A polymorphic substance is capable of forming a number of different crystalline phases that are referred to as its polymorphs. The critical process that determines the outcome of a crystallization process in a polymorphic system is thought to be the nucleation state, which is the self-assembled stage just prior to the formation of crystals with long-range order. While nucleation is well known to be influenced by macroscopically measurable parameters such as temperature, supersaturation and solvent choice our understanding of the underlying molecular self-assembly processes is very limited. The research described in this thesis explores a new approach to extending our knowledge in this area by the use of a combination of medium throughput crystallisation experiments together with the computation of a range of molecular and solute/solvent descriptors of the system under study.The main objective of the work was to develop a protocol for relating experimental and computational data via artificial neural network (ANN) analysis, to identify significant links between experimental polymorphic outcomes and molecular properties. By creating a model that can predict the polymorphic form in a given experiment it is anticipated that our understanding of links between nucleation and crystallisation will be enhanced through the determining the pivotal properties of a molecule that cause it to form one polymorph over another. The ANN method was developed in the context of the carbamazepine system, applying several statistical techniques to the results of 88 crystallisation experiments, featuring 13 solvents, 3 evaporation rates and 4 temperatures. The results show that this approach allows the formulation of further research hypotheses through examination of the physical meaning of the set of descriptors identified by the ANN approach. Crucially, principal component analysis (PCA) was found to be able to efficiently narrow down large sets of computationally derived descriptors to a manageable set by removing redundancy through strongly cross-correlated parameters. The best ANN model generated in this research was capable of predicting the major polymorphic form in 89 % of cross-validation experiments.The optimised set of descriptors included both solute and solvent properties, which predominantly described the intermolecular interactions in solution. The physical meanings of the descriptors and their impact on the molecular processes during nucleation has been considered and their cross correlation has been examined. Initial results from further experimentation with the tolbutamide and ROY systems indicate that the methodology is also transferable to other polymorphic systems.

GIS has been used in Veterinary Science for a couple of year and the application thereof has been growing rapidly. A number of GIS models have been developed to predict the occurrences of certain types of insect species including the Culicoides species (spp), the insect vectors responsible for the transmission of the African horse sickness (AHS) virus. AHS is endemic to sub-Saharan Africa and is carried by two midges called Culicoides Imicola and Culicoides Bolitinos. The disease causes severe illness in horses and has significant economic impact if not dealt with timeously. Although these models had some success in the prediction of possible abundance of the Culicoides spp. the complicated nature and high number of variables influencing the abundance of Culicoides spp. posed some challenges to these GIS models. This informs the need for models that can accurately predict potential abundance of Culicoides spp to prevent unnecessary horse deaths. This lead the study to the use of a combination of a GIS and an artificial neural networks (ANN) to develop a model that can predict the abundance of C. Imicola and C. Bolitinos. ANNs are models designed to imitate the human brain and have the ability to learn through examples. ANNs can therefore model extremely complex features. In addition, using GIS maps to visualise the predictions will make the models more accessible to a wider range of practitioners. / Dissertation (MSc)--University of Pretoria, 2010. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

The aim of this work is to quantify the concentration of chromic acid (CA) in a saturated solution of chromium trioxide and sodium dichromate using Artificial Neural Networks (ANNs). A set of titration curves was obtained by automated acid-base titration according to a factorial experimental design that was developed for this purpose. These titration curves were divided into three subsets, a learning, training and test set for use by ANNs. Once trained, ANNs have the ability to recognize, generalize and relate the input to a particular output. Concentration of chromic acid (CA), total chromium(VI) and/or dichromate was used as the outputs and titration curves as the inputs to ANNs. Our aim here was to establish whether ANNs would be able to predict the concentration of chromic acid with an absolute error below 1%. For real world problem, the neural networks are only given the inputs and are expected to produce reasonable outputs corresponding to that inputs without any prior ‘knowledge’ about theory involved – here, no interpretation of titration curves was performed by ANNs. The test set of data that was not used for learning process, was used to validate the performance of the neural networks, to verify whether the ANNs learned the input-output patterns properly and how well trained ANNs were able to predict the concentrations of chromic acid, dichromate and total chromium. A number of ANNs models have been considered by varying the number of neurons in the hidden layer and parameters related to the learning process. It has been shown that ANNs can predict the concentration of chromic acid with required accuracy. A number of factors that affect the performance of the neural networks, such as the number of points in a titration curve, number of test points and their distribution within the training set, has been investigated. This work demonstrates that ANNs can be used for online monitoring of an electrolytic industrial process to manufacture chromic acid. / Dissertation (MSc)--University of Pretoria, 2009. / Chemistry / unrestricted

