An Analysis of TRL-Based Cost and Schedule Models

The GAO's, NASA's, and the DoD's adoption of the technology readiness level (TRL) scale to improve technology management has led to the emergence of many TRL-based models that are used to monitor technology maturation, mitigate technology program risk, characterize TRL transition times, or model schedule and cost risk for individual technologies, as well as technology systems and portfolios. In the first part of this paper, we develop a theoretical framework to classify those models based on the (often implicit) assumptions they make; we then propose modifications and alternative models to make full use of the assumptions. In the second part, we depart from those assumptions and present a new decision-based framework for cost and schedule joint modeling

Psychological Health in the United States Military: Making Sense of What We Know

Hundreds of thousands of United States military service members are suffering from PTSD and other psychological health conditions as a result of their wartime service. A myriad of possible system interventions and resource allocation schemas have been researched and proposed, but finite budgets and manpower dictate a careful allocation of resources to optimize outcomes. We describe a stock-and-flow model of psychological health treatment tailored to the unique context of the military’s healthcare system. Our model, implemented as a “Management Flight Simulator”, reports the impact of system interventions on areas of stakeholder concern and is designed to communicate complex systemic behaviors to those without domain specific knowledge

LAI and Enterprise Excellence: Presented to Lean Flight Initiative 4

Lean Flight Initiative 4 Conference presentatio

It’s All Rocket Science: On the Equivalence of Development Timelines for Aerospace and Nuclear Technologies

Early in the lifecycle of a system development, systems engineers must execute trade studies to allocate resources between different research and development efforts that are developing technologies to be deployed into the system, and they must prepare risk management plans for the selected technologies. We have been developing a statistical model for schedule and cost uncertainty based on a small number of inputs that are quite objective and are already integrated with technology readiness assessment. An algorithm that transforms Technical Maturity (TM) scores from Department of Energy projects into a Technology Readiness Level (TRL) score was created, allowing us to add data from a US Department of Energy to an existing set of data from NASA. We statistically tested whether the two samples (i.e. the DoE and NASA datasets) were randomly drawn from the same population and concluded that the transition times for developing aerospace and nuclear technologies are very similar

Development, Validation and Implementation Considerations of a Decision Support System for Unmanned & Autonomous System of Systems Test & Evaluation

Test Week 2010, Policies, Programs, People, and Resources presentatio

System design and engineering trade‐off analytics: State of the published practice

The article of record as published may be found at http://dx.doi.org/10.1002/sys.21571System design and engineering involves making decisions involving multiple stakeholders with diverse and, potentially conflicting, objectives. As more and more data become available with digital engineering, big data, and data science, trade-off analytics will be an increasing important tool for engineers. We used a structured literature survey with Web of Science key words and bibliographic, categorical, and bibliometric analysis to answer 14 research questions. As our literature survey demonstrates, trade-off analysis can be found in almost every engineering domain. We provide several insights from the literature survey for educators and practitioners

Gaussian Influence Diagrams

An influence diagram is a network representation of probabilistic inference and decision analysis models. The nodes correspond to variables that can be either constants, uncertain quantities, decisions, or objectives. The arcs reveal probabilistic dependence of the uncertain quantities and information available at the time of the decisions. The influence diagram focuses attention on relationships among the variables. As a result, it is increasingly popular for eliciting and communicating the structure of a decision or probabilistic model. This paper develops the framework for assessment and analysis of linear-quadratic-Gaussian models within the influence diagram representation. The "Gaussian influence diagram" exploits conditional independence in a model to simplify elicitation of parameters for the multivariate normal distribution. It is straightforward to assess and maintain a positive (semi-)definite covariance matrix. Problems of inference and decision making can be analyzed using simple transformations to the assessed model, and these procedures have attractive numerical properties. Algorithms are also provided to translate between the Gaussian influence diagram and covariance matrix representations for the normal distribution.influence diagram, Gaussian decision model, multivariate normal assessment

Creating Executable Agent-Based Models Using SysML

Abstract. Simulation plays an important role in the analysis of alternatives during the early phases of systems engineering activities. This is especially true for endeavors in the system of systems realm where there is even more need for simulation to characterize interdependencies. In this paper, we develop a generic framework to translate a SysML conceptual model to an executable agent-based simulation model. We demonstrate the translation using a simplified air traffic management problem. Along with the potential advantages, we also identify major challenges and possible mismatches in accomplishing a suitable translation for real-world systems of systems

A 35-bp Conserved Region Is Crucial for <i>Insl3</i> Promoter Activity in Mouse MA-10 Leydig Cells

The peptide hormone insulin-like 3 (INSL3) is produced almost exclusively by Leydig cells of the male gonad. INSL3 has several functions such as fetal testis descent and bone metabolism in adults. Insl3 gene expression in Leydig cells is not hormonally regulated but rather is constitutively expressed. The regulatory region of the Insl3 gene has been described in various species; moreover, functional studies have revealed that the Insl3 promoter is regulated by various transcription factors that include the nuclear receptors AR, NUR77, COUP-TFII, LRH1, and SF1, as well as the Krüppel-like factor KLF6. However, these transcription factors are also found in several tissues that do not express Insl3, indicating that other, yet unidentified factors, must be involved to drive Insl3 expression specifically in Leydig cells. Through a fine functional promoter analysis, we have identified a 35-bp region that is responsible for conferring 70% of the activity of the mouse Insl3 promoter in Leydig cells. All tri- and dinucleotide mutations introduced dramatically reduced Insl3 promoter activity, indicating that the entire 35-bp sequence is required. Nuclear proteins from MA-10 Leydig cells bound specifically to the 35-bp region. The 35-bp sequence contains GC- and GA-rich motifs as well as potential binding elements for members of the CREB, C/EBP, AP1, AP2, and NF-κB families. The Insl3 promoter was indeed activated 2-fold by NF-κB p50 but not by other transcription factors tested. These results help to further define the regulation of Insl3 gene transcription in Leydig cells