A Graphical Modeling Environment for the Generation of Workflows for the Globus Toolkit

Author(s):  
Francisco Hernández ◽  
Purushotham Bangalore ◽  
Jeff Gray ◽  
Kevin Reilly
Author(s):  
Janis P. Terpenny ◽  
Deepu Mathew

As engineering products become more complicated, collaboration among multi-disciplinary design teams that are separated by location, time and across organizations is becoming an increasingly difficult task. To be effective, collaboration requires exchanging, interpreting and integrating knowledge in various locations. According to a recent study, the cost of this breakdown in knowledge in the automotive industry alone is at least $1 billion per year. There has been a significant amount of research in recent years to improve the accessibility of knowledge during design. Very little has, however, been invested in format, flow and relationships of knowledge to support the process of collaborative distributed design. Progress is particularly lagging for early stages of engineering design, conceptual design, when the need for and payoff of knowledge exchange is the greatest. This paper presents the Integrated Design Environment that is being developed at the Systems Modeling And Realization Technologies (SMART) Lab at the University of Massachusetts, Amherst. This environment facilitates knowledge flow, knowledge capture and reuse with a generalized graphical modeling environment for conceptual modeling and synthesis. The paper first provides a background in conceptual design and knowledge-based engineering followed by an architectural view of the environment and finally an example problem based on the design of a coffee maker to facilitate discussion.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yunqi Bu ◽  
Johannes Lederer

Abstract Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer’s disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer’s patients compared to other subjects.


Author(s):  
Giulio Masetti ◽  
Silvano Chiaradonna ◽  
Felicita Di Giandomenico ◽  
Brett Feddersen ◽  
William H. Sanders

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