated driving system influence the driver's automation use decisions, and to provide design recommendations for SAE level 2 or 3 automated driving systems that would promote appropriate decisions of the driver. A risk-based framework, "decision matrix" is developed to represent the driver's decisions in terms of two variables: 1) perceived reliability of the automation, and 2) perceived consequence of the automation's potential unreliable behavior. Each block of the matrix represents a level of the perceived risk and an accompanying automation use decision of the driver; 1) use, 2) use with monitoring, and 2) do not use. Having the decision matrix at the core, an overall driver-automation system architecture is developed in order to describe how the driver makes a cognitive assessment of the observable states to evaluate the two decision matrix variables. The architecture also includes a learning process by which the driver develops and evolves his/her mental model based on the experience. The architecture is used to identify potentially inappropriate decisions of the driver as a result of the inaccurate evaluations of either the automation reliability or the consequences, or both. The framework suggests various methods (e.g. minimum performance of the automation, limiting automation use, information display and interface design, and training) which manufacturers can design to support the driver in learning of the automation's limitations and thus making appropriate evaluations of the decision variables. As a next step, potential designs of these methods to promote appropriate automation use decisions will be evaluated to determine the effectiveness of the methods

Human Factors Studies of an ADS-B Based Traffic Alerting System for General Aviation

Several recent high profile mid-air collisions highlight the fact that mid-air collisions are a concern for general aviation. Current traffic alerting systems have limited usability in the airport environment where a majority of mid-air collisions occur. A Traffic Situation Awareness with Alerting Application (TSAA) has been developed which uses Automatic Dependent – Surveillance – Broadcast (ADS-B), a Global Positioning System (GPS) based surveillance system, to provide reliable alerts in a condensed environment...This project was funded by the U.S. Department of Transportation - Federal Aviation Administration through the University of Maryland (NEXTOR II) Contract #Z988401