Evaluation of Technology Concepts for Traffic Data Management and Relevant Audio for Datalink in Commercial Airline Flight Decks

Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., party-line transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Centers Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nations air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two

Impact of Advanced Synoptics and Simplified Checklists During Aircraft Systems Failures

AbstractNatural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds demanded in todays aviation environment. Increasingly Autonomous Systems (IAS) are uniquely suited to solve this problem. NASA is conducting research and development of IAS - hardware and software systems, utilizing machine learning algorithms, seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. IAS offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. A human-in-the-loop test was conducted in NASA Langleys Integration Flight Deck B-737-800 simulator to evaluate advanced synoptic pages with simplified interactive electronic checklists as an IAS for routine air carrier flight operations and in response to aircraft system failures. Twelve U.S. airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to failures. These data are fundamental to and critical for the design and development of future increasingly autonomous systems that can better support the human in the cockpit. Synoptic pages and electronic checklists significantly improved pilot responses to non-normal scenarios, but implementation of these aids and other intelligent assistants have barriers to implementation (e.g., certification cost) that must overcome

Thermal Conductivity, Heat Sources and Temperature Profiles of Li-ion Secondary Batteries

We measure and report the thermal conductivity of several commercial and non-commercial Li-ion secondary battery electrode materials, with and without electrolyte solvents. We also measure the Tafel potential, the ohmic resistance, reaction entropy and external temperature of a commercial pouch cell secondary Li-ion battery. Finally we combine all the experimentally obtained data in a thermal Fourier model and discuss the corresponding internal and external temperature profiles during charging and discharging. Electrochemical accumulators and power sources can be both very effective and efficient energy converters. However, as one seeks to intensify both volumetric and specific capacity the heat of these is an inevitable topic in engineering. Moreover, in order to increase performance, the electrodes are necessarily made porous, so that the active specific surface can be increased. In doing so, the thermal conductivity can be lowered by several orders of magnitude. Literature describing thermal conductivity of this property of different Li-ion electrodes is scarse, according to recent reviews e.g. [1], although it is very important. For the ex-situ thermal conductivity measurements we chose commercial electrode materials and for the temperature profile measurements and the electrochemical characterisation we chose a commercial Li-ion pouch cell battery. The electrode materials that we investigated with respect to thermal conductivity were a commercial cathode material (LiCoO 3 ) and a commercial anode material (SLP50). These materials were measured with in an already established procedure [2], both as dry pristine electrode and with a surplus of an electrolyte solvent. The commercial battery was characterised by classical charge and discharge cycling at different current rates.. These experiments were performed in a temperature regulated cabinet with a thermocouple on the battery surface and another in the ambient air. Thus all information required to model the battery's internal and external temperature profiles were collected for the modelling part. The thermal conductivity of dry and soaked electrode material was found to be 0.30 ±0.01 and 0.89±0.04 W K -1 m -1 for the anode material and 0.36±0.003 and 1.10±0.06 for the cathode material. For all materials examined it was found that adding electrolyte solvent increased the thermal conductivity by at least a factor of three. Measuring and combining the surface and the ambient temperatures of an air cooled commercial pouch cell battery at ±2°C, the electric heat sources, and the thermal conductivity of the electrode components made it possible to estimate internal and external temperature profiles at any current density. At 12C charging rate (corresponding to 5 minutes complete charging) the internal temperature differences was estimated to be in the range of 3-4 K, depending on the electrode thermal conductivity. The external temperature drop in air flowing (by forced convection) at the battery surface was estimated to nearly 70K. Thus it is clear that though it is the external temperature gradients that need the most attention with respect to engineered cooling, also internal temperatures become significant at large current rates

Prevalence of diarrheagenic Escherichia coli and impact on child health in Cap-Haitien, Haiti

