A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS

This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

New Perspectives on the System Usage Construct

Information systems are designed to support human and organizational purposes. To achieve their ends, information systems must be used. Although this may seem to be self-evident, there are many aspects of systems usage that are not so, and yet, in spite of this, there has been little intense conceptual scrutiny of this construct in past research. The objective of this thesis, therefore, is to develop new in-depth perspectives for studying system usage. Drawing on critical realist assumptions and studies of research diversity, I explain how epistemological factors enable while ontological factors constrain the diversity of meanings of system usage, and I build on this reasoning to advance a systematic approach for conceptualizing and measuring system usage in an appropriate way for a given research context. To demonstrate the approach and judge its usefulness, I carry out three empirical studies to test whether measures of system usage that are selected according to the proposed approach provide more explanatory power and lead to more coherent results in specific research contexts than other measures of system usage. Exploring the relationship between system usage and user task performance among 804 users of spreadsheet software, the experiments reveal support for the usefulness of the approach and demonstrate how it can enable researchers to conceptualize and measure system usage in an appropriate manner for a given research context. Together, the conceptual approach and empirical studies contribute by: (1) providing a systematic way to conceptualize and measure system usage for a given study context, (2) revealing rich new directions for research on the nature of system usage, its antecedents, and its consequences, and (3) suggesting a new approach for construct development and investigation in IS research

Institutional and Individual Influences on Scientists\u27 Data Sharing Behaviors

In modern research activities, scientific data sharing is essential, especially in terms of data-intensive science and scholarly communication. Scientific communities are making ongoing endeavors to promote scientific data sharing. Currently, however, data sharing is not always well-deployed throughout diverse science and engineering disciplines. Disciplinary traditions, organizational barriers, lack of technological infrastructure, and individual perceptions often contribute to limit scientists from sharing their data. Since scientists\u27 data sharing practices are embedded in their respective disciplinary contexts, it is necessary to examine institutional influences as well as individual motivations on scientists\u27 data sharing behaviors. The objective of this research is to investigate the institutional and individual factors which influence scientists\u27 data sharing behaviors in diverse scientific communities. Two theoretical perspectives, institutional theory and theory of planned behavior, are employed in developing a conceptual model, which shows the complementary nature of the institutional and individual factors influencing scientists\u27 data sharing behaviors. Institutional theory can explain the context in which individual scientists are acting; whereas the theory of planned behavior can explain the underlying motivations behind scientists\u27 data sharing behaviors in an institutional context. This research uses a mixed-method approach by combining qualitative and quantitative methods: (1) interviews with the scientists in diverse scientific disciplines to understand the extent to which they share their data with other researchers and explore institutional and individual factors affecting their data sharing behaviors; and (2) survey research to examine to what extent those institutional and individual factors influence scientists\u27 data sharing behaviors in diverse scientific disciplines. The interview study with 25 scientists shows three groups of data sharing factors, including institutional influences (i.e. regulative pressures by funding agencies and journals and normative pressure); individual motivations (i.e. perceived benefit, risk, effort and scholarly altruism); and institutional resources (i.e. metadata and data repositories). The national survey (with 1,317 scientists in 43 disciplines) shows that regulative pressure by journals; normative pressure at a discipline level; and perceived career benefit and scholarly altruism at an individual level have significant positive relationships with data sharing behaviors; and that perceived effort has a significant negative relationship. Regulative pressure by funding agencies and the availability of data repositories at a discipline level and perceived career risk at an individual level were not found to have any significant relationships with data sharing behavior

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Using latent class cluster analysis to identify and profile organizational subclimates: An exploratory investigation using safety climate as an exemplar

Organizational climate refers to the shared meaning organizational members attach to the events, policies, practices, and procedures they experience as well as to the behaviors they see being rewarded, supported, and expected (Schneider, Ehrhart, & Macey, 2011). Climate scholars have most frequently used referent-shift consensus and dispersion composition models (Chan, 1998) to conceptualize and measure organizational climate. Based on these models, climate emergence has been characterized by low variance or high consensus of individual-level climate perceptions (Chan, 1998; Ehrhart, Schneider, & Macey, 2013; Hazy & Ashley, 2011; Kuenzi & Schminke, 2009) within formally defined organizational groups (e.g., work teams). Climate scholars have begun to acknowledge these approaches may not offer adequate explanations for organizational-level perceptual variance patterns that could result from socially-derived influences such as demographic attribute similarity. Perceptual variance may instead be better explained by a patterned emergence compilation model (Fulmer & Ostroff, 2015), whereby nonuniform patterns of dispersion assume that skewness and/or multiple modes exist within the climate of an organization. Ostroff and Fulmer (2014) and Fulmer and Ostroff (2015) have proposed that configural measurement techniques such as latent class cluster analysis (LCCA; Nylund, Asparouhov, & Muthén, 2007) be used to identify subgroups of employees who perceive the organization similarly (i.e., subclimates). LCCA addresses the problems inherent in identifying subclimates via traditional composition models and measurement approaches, but has yet to be used for this purpose. To address this gap, this exploratory study examined whether an organization may be usefully classified into subclimates, based on similarity of response patterns across safety climate dimensions. Subclimates were conceptualized as latent, unobserved groups characterized by systematic response patterns that exhibit within-group agreement and between-group differentiation, using LCCA to reveal five latent groups. Each distinct subclimate was subsequently examined for meaningful differences between them on profile characteristics and demographic attributes

Multivariate Multilevel Value-Added Modeling: Constructing a Teacher Effectiveness Composite

This simulation study presents a justification for evaluating teacher effectiveness with a multivariate multilevel model. It was hypothesized that the multivariate model leads to more precise effectiveness estimates when compared to separate univariate multilevel models. Then, this study investigated combining the multiple effectiveness estimates that are produced by the multivariate multilevel model and produced by separate univariate multilevel models. Given that the models could produce significantly different effectiveness estimates, it was hypothesized that the composites formed from the results of the multivariate multilevel model differ from the composites formed from the results of the separate univariate models in terms of bias. The correlations between the composites from the different models were very high, providing no evidence that the model choice was impactful. Also, the differences in bias and fit were slight. While the findings do not really support a claim for the use of the more complex multivariate model over the univariate models, the increased theoretical validity from adding outcomes to the VAM does

The Class Size Controversy

[Excerpt] When we ask whether class size matters for achievement, it is essential to ask also, how class size matters. This is important for three reasons. First, if we can observe not only achievement differences, but also the mechanisms through which the differences are produced, this will increase our confidence that the differences are real, and not an artifact of some unmeasured or inadequately controlled condition. Second, the effects of class size may vary in different circumstances, and identifying how class size affects achievement will help us to understand why the effects of class size are variable. Third, the potential benefits of class size reduction may be greater than what we observe. For example, suppose class size reductions aid achievement, but only when teachers modify instructional practices to take advantage of the smaller classes. If a few teachers make such modifications, but most do not, then understanding how class size affects achievement in some cases will help reveal its potential effects, even if the potential is generally unrealized