Experimental Study of Vertical Flight Path Mode Awareness

An experimental simulator study was run to test pilot detection of an error in autopilot mode selection. Active airline air crew were asked to fly landing approaches by commanding the Flight Path Angle mode while monitoring the approach with both a Head Up Display and Head Down Displays. During one approach, the Vertical Speed mode was intentionally triggered by an experimenter instead, causing a high rate of descent below the intended glide path. Of the 12 pilots, 10 were unable to detect the high descent rate prior to significant glide path deviation

Design of Support Systems for Dynamic Decision Making in Airline Operations

Presented at the Institute of Electrical and Electronics Engineers (IEEE) Systems and Information Engineering Design Symposium, Charlottesville, Virginia, April, 2006 and published in the Proceedings of the 2006 IEEE Systems and Information Engineering Design Symposium. ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.To date, there has been very little research conducted on the design of support systems for dynamic decisions environments, such as airline operations. The paper discusses the idea that the regulation of dynamic systems has implications for both "internal" and "external" dynamic systems with respect to the human operator. Hollnagel's Contextual Control Modes are suggested as a framework for designing such support systems, noting that they can identify requirements specific to different contextual control modes

Modeling the Work of Humans and Automation in Complex Operations

Humans have always been the vital components of complex operations, notably including aviation. They remain so even as sophisticated automation systems are introduced, changing -but not eliminating -the role of the human relative to the collective work required to achieve mission performance. Automation designers and certification agencies are interested in methods to predict and model how complex operations can be performed by teams of humans and automated agents. This paper proposes that the combined activities of both human and automation required by a proposed design can best be captured by focusing on modeling the work inherent to a complex operation. As a fundamental first step, the overall concept of operations spanning all the work activities can be examined for its feasibility in nominal and off-nominal conditions. These activities can then also be examined to see whether the demands they place upon the human agents in the system are feasible and facilitate the human's ability to contribute, rather than assuming unreasonable situations such as excessive workload, boredom, incoherent task descriptions, excessive monitoring requirements, etc. Further, trade-offs in distributing these activities across agents (both human and automated) can be evaluated in terms of task-interleaving created by the distribution of activity and in terms of the 'interaction overhead' associated with communication and coordination between agents required for a given distribution. A description of a modeling and simulation framework capable of modeling work is provided along with an analysis framework to evaluate proposed complex operations

A Simulation Engine to Predict Multi-Agent Work in Complex, Dynamic, Heterogeneous Systems

©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/COGSIMA.2011.5753432This paper documents a simulation engine developed to accurately and efficiently simulate work by multiple agents in complex dynamic systems. Agents (human or mechanical) are modeled as responding to, and changing, their environment by executing the actions that get and set the value of resources in the environment. Each action comprises the processes that need to be evaluated at the same time by the same agent, which are used to reference (get) resources, consider them according to simple or complicated processes, and then interact back on the environment by setting resources appropriately. This paper specifically addresses timing within the simulation. The simplest approach would update all actions at the smallest unit of conceivable time, an approach that is not only computationally inefficient, but also not an accurate representation of situated behavior. Instead, every action declares its next update time as required to accurately model its internal dynamics and the simulation engine executes them asynchronously. Thus, an action and the resources it ’gets’ from the environment are not inherently contemporary; instead, each action also specifies, for each resource value that it gets, the quality of service required in terms of its temporal currency. This reflects dynamics of the real processes being simulated: when, in actual operations, would the environment be sampled, and how accurately must its state be known? Additionally, this also reflects dynamics of environmental resources how often (or how fast) does each inherently change? Using these constructs, the list of actions to be simulated are sorted by the simulation engine according to their next update time. Each action, when its time comes, is given to their agent model to be executed, and then is sorted back into the action list according to its self-reported next update time. Thus, actions are each updated when they need to be. In situations where, for example, action Y needs to get a resource which, because action X has not set it recently, does not meet action Ys required Quality of Service. The simulation engine will invoke action X immediately before action Y, mimicking cases in the real system where one process calls on another to establish the conditions it needs. The presented simulation engine is a complete redevelopment, designed and written from scratch at the Cognitive Engineering Center at the Georgia Institute of Technology

The causes of stalling fertility transitions

An examination of fertility trends in countries with multiple DHS surveys found that in the 1990s fertility stalled in mid-transition in seven countries: Bangladesh, Colombia, Dominican Republic, Ghana, Kenya, Peru, and Turkey. An analysis of trends in the determinants of fertility revealed a systematic pattern of leveling off or near leveling in a number of determinants, including contraceptive use, the demand for contraception, and wanted fertility. Findings suggest no major deterioration in contraceptive access during the stall, but levels of unmet need and unwanted fertility are relatively high and improvements in access to family planning methods would therefore be desirable. No significant link was found between the presence of a stall and trends in socioeconomic development, but at the onset of the stall the level of fertility was low relative to the level of development in all but one of the stalling countries

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Human-Centered Automation as Effective Work Design

This paper describes how the challenge of human-centered automation can be recast as the challenge of, first, designing the work performed by a team of agents and then, second, allocating this work amongst all the agents, human and automated, in support of their own needs and capabilities and to foster team goals. The paper starts by formally describing the construct of work as a structure which can be formally analyzed and around which other design decisions can be made. It then reviews the requirements of effective function allocation within a team to enable their collective taskwork, and to provide the appropriate teamwork. An example is given that highlights key tradeoffs in designing and allocating work in teams of human and automated agents: no one design can maximize all the desired attributes of human-centered automation

Pilot non-conformance to alerting system commands during closely spaced parallel approaches

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1997.Includes bibliographical references (p. 119-121).by Amy Pritchett.Sc.D