Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references. Also available in print.

The field of statistical research in weather allows for the application of old and new methods, some of which may describe relationships between certain variables better such as temperatures and pressure. The objective of this study was to apply a variety of traditional and novel statistical methods to analyze data from the National Data Buoy Center, which records among other variables barometric pressure, atmospheric temperature, water temperature and dew point temperature. The analysis included attempts to better describe and model the data as well as to make estimations for certain variables. The following statistical methods were utilized: linear regression, non-response analysis, residual analysis, descriptive statistics, parametric analysis, Kolmogorov-Smirnov test, autocorrelation, normal approximation for the binomial, and chi-squared test of independence. Of the more significant results, one was establishing the Johnson SB as the best fitting parametric distribution for a group of pressures and another was finding that there was high autocorrelation in atmospheric temperature and pressure for small lags. This topic remains conducive to future research, and such endeavors may strengthen the field of applied statistics and improve our understanding of various weather entities.

Creating an accurate hurricane location forecasting model is of the utmost importance because of the safety measures that need to occur in the days and hours leading up to a storm's landfall. Hurricanes can be incredibly deadly and costly, but if people are given adequate warning, many lives can be spared. This thesis seeks to develop an accurate model for predicting storm location based on previous location, previous wind speed, and previous pressure. The models are developed using hurricane data from 1980-2009.

The purpose of this thesis is to develop a simple tool that can help designers and researchers obtain general guidelines for buildings in terms of energy usage and LCC. Another objective of this thesis is to apply this tool to residential buildings in order to understand which variables are relevant in terms of energy consumption and LCC costs. A one-story rectangular house was parameterized in terms of five variables: total glazing area; south window-to-wall ratio (WWR); east and west WWR (which are symmetrical for these two facades); insulation width; and window type (ranging from a single clear window to a double low e-clear argon filled window). A high average glazing area (30-40% of floor area) was applied in order to increase energy loads and to augment the importance of the window properties. Simulation was performed through Energy-plus (in conjunction with a code developed especially for this project) for three cities: Austin, Boston, and Seattle. A total of 1055 simulations were run for each city. The experiment showed that only the total glazing area, the E-W WWR and the window types were relevant variables. The former variable is highly correlated with total energy consumption across all cities. Another important conclusion was that each variable's effect on energy consumption worked independently of each other, as there were no considerable differences when analyzing variables individually, as opposed to analyzing them holistically. Results showed that, for Austin and Boston, it was possible to reduce energy loads by 35% and 27% respectively with a double low-e green window (as compared to a single clear window). Similarly, Seattle showed a reduction of 29% for a double low e-clear argon filled window. Nevertheless, the simplest type of window (type 1) presented the best results in terms of LCC. Therefore, we can conclude that only under a high-energy demand situation, such as with office buildings, would it be possible to obtain positive LCC results for double glazed windows. Consequently, double glazed windows will not present positive economical results in typical residential buildings. A second simulation was performed under a tighter HVAC schedule and higher internal loads. In this new scenario, the best windows were the same as with the first simulation, but maximum energy savings were higher: 50%, 34% and 35% for Austin, Boston, and Seattle, respectively. Nevertheless, when considering LCC, a double-clear window presented the best results for Austin, Boston, and Seattle, with 17%, 11%, and 5% reductions in costs respectively compared to the type 1 window. Therefore, if designers are only concerned with costs, the problem of what window to choose becomes non-trivial only for high-energy demand cases. / text

The research was conducted in the light of a training programme which will train a total of 150 engineers of AVIC I in Cranfield University during a period of 3 years. Cost has become an essential driver to aircraft design, as well as performances due to either the limited defence budget or competitive airline market. Consequently, knowing the possible cost prior to making actual expenditure will help managers to make proper decisions and allocate resources efficiently, and designers to optimize their work. Existing aircraft cost estimating models are outdated and mainly based on a database including both military and civil aircraft with various missions. This research concentrated on commercial jet aircraft and was to develop a suitable acquisition cost estimating methodology for conceptual design from a commercial aircraft manufacturer’s perspective. The literature reviewing took a comprehensive overview of some widely-applied cost estimating methods: Analogy, Parametric, Bottom-up, Feature-based costing, Activitybased costing (ABC), Expert judgement, and etc. Some practical cost models were also reviewed to learn the application of cost estimating in the aerospace industry. Then, analogy and parametric approaches were selected to perform the methodology development considering the limited data available at the conceptual design phase. An investigation was deployed to identify the actual problems in practice. The results helped to recognize the needs of industry. Also, the preparation works for development are presented to understand the environment. With subjective judgement and statistical techniques, a series of cost estimating relationships (CERs) were achieved, in which some historic explanatory parameters remained or were eliminated, and some new ones introduced. Size of aircraft became another variable besides weight. As to engines, all developed explanatory variables have been revealed in prior researches. The validation of CERs proves that they can provide reliable cost estimates with high accuracy and can be applied to conceptual design. In addition, a case study was conducted using a baseline aircraft defined in the group design project (GDP) and presents cost forecasting for the proposed aircraft. At last, discussion and conclusion presents an overview of the research. A framework for cost estimating system can be educed. Also, the future work is proposed for in-depth research.

