Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Effect of Longâ Term Oral Bisphosphonates on Implant Wound Healing: Literature Review and a Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141603/1/jper0584.pd

Gap Assessment in the Emergency Response Community

This report describes a gap analysis of the emergency response and management (EM) community, performed during the fall of 2009. Pacific Northwest National Laboratory (PNNL) undertook this effort to identify potential improvements to the functional domains in EM that could be provided by the application of current or future technology. To perform this domain-based gap analysis, PNNL personnel interviewed subject matter experts (SMEs) across the EM domain; to make certain that the analyses reflected a representative view of the community, the SMEs were from a variety of geographic areas and from various sized communities (urban, suburban, and rural). PNNL personnel also examined recent and relevant after-action reports and U.S. Government Accountability Office reports