Simulación de sistemas Multi-Agente mediante un esquema Monte Carlo basado en ecuaciones diferenciales ordinarias

[ES] En este trabajo se expone una metodología para modelar un sistema Multi-Agente (SMA), para que sea equivalente a un sistema de Ecuaciones Diferenciales Ordinarias (EDO), mediante un esquema basado en el método de Monte Carlo. Se muestra que el SMA puede describir con mayor riqueza modelos de sistemas dinámicos con variables cuantificadas discretas. Estos sistemas son muy acordes con los sistemas biológicos y fisiológicos, como el modelado de poblaciones o el modelado de enfermedades epidemiológicas, que en su mayoría se modelan con ecuaciones diferenciales. Los autores piensan que las ecuaciones diferenciales no son lo suficientemente apropiadas para modelar este tipo de problemas y proponen que se modelen con una técnica basada en agentes. Se plantea un caso basado en un modelo matemático de Leucemia Mieloide Crónica (LMC) que se transforma en un SMA equivalente. Se realiza una simulación de los dos modelos (SMA y EDO) y se compara los resultados obtenidos.

Valuing Expansions of the Electricity Transmission Network under Uncertainty: The Binodal Case

Transmission investments are currently needed to meet an increasing electricity demand, to address security of supply concerns, and to reach carbon-emissions targets. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We propose a valuation model that accommodates both physical and economic uncertainties following the Real Options approach. It combines optimization techniques with Monte Carlo simulation. We illustrate the use of our model in a simplified, two-node grid and assess the decision whether to invest or not in a particular upgrade. The generation mix includes coal-and natural gas-fired stations that operate under carbon constraints. The underlying parameters are estimated from observed market data.

Stabilizing to disruptive transition of focal adhesion response to mechanical forces

Strong mechanical forces can, obviously, disrupt cell-cell and cell-matrix adhesions, e.g., cyclic uniaxial stretch induces instability of cell adhesion, which then causes the reorientation of cells away from the stretching direction. However, recent experiments also demonstrated the existence of force dependent adhesion growth (rather than dissociation). To provide a quantitative explanation for the two seemingly contradictory phenomena, a microscopic model that includes both integrin-integrin interaction and integrin-ligand interaction is developed at molecular level by treating the focal adhesion as an adhesion cluster. The integrin clustering dynamics and integrin-ligand binding dynamics are then simulated within one unified theoretical frame with Monte Carlo simulation. We find that the focal adhesion will grow when the traction force is higher than a relative small threshold value, and the growth is dominated by the reduction of local chemical potential energy by the traction force. In contrast, the focal adhesion will rupture when the traction force exceeds a second threshold value, and the rupture is dominated by the breaking of integrin-ligand bonds. Consistent with the experiments, these results suggest a force map for various responses of cell adhesion to different scales of mechanical force. PMID: 20542514

Improved sampling techniques for the direct simulation Monte Carlo method

The direct simulation Monte Carlo (DSMC) method is a widely used approach for flow simulations having rarefied or nonequilibrium effects. It involves heavily to sample instantaneous values from prescribed distributions using random numbers. In this note, we briefly review the sampling techniques typically employed in the DSMC method and present two techniques to speedup related sampling processes. One technique is very efficient for sampling geometric locations of new particles and the other is useful for the Larsen-Borgnakke energy distribution.