This dissertation provides an account of how computing left behind its origins in academic and military research to become part of the hospital's equipmental setting. I examine the efforts of reformers, including administrators, planners, architects, and computer consultants, to provide appropriate accommodation for modern biomedicine. I explore three stories in order to untangle the admixture of architecture, medicine, and computation as they intertwined through a mutual engagement with automation, operations research, cybernetics, and biomedical research in the postwar hospital. In Boston, pioneering research consultants Bolt Beranek and Newman collaborated with the Massachusetts General Hospital on an experimental total information system known as the Hospital Computer Project. In London, architects Llewelyn Davies Weeks used computer algorithms to help design Northwick Park Hospital. And in Canada, the Montreal Neurological Institute adopted computing to transform its expertise in clinical brain imaging research. When possible, I emphasize specific computers, arguing that attention to the presence of the machine itself contributes to our understanding of hospital life.

PipeSynth is a design automation approach that combines various optimization research and artificial intelligence methods for synthesizing fluid networks. Starting with only the port locations, PipeSynth generates and optimizes the most effective network for a given application. This ideal network is found by not only optimizing the sizes of each pipe and orientation of fittings in the network (parameters), but also optimizing the layouts of how they are all connected (topology). Using Uniform-Cost-Search for topology optimization, and a combination of non-gradient based parametric optimization methods,PipeSynth demonstrates how advances in automated design can enable engineers to manage much more complex fluid network problems. PipeSynth uses a unique representation of fluid networks that synthesizes and optimizes networks one pipe at a time, in three-dimensional space. PipeSynth has successfully solved several problems containing multiple interlaced networks concurrently with multiple inputs and outputs. PipeSynth shows the power of automated design and optimization in producing solutions more effectively and efficiently than traditional design approaches. / text

Inference in generalized linear mixed models (GLMM) remains a topic of debate. Baayen, Davidson, and Bates (2008) outlines criticism against conventional ways of performing inference for GLMMs. There are various alternatives proposed but lit- tle consistency is found on which is the most reasonable. Our focus is on assessing temporal trends for mainly ecological count data. That is, we hope to provide a prag- matic approach to Poisson GLMMs for ecological researchers within the statistical programming environment R. To achieve this, we start by providing a description of the selected estimation and inferential procedures. We then complete a large scale simulation to evaluate each of the estimation methods. We implement a power analy- sis to assess each of the selected inferential procedures. We then go on to apply these procedures to data sampled by The National Parks of Canada. Finally, we conclude by giving a summary of our ?ndings and outlying work for the future.

The following thesis investigates parametric thinking and evolutionary solving principles in the creation of a framework for residential high rise design. Site specific parameters such as site access (pedestrian and vehicular), views, acknowledgement of neighbours, or climatic, such as natural day lighting and ventilation, should be used to inform the design. Parametric software is used as a tool to generate designs dynamically. With the help of an evolutionary solver component, the design potential is augmented by generating multiple iterations which are analyzed for their success or failure in an effort to provide an appropriate response within the context of the site. The framework is tested on a site located at the corner of Duplex Avenue and Eglinton Avenue West in Toronto, Canada.

The availability and use of new materials and construction techniques are allowing bridges to be built that are longer and more slender to those that have been constructed in the past. This can cause bridges to have lower stiffness and damping, and thus be less able to resist dynamic effects. This is of special concern for pedestrian bridges, because the harmonic loads that pedestrians apply to the bridge have the potential to excite the bridge’s natural frequencies. In addition, pedestrians can be sensitive to these vibrations. A model was developed in order to better understand the effects that a pedestrian can have on these vibration-prone bridges. The model consisted of two parts: a finite element model that used the structural data for the bridge in order to produce mass-normalized mode shapes, and a bridge-pedestrian interaction program that used the structural and modal data, along with pedestrian loading scenarios, to generate the bridge response. A parametric study of two bridges was conducted. The bridges included: a short span bridge that would not be expected to respond excessively to pedestrian loads, and a long-span, lively bridge that had natural frequencies in the range of pedestrian loading. Many loading cases were examined by varying the following parameters: load case, number of pedestrians, damping, and pacing frequency. The modal solution was an effective method of finding the bridge responses. It was determined that pedestrian loads can be represented by a simple constant plus sinusoidal load. The excessive vibrations of long and slender bridges could be addressed by increasing damping on susceptible modes. / Thesis (Master, Civil Engineering) -- Queen's University, 2010-05-03 12:36:05.561