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Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

black swan event↗

Integrated Systems Engineering, Safety, Reliability and Risk Management – Minimizing Black Swan Events

This paper examines key barriers that can possibly inhibit safe and reliable mission execution and, in the worst case, result in loss of human life due to many unknown contributory factors that can lead to Black Swan events. Some of the representative contributory factors include decision errors, overconfidence and a host of common causes including cultural and human factors. Decisions are always easy to criticize in hindsight when more information is available after a major accident. Depending on the type and complexity of the project and/or mission, the catastrophic risks of drifting into failure can be alleviated by implementing uniquely and strategically tailored Integrated-System-of-Systems, dynamic, risk-informed decision management processes. This paper presents some of the lessons learned from James Webb Space Telescope (JWST), NASA’s Human Space Flight program, and industry that provide motivation to organizations working on mega-complex missions to prudently accomplish targeted mission success. These lessons are important for future human Lunar, Mars and Beyond missions planned to be pursued by NASA through a public-private partnership using nimble but effective safety-conscious, proven sound engineering practices including implementation of integrated risk mitigation practices.

SLS↗

Warning Signs of Potential Black Swan Outbreaks in Infectious Disease

Black swan events in infectious disease describe rare but devastatingly large outbreaks. While experts are skeptical that such events are predictable, it might be possible to identify the warning signs of a black swan event. Specifically, following the initiation of an outbreak, key differentiating features could serve as alerts. Such features could be derived from meta-analyses of large outbreaks for multiple infectious diseases. We hypothesized there may be common features among the pathogen, environment, and host epidemiological triad that characterize an infectious disease black swan event. Using Los Alamos National Laboratory’s tool, Analytics for Investigation of Disease Outbreaks, we investigated historical disease outbreak information and anomalous events for several infectious diseases. By studying 32 different infectious diseases and global outbreaks, we observed that in the past 20–30 years, there have been potential black swan events in the majority of infectious diseases analyzed. Importantly, these potential black swan events cannot be attributed to the first introduction of the disease to a susceptible host population. This paper describes our observations and perspectives and illustrates the value of broad analysis of data across the infectious disease realm, providing insights that may not be possible when we focus on singular infectious agents or diseases. Data analytics could be developed to warn health authorities at the beginning of an outbreak of an impending black swan event. Such tools could complement traditional epidemiological modeling to help forecast future large outbreaks and facilitate timely warning and effective, targeted resource allocation for mitigation efforts.

60 APPLIED LIFE SCIENCES↗

Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns

This paper examines the relationship between schedule delays and cost overruns on complex projects. It is generally accepted by many project practitioners that cost overruns are directly related to schedule delays. But what does "directly related to" actually mean? Some reasons or root causes for schedule delays and associated cost overruns are obvious, if only in hindsight. For example, unrealistic estimates, supply chain difficulties, insufficient schedule margin, technical problems, scope changes, or the occurrence of risk events can negatively impact schedule performance. Other factors driving schedule delays and cost overruns may be less obvious and more difficult to quantify. Examples of these less obvious factors include project complexity, flawed estimating assumptions, over-optimism, political factors, "black swan" events, or even poor leadership and communication. Indeed, is it even possible the schedule itself could be a source of delay and subsequent cost overrun? Through literature review, surveys of project practitioners, and the authors' own experience on NASA programs and projects, the authors will categorize and examine the various factors affecting the relationship between project schedule delays and cost growth. The authors will also propose some ideas for organizations to consider to help create an awareness of the factors which could cause or influence schedule delays and associated cost growth on complex projects.

Overruns↗

Prospective Safety Analysis and the Complex Aviation System

Fatal accident rates in commercial passenger aviation are at historic lows yet have plateaued and are not showing evidence of further safety advances. Modern aircraft accidents reflect both historic causal factors and new unexpected "Black Swan" events. The ever-increasing complexity of the aviation system, along with its associated technology and organizational relationships, provides fertile ground for fresh problems. It is important to take a proactive approach to aviation safety by working to identify novel causation mechanisms for future aviation accidents before they happen. Progress has been made in using of historic data to identify the telltale signals preceding aviation accidents and incidents, using the large repositories of discrete and continuous data on aircraft and air traffic control performance and information reported by front-line personnel. Nevertheless, the aviation community is increasingly embracing predictive approaches to aviation safety. The "prospective workshop" early assessment tool described in this paper represents an approach toward this prospective mindset-one that attempts to identify the future vectors of aviation and asks the question: "What haven't we considered in our current safety assessments?" New causation mechanisms threatening aviation safety will arise in the future because new (or revised) systems and procedures will have to be used under future contextual conditions that have not been properly anticipated. Many simulation models exist for demonstrating the safety cases of new operational concepts and technologies. However the results from such models can only be as valid as the accuracy and completeness of assumptions made about the future context in which the new operational concepts and/or technologies will be immersed. Of course that future has not happened yet. What is needed is a reasonably high-confidence description of the future operational context, capturing critical contextual characteristics that modulate both the likelihood of occurrence of hazards, and the likelihood that those hazards will lead to negative safety events. Heuristics extracted from scenarios, questionnaires, and observed trends from scanning the aviation horizon may be helpful in capturing those future changes in a way conducive to safety assessment. What is also needed is a checklist of potential sources of emerging risk that arise from organizational features that are frequently overlooked. The ultimate goal is to develop a pragmatic, workable method for using descriptions of the future aviation context, to generate valid predictions of safety risks.

prospection↗

The Discovery of the Antarctic Ozone Hole

The author Nassim Taleb has coined the term ‘‘Black Swan’’ event to describe a very low probability event that come as a surprise and has a major effect in the field in which it occurs. He suggests that such events have occurred in history, finance, science, and technology more frequently than one can expect from stochastic theory. The discovery of the Antarctic Ozone Hole fits this description well. In this paper, we describe the events surrounding this discovery and the role of NASA satellite data before and soon after the seminal paper by Farman et al., in May 1985 that first brought this phenomenon to the attention of the broader science community.

Bhartia, Pawan Kumar↗

Has NO(2) in Metropolitan Areas Returned to Pre-COVID-19 Levels as Measured by OMI Satellite Data?

Nitrogen oxides (NOx = NO + NO2) are primarily emitted through the combustion of fossil fuels, such as automobiles and power plants. NOx can contribute to chemical reactions that produce ozone and fine particulate matter (PM2.5) pollution. Using tropospheric vertical column density (TVCD) NO2 data from satellite hyperspectral instruments, such as the Ozone Monitoring Instrument (OMI) on board NASA’s EOS Aura satellite, one can monitor changes in NO2 pollution during “black-swan” events, such as mobility restrictions during COVID-19 lockdowns. Our OMI team has created the Global Nitrogen Dioxide Monitoring Home Page (https://so2.gsfc.nasa.gov/no2/no2_index.html ) where we display NO2 TVCD time series, animated maps, and COVID-specific NO2 anomalies comparing to a pre-COVID five-year average for 317 cities and regions around the world. During the pandemic as a result of the widespread lockdowns, many areas saw low NO2 anomalies superimposed on the natural weather related NO2 changes, such as seasonal NO2 cycle. Has NO2 levels returned to pre-Covid-19 levels in large metropolitan areas? We analyze NO2 time series from March through September for the years 2020-2023 using as a reference the average NO2 from 2015-2019 for those months. We will present data from selected cities that represent all countries.

Keith D Evans↗

Storylines for unprecedented heatwaves based on ensemble boosting

Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders.

54 ENVIRONMENTAL SCIENCES↗