SUMMARY1. Vasopressin and oxytocin have been measured in the neurohypophysis of rats which had either been given a 2 % (w/v) NaCi solution to drink or been deprived of drinking water for up to 5 days.2. There was a progressive decrease in the vasopressin content of the glands in both groups; the rates ofdepletion were not significantly different.3. Oxytocin also decreased progressively in both groups but the glands from the 'saline-treated' rats contained less hormone than those from the 'water-deprived' animals.4. The differences in gland content of oxytocin could not be correlated with differences between the plasma osmolalities or sodium concentrations of the two groups.

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.

Supply control has an important part to play in harm reduction; however, proponents of supply-side drug law enforcement need to be mindful of the unintended adverse consequences that might flow from successfully disrupting the market for a particular illegal drug.

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Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.