BACKGROUND: Diarrheagenic Escherichia coli (DEC) are common pathogens infecting children during their growth and development. Determining the epidemiology and the impact of DEC on child anthropometric measures informs prioritization of prevention efforts. These relationships were evaluated in a novel setting, Cap-Haitien, Haiti. METHODS: We performed pre-specified secondary analysis of a case-control study of community-dwelling children, 6-36 months of age, enrolled 96 cases with diarrhea and 99 asymptomatic controls. Assessments were performed at enrollment and one month later at follow-up. Established endpoint PCR methodologies targeted DEC gDNA isolated from fecal swabs. The association between DEC and anthropometric z-scores at enrollment was determined using multivariate linear regression. Lastly, we assessed the association between specific biomarkers, choline and docosahexaenoic acid (DHA) and diarrheal burden. RESULTS: Enterotoxigenic Escherichia coli (ETEC) was identified in 21.9% of cases vs. 16.1% of controls with heat-stable producing ETEC significantly associated with symptomatic disease. Enteroaggregative E. coli (EAEC) was found in 30.2% of cases vs. 27.3% of controls, and typical enteropathogenic E. coli in 6.3% vs. 4.0% of cases and controls, respectively. Multivariate linear regression, controlled for case or control status, demonstrated ETEC and EAEC were significantly associated with reduced weight-age z-score (WAZ) and height-age z-score (HAZ) after adjusting for confounders. An interaction between ETEC and EAEC was observed. Choline and DHA were not associated with diarrheal burden. CONCLUSIONS: DEC are prevalent in north Haitian children. ETEC, EAEC, household environment, and diet are associated with unfavorable anthropometric measures, with possible synergistic interactions between ETEC and EAEC. Further studies with longer follow up may quantify the contribution of individual pathogens to adverse health outcomes

Genetic associations with temporal shifts in obesity and severe obesity during the obesity epidemic in Norway:A longitudinal population-based cohort (the HUNT Study)

Background Obesity has tripled worldwide since 1975 as environments are becoming more obesogenic. Our study investigates how changes in population weight and obesity over time are associated with genetic predisposition in the context of an obesogenic environment over 6 decades and examines the robustness of the findings using sibling design. Methods and findings A total of 67,110 individuals aged 13–80 years in the Nord-Trøndelag region of Norway participated with repeated standardized body mass index (BMI) measurements from 1966 to 2019 and were genotyped in a longitudinal population-based health study, the Trøndelag Health Study (the HUNT Study). Genotyping required survival to and participation in the HUNT Study in the 1990s or 2000s. Linear mixed models with observations nested within individuals were used to model the association between a genome-wide polygenic score (GPS) for BMI and BMI, while generalized estimating equations were used for obesity (BMI ≥ 30 kg/m2) and severe obesity (BMI ≥ 35 kg/m2). The increase in the average BMI and prevalence of obesity was steeper among the genetically predisposed. Among 35-year-old men, the prevalence of obesity for the least predisposed tenth increased from 0.9% (95% confidence interval [CI] 0.6% to 1.2%) to 6.5% (95% CI 5.0% to 8.0%), while the most predisposed tenth increased from 14.2% (95% CI 12.6% to 15.7%) to 39.6% (95% CI 36.1% to 43.0%). Equivalently for women of the same age, the prevalence of obesity for the least predisposed tenth increased from 1.1% (95% CI 0.7% to1.5%) to 7.6% (95% CI 6.0% to 9.2%), while the most predisposed tenth increased from 15.4% (95% CI 13.7% to 17.2%) to 42.0% (95% CI 38.7% to 45.4%). Thus, for 35-year-old men and women, respectively, the absolute change in the prevalence of obesity from 1966 to 2019 was 19.8 percentage points (95% CI 16.2 to 23.5, p < 0.0001) and 20.0 percentage points (95% CI 16.4 to 23.7, p < 0.0001) greater for the most predisposed tenth compared with the least predisposed tenth, defined using the GPS for BMI. The corresponding absolute changes in the prevalence of severe obesity for men and women, respectively, were 8.5 percentage points (95% CI 6.3 to 10.7, p < 0.0001) and 12.6 percentage points (95% CI 9.6 to 15.6, p < 0.0001) greater for the most predisposed tenth. The greater increase in BMI in genetically predisposed individuals over time was apparent after adjustment for family-level confounding using a sibling design. Key limitations include a slightly lower survival to date of genetic testing for the older cohorts and that we apply a contemporary genetic score to past time periods. Future research should validate our findings using a polygenic risk score constructed from historical data. Conclusions In the context of increasingly obesogenic changes in our environment over 6 decades, our findings reveal a growing inequality in the risk for obesity and severe obesity across GPS tenths. Our results suggest that while obesity is a partially heritable trait, it is still modifiable by environmental factors. While it may be possible to identify those most susceptible to environmental change, who thus have the most to gain from preventive measures, efforts to reverse the obesogenic environment will benefit the whole population and help resolve the obesity epidemic