Compositional Analysis of Dynamic Bayesian Networks and Applications to Complex Dynamic System Decomposition

Yang, Shah-An; Zhou, Yuchen; Baras, John S.

doi:10.1016/j.procs.2013.01.018

Cited by 14 publications

(23 citation statements)

References 9 publications

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“…a near Gaussian profile). Future improvements to this model and its implementation in the simulation tool are expected to reduce this model even further, as previous implementations of its symmetric reduction principles have achieved orders-of-magnitude reduction in system size (Yang 2011). …”

Section: Medical Drug Screening For Antibiotic Developmentmentioning

confidence: 99%

“…The software package enables the specification of a network of interacting Markov Chains, referred to as Markov Chain Cells in this paper, and of simulating the interactions of these Cells in order to investigate target characteristics of the network. Through reduction techniques utilizing symmetry in the Markov Chain network, highly complex models can be analyzed that would otherwise go beyond the computational capacity of most systems (Yang et al 2011).…”

Section: Medical Drug Screening For Antibiotic Developmentmentioning

confidence: 99%

“…The next-iteration state of each biofilm element is dependent upon its own Cell's current state, the current states of its adjacent Cells, and the current states of the experimental conditions. Overall, the number of possible states for the biofilm model was found to be X = 2*2*3 N according to Bayesian statistics (Yang et al 2011). Through the tool's symmetry reduction methods, the system simulation was condensed from this set of possible states to a model with 0.75*X number of states, a 25% decrease in overall system size.…”

Section: Figure 10 Visual Representation Of the Biofilm Markov Modelmentioning

confidence: 99%

“…By establishing an observer in the tool to track the number of biofilm Cells in each of the three developmental stages, a full spectrum of theoretical biofilm growth characteristics is obtained that agrees with intuitive expectations (i.e., a near Gaussian profile). Future improvements to this model and its implementation in the simulation tool are expected to reduce this model even further, as previous implementations of its symmetric reduction principles have achieved orders-of-magnitude reductions in system size (Yang et al 2011).…”

Section: Figure 10 Visual Representation Of the Biofilm Markov Modelmentioning

confidence: 99%

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Platforms for Engineering Experimental Biomedical Systems

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Ghodssi

et al. 2012

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Abstract. The systematic design, validation, and verification of systems for biomedical experiments in laboratory and clinical applications are complex due to the highly stochastic nature of biological systems. This paper presents a platform framework for the modeling of these biological components in the context of system-level analysis that enables formal validation and verification of biomedical devices. Looking forward, the capabilities of this platform enable the development of more efficient and effective experimental biomedical systems.

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Section: Medical Drug Screening For Antibiotic Developmentmentioning

confidence: 99%

Section: Medical Drug Screening For Antibiotic Developmentmentioning

confidence: 99%

Section: Figure 10 Visual Representation Of the Biofilm Markov Modelmentioning

confidence: 99%

Section: Figure 10 Visual Representation Of the Biofilm Markov Modelmentioning

confidence: 99%

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Platforms for Engineering Experimental Biomedical Systems

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Ghodssi

et al. 2012

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“…Feedstock recycling can lead to polyol recovery by various methods such as hydrolysis, aminolysis, glycolysis, hydroglycolysis. Energy recovery is suitable only for materials which are difficult to recycle such as PU laminates to wood, leather or fabrics [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Thermo-Chemical Decomposition Study of Polyurethane Elastomer Through Glycerolysis Route with Using Crude and Refined Glycerine as a Transesterification Agent

et al. 2017

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Scalable Fabrication of Microcellular Open‐Cell Polymer Foams

2021

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Open‐cell foams are foams with porous, interconnected cellular structure. This unique structure has given open‐cell foams the versatility to be used in different types of applications including acoustics, filtration, membranes, and bioscaffolds. Conventional open‐cell polymer foams comprise mainly thermosetting and/or crosslinked materials; such materials are difficult to process and have limited end‐product recyclability. This article presents a commercially viable molding process to fabricate open‐cell foams using noncrosslinked thermoplastic polypropylene (PP). Highly open‐cell and/or reticulated structures at an open‐cell content of 84% are attained. The strategies employed in molding of PP foam with high open‐cell degree include: 1) use of mold‐opening method to achieve high void fractions; 2) reduction of cell‐wall thickness through increasing the cell densities via controlled crystallization; and 3) use of low viscosity or low melt‐strength polymer resins to promote cell‐wall opening. The molding process proposed may be extended to other semicrystalline thermoplastic materials for fabricating recyclable open‐cell foams.

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Compositional Analysis of Dynamic Bayesian Networks and Applications to Complex Dynamic System Decomposition

Cited by 14 publications

References 9 publications

Platforms for Engineering Experimental Biomedical Systems

Platforms for Engineering Experimental Biomedical Systems

Thermo-Chemical Decomposition Study of Polyurethane Elastomer Through Glycerolysis Route with Using Crude and Refined Glycerine as a Transesterification Agent

Scalable Fabrication of Microcellular Open‐Cell Polymer Foams

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