Next Generation MEMS Design Tools

2001 ◽  
Author(s):  
Barry Dyne
Keyword(s):  
Design Tools ◽  
Next Generation

A Model for Information Flow in Design

2000 ◽  
Author(s):  
Steven B. Shooter ◽  
Walid T. Keirouz ◽  
Simon Szykman ◽  
Steven Fenves
Keyword(s):  
Information Flow ◽  
Information Exchange ◽  
Computer Aided Design ◽  
Design Tools ◽  
Next Generation ◽  
Levels Of Abstraction ◽  
Design Information ◽  
Storage And Retrieval ◽  
Computer Aided ◽  
Aided Design

Abstract The complexity of modern products and design tools has complicated the exchange of design information. It is widely recognized that the capture, storage, and retrieval of design information is one of the major challenges for the next generation of computer aided design tools. This paper presents a model for the flow of design information that supports a semantics-based approach for developing information exchange standards. The model classifies design information into various types, organizes these types into information states and levels of abstraction, and identifies the various transformations that operate between the information states. The model is then applied to an example of a transmission for a cordless drill.

Context-aware Framework for Supporting Personalisation and Adaptation in Creation of Learning Designs

2012 ◽  
pp. 229-248 ◽  
Author(s):  
Patricia Charlton ◽  
George D. Magoulas
Keyword(s):  
Semantic Web ◽  
User Study ◽  
Digital Technologies ◽  
Point Of View ◽  
Design Tools ◽  
Context Aware ◽  
Learning Design ◽  
Next Generation ◽  
Resource Creation ◽  
High Level

One of the current interests in the field of learning design is to find ways to support teachers who wish to develop designs that incorporate digital technologies. The focus from a pedagogical point of view is to enable teachers to exploit the constructivist potential of digital technologies for learning: those that support learners in discussing, collaborating, and creating user-generated designs. These general requirements align at the high-level with the Semantic Web vision of resource creation, sharing and re-use. Leveraging the Semantic Web developments and exploiting the observation that ontological models can form the domain grounding for context-aware applications, this chapter provides the design of a framework for supporting next generation learning design tools that provide adaptive and personalised experiences. Included in the chapter are the initial findings from the result of a user study of the framework.

scholarly journals In silico design and automated learning to boost next-generation smart biomanufacturing

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Pablo Carbonell ◽  
Rosalind Le Feuvre ◽  
Eriko Takano ◽  
Nigel S Scrutton
Keyword(s):  
In Silico ◽  
Machine Learning Algorithms ◽  
Design Tools ◽  
Next Generation ◽  
In Silico Design ◽  
Automated Generation ◽  
Design Changes ◽  
Automated Learning ◽  
Increasing Demand ◽  
New Generation

Abstract The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by in silico optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.

1477: Durability of Next Generation Flexible Ureteroscopes: Randomized Prospective Study

2004 ◽  
Vol 171 (4S) ◽  
pp. 389-389
Author(s):  
Manoj Monga ◽  
Ramakrishna Venkatesh ◽  
Sara Best ◽  
Caroline D. Ames ◽  
Courtney Lee ◽  
...  
Keyword(s):  
Prospective Study ◽  
Next Generation ◽  
Flexible Ureteroscopes
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