SimInf: An R package for Data-driven Stochastic Disease Spread Simulations

We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and OpenMP to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goal was to make SimInf extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. In this paper, we provide a technical description of the framework and demonstrate its use on some basic examples. We also discuss how to specify and extend the framework with user-defined models.Comment: The manual has been updated to the latest version of SimInf (v6.0.0). 41 pages, 16 figure

Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks

Nature presents multiple intriguing examples of processes which proceed at high precision and regularity. This remarkable stability is frequently counter to modelers' experience with the inherent stochasticity of chemical reactions in the regime of low copy numbers. Moreover, the effects of noise and nonlinearities can lead to "counter-intuitive" behavior, as demonstrated for a basic enzymatic reaction scheme that can display stochastic focusing (SF). Under the assumption of rapid signal fluctuations, SF has been shown to convert a graded response into a threshold mechanism, thus attenuating the detrimental effects of signal noise. However, when the rapid fluctuation assumption is violated, this gain in sensitivity is generally obtained at the cost of very large product variance, and this unpredictable behavior may be one possible explanation of why, more than a decade after its introduction, SF has still not been observed in real biochemical systems. In this work we explore the noise properties of a simple enzymatic reaction mechanism with a small and fluctuating number of active enzymes that behaves as a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We then show that the inclusion of a plausible negative feedback mechanism turns the system from a noisy signal detector to a strong homeostatic mechanism by exchanging high gain with strong attenuation in output noise and robustness to parameter variations. Moreover, we observe that the discrepancy between deterministic and stochastic descriptions of stochastically focused systems in the evolution of the means almost completely disappears, despite very low molecule counts and the additional nonlinearity due to feedback. The reaction mechanism considered here can provide a possible resolution to the apparent conflict between intrinsic noise and high precision in critical intracellular processes

Parameters and Application in Electric Vehicle Battery Charging Based on State of Power (SoP) and State of Energy (SoE)

Estimation of equivalent circuit parameters and open circuit voltage of a battery to predict its state is important for electric vehicle (EV) applications. There is a need to measure the open circuit voltage as accurately as possible as it mirrors the state-of-charge (SoC) of the battery. As calculation of the SoC by integrating the amount of current going in or out of the battery is inaccurate and requires post-processing, this investigation presents one different way to calculate the open circuit voltage and thus the state of charge while the battery is being used. This paper also presents an analytical model of the state of an EV battery pack with the concept of State of Power (SoP) and State of Energy (SoE). These figures of merits help the user to determine how far a battery pack can be used in terms of the vehicle range and acceleration/deceleration capability. LiFePo4 cells were used as the type of Li-ion battery in this investigation. This paper investigates these aspects with the help of vehicle and battery data obtained experimentally and in laboratory environment. The simulation results have been compared and validated against the experimentally obtained results

Learning Resources in Sustainable Energy (SustEner)

This paper present comprehensive learningresources developed for education in sustainable energy forprofessionals from industry, for teachers and also forstudents. Nine on-line learning modules are available withina modern learning portal. Each module is enriched byremote or virtual experiments that enable the learner to getsome practical experience and better understanding of thepresented theoretical concepts. Outlines of the learningmodules with short description of the remote or virtualexperiments are given