Spectrophotovoltaic orbital power generation

A system with 1000 : 1 concentration ratio is defined, using a cassegrain telescope as the first stage concentration (270 x) and compound parabolic concentrators (CPC) for the second stage concentration of 4.7 x for each spectral band. Using reported state of the art (S.O.A.) solar cells device parameters and considering structural losses due to optics and beamsplitters, the efficiencies of one to four cell systems were calculated with efficiencies varying from approximately 22% to 30%. Taking into account cost of the optics, beamsplitter, radiator, and the cost of developing new cells the most cost effective system is the GaAs/Si system

Hellinger Distance Trees for Imbalanced Streams

Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority class recall rates. In this paper we address this deficiency by proposing the use of the Hellinger distance measure, as a very fast decision tree split criterion. We demonstrate that by using Hellinger a statistically significant improvement in recall rates on imbalanced data streams can be achieved, with an acceptable increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International Conference on Pattern Recognition (ICPR) 201

Nitrogenase activity associated with codium species from New Zealand marine habitats

Nitrogenase activity, measured as acetylene reduction, was recorded at rates up to 1028 nmol.h \g * dry weight for Codium adhaerens (Cabr.) Ag. var. convolutum Dellow and Codium fragile (Sur.) Hariot subsp. tomentosoides (Van Goor) Silva collected from New Zealand habitats. In both species the ability to reduce acetylene is invariably associated with the presence of a heterocystous blue-green alga, Calothrix sp., epiphytic or embedded in the Codium thallus. A highly significant (P < 0.001) correlation between heterocyst frequency and nitrogenase activity was found. Nitrogenase and net photosynthesis of the Codium-Calothrix system have different steady-state responses to light intensity, and the kinetics of the two processes also differ in that nitrogenase is slow to respond to illumination or darkening. Glucose additions to Codium did not significantly increase nitrogenase activity. Nitrogenase is relatively insensitive to oxygen tension over the range 0-1.0 atm (0-1.033 kgf.cnT2) and still occurs at 1.5 atm (1.55 kgf.cm"2); this condition is unique in all nitrogenase systems thus far reported. Collectively these facts suggest that Calothrix is the agent primarily responsible for nitrogenase activity in these Codium species

Encouraging play in the natural environment : a child-focused case study of Forest School

There is concern that children are becoming disengaged from the natural environment and are not being afforded the opportunities to play in such environments. To examine children\u27s perceptions, knowledge and experiences of play in the natural environment, 17 children from one school participated in small focus groups before and after a 12-week Forest School that took place within a school woodland area. Using two qualitative approaches, we found that Forest School had a positive influence on children\u27s natural play and their knowledge of the natural world around them.<br /

Deep Optimisation:Solving Combinatorial Optimisation Problems using Deep Neural Networks

Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper we extend its application to solving optimisation problems. Model Building Optimisation Algorithms (MBOAs), a branch of evolutionary algorithms, have been successful in combining machine learning methods and evolutionary search but, until now, they have not utilised DNNs. DO is the first algorithm to use a DNN to learn and exploit the problem structure to adapt the variation operator (changing the neighbourhood structure of the search process). We demonstrate the performance of DO using two theoretical optimisation problems within the MAXSAT class. The Hierarchical Transformation Optimisation Problem (HTOP) has controllable deep structure that provides a clear evaluation of how DO works and why using a layerwise technique is essential for learning and exploiting problem structure. The Parity Modular Constraint Problem (MCparity) is a simplistic example of a problem containing higher-order dependencies (greater than pairwise) which DO can solve and state of the art MBOAs cannot. Further, we show that DO can exploit deep structure in TSP instances. Together these results show that there exists problems that DO can find and exploit deep problem structure that other algorithms cannot. Making this connection between DNNs and optimisation allows for the utilisation of advanced tools applicable to DNNs that current MBOAs are unable to use

Analytic nuclear forces and molecular properties from full configuration interaction quantum Monte Carlo

Unbiased stochastic sampling of the one- and two-body reduced density matrices is achieved in full configuration interaction quantum Monte Carlo with the introduction of a second, "replica" ensemble of walkers, whose population evolves in imaginary time independently from the first, and which entails only modest additional computational overheads. The matrices obtained from this approach are shown to be representative of full configuration-interaction quality, and hence provide a realistic opportunity to achieve high-quality results for a range of properties whose operators do not necessarily commute with the hamiltonian. A density-matrix formulated quasi-variational energy estimator having been already proposed and investigated, the present work extends the scope of the theory to take in studies of analytic nuclear forces, molecular dipole moments and polarisabilities, with extensive comparison to exact results where possible. These new results confirm the suitability of the sampling technique and, where sufficiently large basis sets are available, achieve close agreement with experimental values, expanding the scope of the method to new areas of investigation.Comment: 11 page