Stochastic Petri Net
Stochastic Petri Net - Related Videos
09:36
Direct Stochastic Optical Reconstruction Microscopy of Extracellular Vesicles in Three Dimensions
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Cited by 8 •
2021
Direct stochastic optical reconstruction microscopy (dSTORM) is used to bypass the typical diffraction limit of light microscopy and to view exosomes at the nanometer scale. It can be employed in both two and three dimensions to characterize exosomes.
14:23
Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
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Cited by 6 •
2018
The overall goal of this methodology is to give the optimal experimental conditions from sample preparation to image acquisition and reconstruction in order to perform 2D dual color dSTORM images of microtubules and intermediate filaments in fixed cells...
00:06
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
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Cited by 5 •
2019
Galvanic vestibular stimulation in humans exhibits improvements in vestibular function. However, it is unknown how these effects occur. Here, we describe how to apply sinusoidal and stochastic electrical noise and evaluate appropriate stimulus amplitudes in individual medial vestibular nucleus neurons in the C57BL/6 mouse.
14:09
High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
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Cited by 7 •
2019
Chip-based super-resolution optical microscopy is a novel approach to fluorescence microscopy and offers advantages in cost effectiveness and throughput. Here, the protocols for chip preparation and imaging are shown for TIRF microscopy and localization-based super-resolution microscopy.
09:27
Multicellular Human Alveolar Model Composed of Epithelial Cells and Primary Immune Cells for Hazard Assessment
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Cited by 16 •
2020
Presented here is a protocol for primary human blood monocyte isolation as well as their differentiation into macrophages and dendritic cells and assembly with epithelial cells into a multicellular human lung model. Biological responses of cocultures composed of immune cells differentiated from either freshly isolated or thawed monocytes, upon exposure to proinflammatory stimuli, are compared.
FAQs
With exponential timing, each timed transition receives a rate that represents its probabilistic firing behavior. When a transition fires, the net moves from one marking to another, creating a state-to-state process that can be represented as a continuous-time Markov chain. This representation gives engineers a basis for analyzing performance, reliability, capacity, and fault-propagation behavior.
The marking records the current distribution of tokens across places, showing which conditions or resources are presently represented. Transitions use that state to represent events, while shared places or synchronization allow interacting processes to be modeled together. This makes the approach useful when concurrent activities compete for resources or must coordinate before the system changes state.
An ordinary Petri net represents system structure and state changes, whereas a Stochastic Petri Net adds uncertain event timing through probability distributions, commonly exponential rates. This extension preserves the representation of conditions, resources, and transitions while making timing behavior analyzable. Engineers can therefore study not only which states are reachable, but also quantitative outcomes associated with random event occurrence.
Engineers first represent system conditions and resources as places, current states as token markings, and uncertain events as timed transitions. They then assign probability distributions or rates to the transitions and translate the resulting behavior into a continuous-time Markov chain. Quantitative analysis of that chain can evaluate performance, reliability, capacity, or fault propagation for the modeled system.
Applications include communication networks, manufacturing systems, computer architectures, and other concurrent processes. The method is especially relevant when system behavior depends on resource sharing, synchronization, delays, or uncertainty. Modeling these features in one framework helps engineers evaluate alternative designs and understand how random event timing may influence system-level outcomes.
The resulting analysis can provide quantitative information about system performance, reliability, capacity, and fault propagation. These outcomes help connect the modeled state changes to engineering decisions, such as evaluating whether a design can handle shared resources, coordinated activities, delays, or uncertain events. The same framework can therefore support both operational assessment and system-design studies.