The reproductive potential of a sheep system is much reduced by the failure of released ova to be fertilized and to survive to parturition. This paper looks at currently available information on fertilization failure and early embryonic deaths. Separate hypotheses are proposed for fertilization and for early embryonic mortality. For fertilization, the hypothesis is that a ewe that mates with a ram will end up with either all or none of her released ova fertilized. For embryonic mortality, the hypothesis is that the survival of a fertilized ovum depends only on how many ova were released with it and is independent of the survival or death of those released with it. A mathematical model is constructed on these hypotheses and its predictions are compared with published experimental results of other workers.
INTRODUCTIONIn the process of constructing a model of the population and nutritional characteristics of a sheep system, several sub-models have been investigated. One of these is a model for fertilization failure and embryonic mortality, the greatest causes of wastage of potential lambs that exist in a typical sheep system. An investigation of the literature showed that, although considerable progress has been made in experimental techniques for estimations of the total prenatal mortality in sheep, still many of the results appear at first sight to be contradictory, and it is difficult to fit them into a single conceptual framework. The model described below attempts a simple description in mathematical terms of the biological situation for fertilization and embryonic mortality and follows the consequences of the assumptions made, in order to assess the predictions of the model against published experimental results.
In complex planning and control operations and tasks like manipulating objects, assisting experts in various fields, navigating outdoor environments, and exploring uncharted territory, modern robots are designed to complement or completely replace humans. Even for those skilled in robot programming, designing a control schema for such robots to carry out these tasks is typically a challenging process that necessitates starting from scratch with a new and distinct controller for each task. The designer must consider the wide range of circumstances the robot might encounter. This kind of manual programming is typically expensive and time consuming. It would be more beneficial if a robot could learn the task on its own rather than having to be preprogrammed to perform all these tasks. In this paper, a method for the path planning of a robot in a known environment is implemented using Q-Learning by finding an optimal path from a specified starting and ending point.
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