The Kineticist's Workbench: Combining Symbolic and Numerical Methods in the Simulation of Chemical Reaction Mechanisms

The Kineticist's Workbench is a program that simulates chemical reaction mechanisms by predicting, generating, and interpreting numerical data. Prior to simulation, it analyzes a given mechanism to predict that mechanism's behavior; it then simulates the mechanism numerically; and afterward, it interprets and summarizes the data it has generated. In performing these tasks, the Workbench uses a variety of techniques: graph- theoretic algorithms (for analyzing mechanisms), traditional numerical simulation methods, and algorithms that examine simulation results and reinterpret them in qualitative terms. The Workbench thus serves as a prototype for a new class of scientific computational tools---tools that provide symbiotic collaborations between qualitative and quantitative methods.

Maygen: A Symbolic Debugger Generation System

With the development of high-level languages for new computer architectures comes the need for appropriate debugging tools as well. One method for meeting this need would be to develop, from scratch, a symbolic debugger with the introduction of each new language implementation for any given architecture. This, however, seems to require unnecessary duplication of effort among developers. This paper describes Maygen, a "debugger generation system," designed to efficiently provide the desired language-dependent and architecture-dependent debuggers. A prototype of the Maygen system has been implemented and is able to handle the semantically different languages of C and OPAL.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Lean Enterprise Transformation: Measuring and Accelerating the transition to lean.

PowerPoint presentation that showcases: • Research Objectives • Strategic Value of the Lean Enterprise • Multi-Stakeholder Value Optimization • Lean Enterprise Self-Assessment Tool (LESAT) • Leading and Lagging Indicators of Lean Enterprise Transformation • Empirical Results in the Aerospace Industry • Accelerating the Lean Transformation - Linking LESAT to Strategic Objectives • Summary and Questions

Lean Transition of Emerging Industrial Capability

This volume of the final report documents the technical work performed from December 1998 through December 2002 under Cooperative Agreement F33615-97-2-5153 executed between the U.S. Air Force, Air Force Research Laboratory, Materials and Manufacturing Directorate, Manufacturing Technology Division (AFRL/MLM) and the McDonnell Douglas Corporation, a wholly-owned subsidiary of The Boeing Company. The work was accomplished by The Boeing Company, Phantom Works, Huntington Beach, St. Louis, and Seattle; Ford Motor Company; Integral Inc.; Sloan School of Management in the Massachusetts Institute of Technology; Pratt & Whitney; and Central State University in Xenia, Ohio and in association with Raytheon Corporation. The LeanTEC program manager for AFRL is John Crabill of AFRL / MLMP and The Boeing Company program manager is Ed Shroyer of Boeing Phantom Works in Huntington Beach, CA. Financial performance under this contract is documented in the Financial Volume of the final report.

Lean Transition of Emerging Industrial Capability (LeanTEC)

Lean Transition of Emerging Industrial Capability (LeanTEC) program was a cooperative agreement between the Boeing Company and AFRL conducted from January 1998 to January 2002. The results of this program are documented in the Manual for Effective Technology Transition Processes included as an attachment to this report. This manual provides processes, procedures, and tools for greatly improving technology transition in the aerospace industry. Methodology for the implementation of these improvements is given along with methods for customizing the various processes, procedures, and tools for a given company or business unit. The indicated methodology was tested by the LeanTEC team and results are documented in the report.