Continuous execution of system dynamics models on input data stream

This article describes a new approach for system dynamics models execution. In most cases when model execution is involved it is performed on a set of static and known data, which are sent to the model as an input. And it is expected, that on the model output modeler will get a set of other system or event characteristics, computed by the model based on the input parameters. This approach still has the widest usage, but it is not the only one scenario, which is demanded by different industries. With growing popularity of concepts such as Internet of Things, demand in modeling based solutions, which take as input continuous data streams, has grown significantly. In comparison with stand-alone client-side modeling systems, cloud-based solutions, such as sdCloud, became a reasonable answer to such industry request. Such systems can provide an ability of continuous execution of system dynamics models. In other words, these systems are ready to accept an incoming data stream and perform model execution that will result in streaming modeling results back to the end-user. Running system dynamics models in parallel with the process it is describing allows to perform predictive modeling of the system status in the future, and it also allows to find additional hidden external impacts to the model. For example, such approach can be a base for predictive maintenance of complicated technical systems, because it allows computing nearest maintenance time more efficient

Effective Execution of Systems Dynamics Models

This article describes a new project - sdCloud, which was designed in purpose to create an effective cloud-based execution environment for System Dynamics models. The article contains overview of basic terms and conditions in System Dynamics, description of sdCloud project and investigations of issues, which has occurred in development process, including integration of existing System Dynamics tools, possibilities of optimization processes in System Dynamics modeling and its basic approaches

New insight into formation of DNA-containing microparticles during PCR: the scaffolding role of magnesium pyrophosphate crystals

<p>This work aims to study molecular mechanisms involved in the formation of DNA-containing microparticles and nanoparticles during PCR. Both pyrophosphate and Mg²⁺ ions proved to play an important role in the generation of DNA microparticles (MPs) with a unique and sophisticated structure in PCR with <i>Taq</i> polymerase. Thus, the addition of <i>Tli</i> thermostable pyrophosphatase to a PCR mixture inhibited this process and caused the destruction of synthesized DNA MPs. Thermal cycling of Na-pyrophosphate (Na-PPi)- and Mg²⁺-containing mixtures (without DNA polymerase and dNTPs) under the standard PCR regime yielded crystalline oval or lenticular microdisks and 3D MPs composed from magnesium pyrophosphate (Mg-PPi). As shown by scanning electron microscopy (SEM), the produced Mg-PPi microparticles consisted of intersecting disks or their segments. They were morphologically similar but simpler than DNA-containing MPs generated in PCR. The incorporation of dNTPs, primers, or dsDNA into Mg-pyrophosphate particles resulted in the structural diversification of 3D microparticles. Thus, the unusual and complex structure of DNA MPs generated in PCR is governed by the unique feature of Mg-pyrophosphate to form supramolecular particles during thermal cycling. We hypothesize the Mg-pyrophosphate particles that are produced during thermal cycling serve as scaffolds for amplicon DNA condensation.</p