Some of the largest United States bankruptcies of publicly-traded non-financial firms have occurred within the last decade. The continuing need to improve bankruptcy prediction has generated numerous research studies utilizing various prediction models. The purpose of this study is to test the usefulness of the multiple discriminant, probit, and artificial neural network (ANN) models in predicting bankruptcy in the United States textile-related industry. Financial data is examined for 47 bankrupt and 104 non-bankrupt publicly-traded firms in the textile-related industry during the time period 1998-2004, which includes the events of the Asian currency crisis and increased competition from China. Models developed by Altman (1968), Altman (1983), Zmijewski (1984) are compared to ANNs based upon each of these models. A comparison to an ANN including all of the ratios of the previous models and variables for firm size and domestic sales is also made. The Altman (1968) model and ANN 68 model are found to have the higher predictive power for one and two years prior to bankruptcy, respectively, for bankrupt firms. The ANN 84 model and the ANN 83 model have the highest correct classification results for nonbankrupt firms for the entire time period. Solvency and leverage variables appear to have the most impact on the bankruptcy prediction of textile-related firms. The additional variables of firm size and domestic sales are not found to improve the predictive accuracy. This study supports the continued use of the original Altman (1968) model for predicting bankruptcy in a manufacturing industry. Simultaneous utilization of the ANN 83 model to predict nonbankrupt firms is also suggested since the majority of the Altman (1968) variables can be used and the higher potential for improved predictability. This study may be extended to years after 2004 with consideration given to quarterly information, NAICs codes, and leverage variable alternatives.

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Artificial neural networks are demanding to computational power of a computer. Increasing their learning speed could mean new posibilities for research or aplication of the algorithm. And that is a purpose of this thesis. The usage of graphics processing units for neural networks learning is one way how to achieve above mentioned goals. This thesis is offering a survey of theoretical background and consequently implementation of a software library for neural networks learning with a Backpropagation algorithm with a support of acceleration on graphics processing unit.

As the impacts of climate change intensify, businesses are increasingly committing to ambitious sustainable development goals, yet an enduring disconnect remains between corporate sustainability activities and declining global environment and society. This study adopts a complexity view that reductionism associated with Newtonian thinking has played a key role in creating many of the sustainability issues now faced by humanity. This dissertation departs from the premise that sustainability needs to be integrated into an organisation and uses a complexity view to argue that corporate sustainability is a co-evolutionary process of emergence. Whilst many studies have examined how sustainability can be integrated into a business, less is known about corporate sustainability as an emergent process. To address the knowledge gap, this research answered three questions: (1) How does sustainability emerge in financial institutions? (2) What is the role of coherence in the emergence of sustainability? and (3) What conditions enable the emergence of sustainability? A mixed method sequential design was used. In the initial quantitative strand of the research, a holistic business assessment survey based on integral theory was implemented in two financial services organisations in Southern Africa. The results were analysed using self-organising maps and explored in narrative interviews in the subsequent qualitative strand of the research. The study makes three contributions to our understanding of emergence in corporate sustainability. First, by proposing four modes by which corporate sustainability is enacted; these elucidate how integral domains are enacted in corporate sustainability. Second, by clarifying the process of emergence by articulating how zones of coherence emerge between embodied and embedded dimensions. Third, by explaining how the shift to corporate sustainability occurs by means of four conditions. These contributions serve to advance our understanding of corporate sustainability as a fundamental shift in the functioning of an organisation towards coevolutionary self-organisation. It is recommended that corporate sustainability is holistically cultivated to support emergence and self-organisation, rather than being integrated through a linear process of change.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The aim of this article is to use artificial neural networks (ANN) to perform the structural design of confined masonry buildings. ANN is easy to operate and allows to reduce the time and cost of seismic designs. To generate the artificial neural network, training models (traditional confined masonry designs) are used to identify the input and output parameters. From this, the final architecture and activation functions are defined for each layer of the ANN. Finally, ANN training is carried out using the backpropagation algorithm to obtain the matrix of weights and thresholds that allow the network to operate and provide preliminary structural designs with a 10% margin of error, with respect to the traditional design, in the dimensions and reinforcements of the structural elements.

Internal combustion engines, used for light duty transportation, represent a major role in mobility, contributing 28.6% to CO$_2$ emissions worldwide. To mitigate environmental impact and ease the transition to clean technologies, the search for more efficient, less polluting engines has been demanded, and unique tools are necessary to meet the constantly upgraded policies. Hence, data-driven approaches that emulate current vehicles represent a valuable contribution to the improvement of engine performance. Dynamometer tests of commercial engines are open-data, and a dependable source for understanding on-road behavior of several vehicle variables. Artificial neural network (ANN) algorithms, a subset of machine learning, have received considerable attention recently given their wide number of applications and the possibility to provide accurate data-driven approximations. This work describes a methodology for applying ANN’s to predict emissions, efficiency, and fuel consumption in combustion engines using dynamometer test data, and to extrapolate its use in new technologies. The procedure is also applied to a hybrid vehicle case study. The proposed methodology accurately generates ANN’s for the prediction of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC) and emissions in conventional engines with 𝑅$^2$>0.91 and mean absolute errors (MAE) of less than five percent. Using the same approach, the hybrid vehicle state of charge (SOC), and the fuel scale state, are predicted, showing good agreement 𝑅$^2$>0.96 and confirming the versatility of the proposed algorithm. Finally, an initial approach for dealing with missing data in the databases is introduced. Using various simple and iterative imputation methods, it was possible to obtain 𝑅$^2$>0.80 for predicting the BTE and BSFC with five percent of the data missing from the input values.