One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage.One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS).No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules.Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.

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Somatic hybrid plants regenerated following the fusion of leaf mesophyll protoplasts of Petunia parodii with those isolated from a cell suspension of albino P. inflata. These two species exhibit a unilateral cross-incompatability with a pre-zygotic mode of reproductive isolation preventing hybridizations with P. inflata as the maternal parent. Selection of somatic hybrids relied on the fact that unfused or homokaryon protoplasts of P. parodii did not develop beyond the cell colony stage while those of the putative somatic hybrids and albino P. inflata parent produced callus. Green somatic hybrid calluses were readily identified against the white background of P. inflata following complementation to chlorophyll synthesis proficiency and continued growth in hybrid cells. Shoots, and ultimately flowering plants, were identified as somatic hybrids based on their floral morphology and colour, chromosome number and the fact that they segregated for parental characters. The frequency of somatic hybrid production was comparable to that previously established for two sexually compatible Petunia species.

This paper introduces a new general platform for the simulation of occupants' presence and behaviours. Called No-MASS (Nottingham Multi-Agent Stochastic Simulation platform) the platform takes a selection of well validated stochastic models to generate a synthetic population of agents, predicts their presence and, in the case of residences also their activities and inferred locations, as well as their use of windows, lights and blinds. A social interaction framework is used to emulate negotiations amongst the members of diverse populations. Furthermore, machine learning techniques allow the agents to learn dynamic behaviours that maximise energy and/ or comfort rewards. This is complemented by a belief-desire-intent framework for the representation of less sophisticated behaviours for which data is scarce. Using the Functional Mockup Interface (FMI) co-simulation standard No-MASS is coupled with EnergyPlus: Energy-Plus parses environmental parameters to No-MASS which in turns parses back the energetic consequences of agents behaviours. Simulations demonstrating the range of results that No-MASS can produce are undertaken and presented.

Two cytochemical methods for the localization of acid and alkaline invertases are given. The first is based upon the reduction of a silver complex at two different pH ranges, whilst the second is based upon the tetrazolium reaction and permits quantification of the rate of activity of alkaline invertase activity. The distribution of alkaline invertase activity throughout the root apex of Pisum sativum and the cell wall localization of acid invertase for material excised from tuber tissue of Helianthus tuberosus are both confirmed.