Divergence Between the Human State Assumption and Actual Aircraft System State

Divergence is defined in this thesis as an inconsistency between the human operator’s assumption of the system state and the actual state of the system, which is substantial enough to have consequential effects on the outcome of the situation. The purpose of this thesis is to explore the concept of divergence and develop a framework that can be used to identify the consequential causes of divergence in cases involving human-system interaction. Many recent aircraft accidents involve divergence between the crew state assumption and the actual system state. As aircraft systems and automation become more complex, it’s possible that the consequential effects of divergence, illustrated by these accidents, could become more prevalent due to the correspondingly more complex understanding that may be required by the crew to effectively operate the aircraft. Divergence was explored as a concept by (1) understanding the previous literature related to divergence such as work on human error, human information processing, situation awareness, and mode awareness (2) developing a framework that can be used to understand possible causes of divergence, (3) illustrating use of the framework with accident case studies, and (4) discussing the implications of the findings of the case study analysis of divergence. Human information processing of divergence was developed using the established human information processing literature including Wickens (1992), Endsley (1995), and Reason (1990). The framework highlighted the inputs to the human and represented human processing of this information in relation to formation of a state assumption. The process model was used to identify potential causes of divergence, which were hypothesized as human information processing failures affecting the human state assumption, and to evaluate the effects of those failures on downstream processes and the human state assumption. Eleven accident case studies involving automation mode confusion were conducted to evaluate divergence using the process model of divergence. Eight of the case studies involved auto-throttle mode confusion and the three remaining cases involved divergence in other automation systems that resulted in controlled flight into terrain. The industry implications of the findings of the case studies were then discussed.U.S. Department of Transportation – Federal Aviation Administration through the Joint Universities Program (JUP) FAA 11-G-016 and NASA’s Aeronautics Fellowship Program

Human factors studies of an ADS-B based traffic alerting system for general aviation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from department-submitted PDF version of thesis. This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. ).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. TSAA was designed to be compatible with general aviation operations. It was specifically designed to enhance 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 added cost of a MFD. TSAA System performance & basic usability was tested using human in the loop studies using a total of 50 general aviation pilots. The studies also evaluated a number of design issues in order to provide recommendation for the final TSAA design. The system was found to be usable and generally effective for all of the encounter scenarios analyzed in both the audio-only and display systems. Performance was significantly improved in the enroute scenarios when a Cockpit Display of Traffic Information (CDTI) was available compared with aural alerts only. In most cases, pilots became aware and responded to traffic earlier when a display was available. Miss distance also increased. Analysis of the audio only system showed that performance improved when alerts were provided to the pilot when compared to performance without a traffic system for a head-on case highlighting the benefit of TSAA. Performance analysis of the final TSAA design showed that 98.7% of all collisions were avoided when TSAA was used. The 1.3% of collisions that did occur were due to the pilots' conscience decision to disregard an alert. The TSAA system was evaluated for functionality and usability. The findings of these studies will contribute to TSAA standards development for the FAA and design recommendations for the avionics manufacturers.by Sathya Samurdhi Silva.S.M

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

Pilot Perception and Use of ADS-B Traffic and Weather Services (TIS-B & FIS-B)

Automatic Dependent Surveillance – Broadcast (ADS-B) is a central component of the NextGen air traffic control modernization program. It is intended to improve traffic surveillance capabilities by sharing accurate aircraft position information between pilots and air traffic controllers. In addition, “ADS-B In” capability provides pilots with traffic information for nearby flights along with relevant weather and airspace information. Pilots can access these products using a variety of installed and portable avionics systems. This study was intended to evaluate potential benefits of ADS-B In traffic and weather services. Goals included identifying the factors that influence the decision whether to equip with ADS-B In as well as evaluating current pilot usage of traffic and flight information uplink services.This project was funded by the U.S. Department of Transportation - Federal Aviation Administration through the Consortium in Aviation Operations Research (NEXTOR II) Center of Excellence

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Global Impact of the COVID-19 Pandemic on Cerebral Venous Thrombosis and Mortality

Background and purpose: Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year. Methods: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020). Results: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths. Conclusions: During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT

Global Impact of the COVID-19 Pandemic on Cerebral Venous Thrombosis and Mortality.

BACKGROUND AND PURPOSE: Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year. METHODS: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020). RESULTS: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths. CONCLUSIONS: During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT

Divergence between the human state assumption and the actual aircraft system state

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 173-184).Divergence is defined in this thesis as an inconsistency between the human operator's assumption of the system state and the actual state of the system, which is substantial enough to have consequential effects on the outcome of the situation. The purpose of this thesis is to explore the concept of divergence and develop a framework that can be used to identify the consequential causes of divergence in cases involving human-system interaction. Many recent aircraft accidents involve divergence between the crew state assumption and the actual system state. As aircraft systems and automation become more complex, it's possible that the consequential effects of divergence, illustrated by these accidents, could become more prevalent due to the correspondingly more complex understanding that may be required by the crew to effectively operate the aircraft. Divergence was explored as a concept by (1) understanding the previous literature related to divergence such as work on human error, human information processing, situation awareness, and mode awareness (2) developing a framework that can be used to understand possible causes of divergence, (3) illustrating use of the framework with accident case studies, and (4) discussing the implications of the findings of the case study analysis of divergence. Human information processing of divergence was developed using the established human information processing literature including Wickens (1992), Endsley (1995), and Reason (1990). The framework highlighted the inputs to the human and represented human processing of this information in relation to formation of a state assumption. The process model was used to identify potential causes of divergence, which were hypothesized as human information processing failures affecting the human state assumption, and to evaluate the effects of those failures on downstream processes and the human state assumption. Eleven accident case studies involving automation mode confusion were conducted to evaluate divergence using the process model of divergence. Eight of the case studies involved auto-throttle mode confusion and the three remaining cases involved divergence in other automation systems that resulted in controlled flight into terrain. The industry implications of the findings of the case studies were then discussed.by Sathya Silva.Ph. D