Modelling forest fire spread using hexagonal cellular automata

In this paper a new mathematical model for predicting the spread of a fire front in homogeneous and inhomogeneous environments is presented. It is based on a bidimensional cellular automata model, whose cells stand for regular hexagonal areas of the forest. The results obtained are in agreement with the fire spreading in real forests.     

A cellular automata model of chemotherapy effects on tumour growth: targeting cancer and immune cells

The effects of therapy on avascular cancer development based on a stochastic cellular automata model are considered. Making the model more compatible with the biology of cancer, the following features are implemented: intrinsic resistance of cancerous cells along with drug-induced resistance, drug-sensitive cells, immune system. Results are reported for no treatment, discontinued treatment after only one cycle of chemotherapy, and periodic drug administration therapy modes. Growth fraction, necrotic fraction, and tumour volume are used as output parameters beside a 2-D graphical growth presentation. Periodic drug administration is more effective to inhibit the growth of tumours. The model has been validated by the verification of the simulation results using in vivo literature data. Considering immune cells makes the model more compatible with the biological realities. Beside targeting cancer cells, the model can also simulate the activation of the immune system to fight against cancer.

Abbreviations CA: cellular automata; DSC: drug sensitive cell; DRC: drug resistant cell; GF: growth fraction; NF: necrotic fraction; ODE: ordinary differential equation; PDE: partial differential equation; SCAM: The proposed stochastic cellular automata model     

Bridging the gap: From cellular automata to differential equation models for pedestrian dynamics

Cellular automata (CA) and ordinary differential equation (ODE) models compete for dominance in microscopic pedestrian dynamics. There are two major differences: movement in a CA is restricted to a grid and navigation is achieved by moving directly in the desired direction. Force based ODE models operate in continuous space and navigation is computed indirectly through the acceleration vector. We present the Optimal Steps Model and the Gradient Navigation Model, which produce trajectories similar to each other. Both are grid-free and free of oscillations, leading to the conclusion that the two major differences are also the two major weaknesses of the older models.     

Take (smoothed) risks when you are young, not when you are old: How to get the best from your pension plan

Using stochastic modelling, we demonstrate that the best investmentstrategy for the accumulation phase of a defined contributionpension plan is one that limits the range of returns that arecredited to the plan member's account. In particular, we showthat with-profit accumulation programmes which make use of asmoothing fund to smooth out returns over time dominate unit-linkedaccumulation programmes. However, for the distribution phase,we show that it is hard in practice for an investment-linkeddistribution programme to beat the income and security providedby a standard annuity, although we again find that, by avoidingextremely poor outcomes, with-profit distribution programmesdominate unit-linked distribution programmes. Return smoothingby means of a smoothing fund is therefore a valuable featureof any long-term investment programme both during the accumulationand distribution phases.     

New approach for the assignment of new European agricultural subsidies using scores from data envelopment analysis: Application to olive-growing farms in Andalusia (Spain)

More than 60% of olive-growing farms in Andalusia (Spain) would have negative returns without European agricultural subsidies. Agenda 2000 criteria imply that agricultural subsidies currently play the role of enhancing the production quality and the environmental and social values of agriculture. Although the necessity of the modulation of subsidies is stated, the EU regulations do not state which objective criteria should be used or how they should be measured, despite the fact that regulations demand objectivity in this measurement.