A study of the required sustainability-driven institutional and behavioural mechanisms to tackle the anticipated implications of agricultural water price shocks: a system dynamics approach.

Economic policies for managing agricultural water use are often complicated by the challenge of using water prices as an efficient economic tool when other non-economic concerns are involved in the decision-making process. This study aims to analyse the impact of water pricing policies on preserving agricultural water resources in Iran. This study applies a system dynamics approach to simulate the system performance and behaviour of stakeholders and the economic implications.

Statistical modeling of biochemical detection systems.

We study the problem of counting the number of particles in a closed volume where the particles motion are modeled by a Brownian motion process. This problem arises in many biological and chemical sensing experiments, e.g.

Effects of scaling on the SNR and speed of biosensors.

This work presents a stochastic model for the observed signal of biosensors, a model that predicts the signal fluctuation of the system and the SNR associated with it using a Markov chain process. In the process, transition probabilities are derived from the target and probe binding kinetics in view of statistical motion and random walk events. Based on this model, we are able to estimate the settling time, power-spectral density (PSD), and signal to noise ratio (SNR) of general affinity-based biosensors.