This research investigates three approaches to new product sales forecasting: statistical, judgmental and the integration of these two approaches. The aim of the research is to find a simple, easy-to-use, low cost and accurate tool which can be used by managers to forecast the sales of new products. A review of the literature suggested that the Bass diffusion model was an appropriate statistical method for new product sales forecasting. For the judgmental approach, after considering different methods and constraints, such as bias, complexity, lack of accuracy, high cost and time involvement, the Delphi method was identified from the literature as a method, which has the potential to mitigate bias and produces accurate predictions at a low cost in a relatively short time. However, the literature also revealed that neither of the methods: statistical or judgmental, can be guaranteed to give the best forecasts independently, and a combination of them is the often best approach to obtaining the most accurate predictions. The study aims to compare these three approaches by applying them to actual sales data. To forecast the sales of new products, the Bass diffusion model was fitted to the sales history of similar (analogous) products that had been launched in the past and the resulting model was used to produce forecasts for the new products at the time of their launch. These forecasts were compared with forecasts produced through the Delphi method and also through a combination of statistical and judgmental methods. All results were also compared to the benchmark levels of accuracy, based on previous research and forecasts based on various combinations of the analogous products’ historic sales data. Although no statistically significant difference was found in the accuracy of forecasts, produced by the three approaches, the results were more accurate than those obtained using parameters suggested by previous researchers. The limitations of the research are discussed at the end of the thesis, together with suggestions for future research.

The development and improvement of methods used for the estimation of the postmortem interval (PMI) is a common area of research in forensic science. This research was conducted to physically and chemically analyze pig carcass decomposition on a soil surface using conventional and newly developed methods for the potential use in estimating the PMI. Photographs of pig carcasses decomposing on forested and open land were scored using a decomposition scoring system and decomposition scores were related to accumulated degree days (ADD). Overall, the ADD values were significantly different for the two groups of carcasses; however, the ADD values for the onset of each score demonstrated more similarity between groups. Decomposition scoring results also indicated that refinements must be made to the calculation of ADD to allow for a meaningful comparison of pig and human decomposition. The decomposition of pig carcasses altered the water content, pH and fatty acid content of soil. The fatty acids, myristic, palmitic, palmitoleic, stearic and oleic acids were successfully extracted and analyzed from decomposition soil. Palmitic, stearic and oleic acids were the most abundant fatty acids detected whilst the levels of myristic and palmitoleic acids were negligible in comparison. A three peak fatty acid cycle was also observed for each fatty acid. Variations in soil pH and fatty acid content of decomposition soil have the potential to indicate the presence of a decomposition site. Furthermore, a nonlinear diffusion model was developed to predict the development of the cadaver decomposition island (CDI) in soil over time. The simulation of the model indicated that the diffusion model has the potential to generate PMI estimations for early stages of decomposition by corresponding the effective radius of the CDI to a particular time point. The general findings of this research indicate that more accurate methods for PMI estimations can potentially be developed with further research. / UOIT

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The increasing use of visible and near infrared light in therapeutic and diagnostic techniques has created a need to model its propagation in tissue. One of the fundamental objectives of such a model is the noninvasive evaluation of the optical properties of tissue. The focus of this thesis was the development of the diffusion approximation in the semi-infinite, slab, cylindrical and spherical geometries. This development required the derivation of approximate boundary conditions which included the zero, extrapolated and partial current boundary conditions. Calculations of the fluence and its related quantities arising from the extrapolated boundary condition were found to be in excellent agreement with the results of the more rigorous partial current boundary condition. A preliminary evaluation of the validity of diffusion theory was performed by comparing its predictions to exact analytical calculations of the fluence in an infinite medium as well as Monte Carlo simulations of the reflectance and transmittance in 1-dimensional planar geometries. In all cases the agreement at late times was excellent. A practical test of the diffusion model was accomplished with the analysis of the reflectance data from a phantom of known optical properties in both the semi-infinite and slab geometries. The model performed well at low concentrations of added absorber, but a considerable discrepancy was found at the highest concentration. A systematic examination of the accuracy of the diffusion model as a function of the fundamental parameters is required to resolve this inconsistency. Approximate expressions describing the equivalent information in the frequency domain were also developed for a semi-infinite medium. These expressions were then used to analyze the phase and modulation obtained from phantoms of known optical properties. Once again reasonable results were obtained at low concentrations of added absorber while a significant discrepancy arose at the highest concentration. The resolution of these discrepancies requires further investigation. / Thesis / Master of Engineering (ME)

