An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development

This final technical report describes the results of a NASA Innovative Advanced Concept (NIAC) Phase 2 study entitled "An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development." This NIAC Phase 2 study was conducted at the Asteroid Deflection Research Center (ADRC) of Iowa State University in 2012-2014. The study objective was to develop an innovative yet practically implementable solution to the most probable impact threat of an asteroid or comet with short warning time (less than 5 years). The technical materials contained in this final report are based on numerous technical papers, which have been previously published by the project team of the NIAC Phase 1 and 2 studies during the past three years. Those technical papers as well as a NIAC Phase 2 Executive Summary report can be downloaded from the ADRC website (www.adrc.iastate.edu)

An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development

To develop an innovative yet practically implementable mitigation technique for the most probable impact threat of an asteroid or comet with short warning time(i.e., when we don't have sufficient warning times for a deflection mission

An Optimal Mitigation Strategy Against the Asteroid Impact Threat with Short Warning Time

To develop an innovative yet practically implementable mitigation technique for the most probable impact threat of an asteroid or comet with short warning time (i.e., when we don't have sufficient warning times for a deflection mission)

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale

Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds.

Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant's degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms

An experimental and detailed chemical kinetic modeling study of hydrogen and syngas mixture oxidation at elevated pressures

International audienc

A European aerosol phenomenology – 3: Physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe

This paper synthesizes data on aerosol (particulate matter, PM) physical and chemical characteristics, which were obtained over the past decade in aerosol research and monitoring activities at more than 60 natural background, rural, near-city, urban, and kerbside sites across Europe. The data include simultaneously measured PM10 and/or PM2.5 mass on the one hand, and aerosol particle number concentrations or PM chemistry on the other hand. The aerosol data presented in our previous works (Van Dingenen et al., 2004; Putaud et al., 2004) were updated and merged to those collected in the framework of the EU supported European Cooperation in the field of Scientific and Technical action COST 633 (Particulate matter: Properties related to health effects). A number of conclusions from our previous studies were confirmed. There is no single ratio between PM2.5 and PM10 mass concentrations valid for all sites, although fairly constant ratios ranging from 0.5 to 0.9 are observed at most individual sites. There is no general correlation between PM mass and particle number concentrations, although particle number concentrations increase with PM2.5 levels at most sites. The main constituents of both PM10 and PM2.5 are generally organic matter, sulfate and nitrate. Mineral dust can also be a major constituent of PM10 at kerbside sites and in Southern Europe. There is a clear decreasing gradient in SO42- and NO3- contribution to PM10 when moving from rural to urban to kerbside sites. In contrast, the total carbon / PM10 ratio increases from rural to kerbside sites. Some new conclusions were also drawn from this work: the ratio between ultrafine particle and total particle number concentration decreases with PM2.5 concentration at all sites but one, and significant gradients in PM chemistry are observed when moving from North-Western, to Southern to Central Europe. Compiling an even larger number of data sets would have further increased the significance of our conclusions, but collecting all the aerosol data sets obtained also through research projects remains a tedious task.JRC.H.2-Air and Climat

Interevent relationships and judgment under uncertainty: Structure determines strategy

Item does not contain fulltextA fundamental empirical question regarding judgments about events is whether experienced absolute frequencies or relative. frequencies are relied on when the likelihood of a particular occurrence is judged. The present research explicates the conditions under which people rely on remembered raw absolute frequencies versus on inferred relative frequencies or proportions when making predictions. Participants saw opinion poll results for candidates prior to an election and, on the basis of these, made judgments concerning the likelihood of each candidate's winning this election. Certain candidates demonstrated a high absolute frequency of winning in the polls, whereas other candidates had high relative win frequencies. The results indicated that adults are cognitively flexible with regard to the inputs used in this judgment. Certain stimulus event configurations induced reasoning by way of absolute frequencies, whereas other configurations elicited judgments based on relative frequencies. More specifically, as the relational complexity of the event structure increased and more inferences were required to make predictions, the tendency to rely on absolute, as opposed to relative, frequencies also increased