Combitech AB was interested in how to automate the process of annotating aluminum scrap when it was adjacent to other metals. This was to ultimately create an annotated dataset that could be utilized for training a segmentation model. The idea was to make use of generative models to generate samples of general scrap metals. Then, with this model, introduce a small dataset of only aluminum, to try to change the features into a domain suitable for aluminum. Since the contents of the samples were generated separately, the system would know where the aluminum was and could then annotate it.  This master's thesis aimed to investigate whether it was possible to construct generative models to generate these samples and see if they had realistic characteristics. It was also investigated if it was possible to get a meaningful model based on a relatively small dataset (aluminum in this case). The data used were two datasets, one with general scrap metal (excluding aluminum) and the other containing only aluminum scrap. Unconditional diffusion models were utilized as generative models. The scrap model achieved satisfactory results, making it possible to generate samples that carried similar properties as the real scrap dataset. When it came to aluminum, which had a much smaller dataset than the scrap dataset, it was possible to get promising results when utilizing transfer learning. However, the same good quality as the scrap model gave was not achieved. This master's thesis has shown that it is possible to get a model to generate realistic-looking images of scrap metal. Furthermore, this scrap model served as a good base when training other generative models to generate images of metals, even if the provided datasets were small. In this way, a foundation was laid for an investigation of an automatic annotation system.

Drinking water distribution networks such as South East Water Ltd. (SEWL), Melbourne Water, Sydney Water, etc. are supposed to transport only dissolved matter rather than a few visible particles. However, it is almost impossible to make the drinking water free from suspended solid particles. The ability to determine the origins of these particles varies between different water supply systems, with possible sources being from catchment, treatment processes, biofilm growth within the water supply pipes, and corrosion products. Improvement of our understanding of the complex hydrodynamic behavior of suspended and/or deposited particles involved in these distribution pipe networks requires mathematical and physical models. Computational Fluid Dynamics (CFD) along with analytical turbulent model is one of the most popular mathematical techniques, which has the ability to predict the behavior of complex flows for such multiphase flow applications. This study has been completed mainly in two steps. A CFD investigation was carried out to predict the hydrodynamic behavior of turbid particle flowing through a horizontal pipe networks including loop consist of bends and straight pipes. Furthermore, an extended analytical model was re-developed for the liquid-solid system to look at the similar behavior of the solid particles flowing in a turbulent field. These two parallel studies will provide better understandings about the turbidity spikes movements in the distribution networks. A comprehensive CFD investigation was carried out for particle deposition in a horizontal pipe loop consisting of four 900 bends in a turbulent flow field. A satisfactory agreement was established with the experimental data as validation. This was a steady state multi-particle problem, which helped to understand the deposition characteristics for different particle sizes and densities at upstream and downstream sides of the bends as well as its circumference. Particle concentration was seen high at the bottom wall in the pipe flow before entering the bends, but for the downstream of bend the deposition was not seen high at the bottom as seen in upstream of bend rather inner side of the bend wall (600 skewed from bottom). The larger particles clearly showed deposition near the bottom of the wall except downstream. As expected, the smaller particles showed less tendency of deposition and this was more pronounced at higher velocity. Due to the high stream line curvature and associated centrifugal force acting on the fluid at different depths the particles became well mixed and resulted in homogeneous distribution near the bend regions. The hydrodynamic behavior of particles flowing in a turbulent unsteady state flowing through a horizontal pipe was also studied to compare with the drinking water distribution networks data. In this numerical simulation six different flow-profiles and particle-load profiles were used to compute particles deposition and re-entrainment into the systems and to identify the conditions of the deposition and suspension mechanisms. Results showed that after a certain length of pipe and period of time after downward velocity gradient, when the velocity was constants over time, the shear stress was sufficiently high enough to cause the particle deposition on and roll along the bottom wall of pipe wall and created a secondary group of particle peak (called kink). Finally, an extended analytical Turbulent Diffusion Model for liquid-solid phase was developed following an existing gas-liquid turbulence model. This turbulent diffusion model was then compared with the results of the CFD investigation making use of the same boundary conditions. The comparison established good agreement between these two models. The influence of velocity on the particle size distribution was dominant over the influence of the superficial liquid velocity, which was also explained by using the new parameter, velocity ratio. This velocity ratio was defined as the ratio between the free flight and gravitational velocity. Due to some inevitable assumptions used in the analytical model, the results showed typically less deposition as compared with the CFD investigation.

To make a High Voltage Direct Current (HVDC) transmission more energy efficient, the voltage of the system has to be increased. To allow for that the components of the system must be constructed to handle the increases AC and DC stresses that this leads to. One key component in such a transmission is the HVDC converter transformer. The insulation system of the transformer usually consists of oil and oil-impregnated pressboard. Modeling of the electric DC field in the insulation system is currently done with the ion drift diffusion model, which takes into account the transport and generation of charges in the oil and the pressboard. The model is however lacking a description of how charges are being injected from the electrodes and the oil-pressboard interfaces. The task of this thesis work was to develop and implement a model for this which improves the result of the ion drift diffusion model. A theoretical study of ion injection was first carried out and proceeding from this, a model for the ion injection was formulated. By using experimental data from 5 different test geometries, the injection model could be validated and appropriate parameter values of the model could be determined. By using COMSOL Multiphysics®, the ion drift diffusion model with the injection model could be simulated for the different test geometries. The ion injection gave a substantial improvement of the ion drift diffusion model. The positive injection from electrodes into oil was found to be in the range 0.3-0.6 while the negative injection was 0.3 lower. Determination of the parameters for the injection from oil-pressboard interfaces proved to be difficult, but setting the parameters in the range 0.01-1 allowed for a good agreement with the experimental data. Here, a fit could be obtained for multiple assumptions about the set of active injection parameters. Finally it is recommended that the investigation of the ion injection continues in order to further improve the model and more accurately determine the parameters of it. Suggestions on how this work could be carried out are given in the end.

This thesis includes three chapters on electricity and natural gas prices. In the first chapter, we give a brief introduction to the characteristics of power prices and propose a mean reversion jump diffusion model, in which jump intensity depends on temperature data and overall system load, to model electricity prices. Compared to the models used in the literature, we find the model proposed in this chapter is better to capture the tail behavior in the electricity prices. In the second chapter, we use the model proposed in the first chapter to simulate the spark spread option and value the power generations. In order to simulate power generation, we first propose and estimate mean reversion jump diffusion model for natural gas prices, in which jump intensity is defined as a function of temperature and storage. Combing the model with the electricity models in chapter 1, we find that the value of power generation is closer to the real value of the power plants as reflected in the recent market transaction than one obtains from many other models used in literature. The third chapter investigates extremal dependence among the energy market. We find a tail dependence that exceeds the Pearson correlation ρ, which means the traditional Pearson correlation is not appropriate to model tail behavior of oil, natural gas and electricity prices. However, asymptotic dependence is rejected in all pairs except Henry Hub gas return and Houston Ship Channel gas return. We also find that extreme value dependence in energy market is stronger in bull market than that in bear market due to the special characteristics in energy market, which conflicts the accepted wisdom in equity market that tail correlation is much higher in periods of volatile markets from previous literature.

The purpose of this paper is to explore the information content in a trading, confirm the relationship between information-trading probability (PIN) and asset returns, and apply PIN to construct an investing strategy on a point of uninformed trader¡¦s view. I develop a decision marking model about trading decision between under order-driven market which is combined on the decision tree of the concept of D. Easley et al. (1997) and Merton (1976) jump diffusion model for modifying the PIN model to apply to order-driven market. As a result, the daily PIN were positive relatively with return, and the investing strategy which was based my model could make profit significantly in the sample period at TWSE in 2003, this investing strategy can earn profit in down and up market condition both. This result supports that hedging against information asymmetric risk is potential.

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