Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.

Data Preparation for the MOLAND Model Application for the Greater Dublin Region

This paper presents the data preparation and processing steps that were taken to provide inputs for the MOLAND model application for the Greater Dublin Region. The model requires spatial and socioeconomic data by county for the beginning and end years of the calibration period i.e. 1990, 2000 and 2006. In addition, projections of socio-economic variables are required for implementing different scenarios. Basic data requirements of the new transport model and description of related data collection works are also presented. Heretofore detailed information and justification for approaches taken in preparing data for ingestion to MOLAND has been undocumented. This paper aims to address that gap. Therefore the steps that have been taken to prepare and process these datasets are described in detail including background information, interpretation and processing methods used and the main assumptions and generalisations adopted

Air quality and MOLAND : description of a methodology to determine emissions output and affected populations

In recent years, awareness of air quality issues in urban areas has increased significantly. The negative consequences for human health as a result of poor air quality have resulted in numerous laws being enacted both in Ireland and around the world, in an attempt to limit exposure. Within the urban environment, motor vehicles are one of the most significant contributors to pollution. As a result, constant monitoring takes place but little work is done on predicting likely changes in air quality, for better or worse, as a result of changes in landuse or transport. The MOLAND model can provide the basis for an emissions estimation methodology to predict changes in vehicular emissions, and consequently air quality, both now and in the future, enabling policy makers and planners to examine the air quality implications of their decisions.Other funderEnvironmental Protection Agencyti,ke.kpw6/10/1

Analysing climate impact on energy demand using the MOLAND model

The importance and contribution of climate to energy demand are discussed. A linear regression model is developed to analyse future energy demand corresponding to climate change. The methodology for spatial analysis and integration to MOLAND are also provided in order to investigate possible consequences of different urban development paths on energy consumption patterns.Other funderEnvironmental Protection Agencyau,ti,ke.kpw6/10/1

Does MOLAND work as a tool for the assessment and/or as a predictor of the distribution of urban biodiversity in Dublin city?

The MOLAND project's aim is to provide spatial planning tools that can be used to assess, monitor and model the development of urban and regional environments. To date it has been applied to an area of over 70,000km2 across Europe. MOLAND's use as a tool for the assessment and forecasting of biodiversity is examined with Dublin city as a case study. It was found that, in its current form, the MOLAND model's use as a biodiversity assessment tool was limited. Several recommendations are made for improvements to its capability in this area.Other funderEnvironmental Protection Agencyti, ke, se - AL 28/07/201

Analysing climate impact on energy demand using the MOLAND model

The importance and contribution of climate to energy demand are discussed. A linear regression model is developed to analyse future energy demand corresponding to climate change. The methodology for spatial analysis and integration to MOLAND are also provided in order to investigate possible consequences of different urban development paths on energy consumption patterns.Other funderEnvironmental Protection Agencyau,ti,ke.kpw6/10/1

Does MOLAND work as a tool for the assessment and/or as a predictor of the distribution of urban biodiversity in Dublin city?

The MOLAND project\u27s aim is to provide spatial planning tools that can be used to assess, monitor and model the development of urban and regional environments. To date it has been applied to an area of over 70,000km2 across Europe. MOLAND\u27s use as a tool for the assessment and forecasting of biodiversity is examined with Dublin city as a case study. It was found that, in its current form, the MOLAND model\u27s use as a biodiversity assessment tool was limited. Several recommendations are made for improvements to its capability in this area.Other funderEnvironmental Protection Agencyti, ke, se - AL 28/07/201

The Impact of Cooperative Membership on Fish Farm Households’ Income: The Case of Ghana

The emergence of agricultural cooperatives is extensively viewed as a necessary institutional arrangement that can help farmers in developing countries overcome the constraints that impede them from improving sustainable agricultural production and acquiring new marketing opportunities. Therefore, this study examines the determinants of cooperative membership and its impact on fish farm household income, using data collected from two regions in Ghana. An endogenous switching regression (ESR) model is utilized to address the potential sample selection bias issue. The results show that household heads’ decisions to join cooperatives are affected by their access to credit, off-farm work, education level, and peer influence. Cooperative membership can increase both household and farm income by 28.54% and 34.75%, respectively. Moreover, we show that different groups of households’ cooperative impacts on farm and household income are heterogeneous. Our findings highlight the importance of cooperative patronization and provide implications that can improve households’ welfare

The Impact of Cooperative Membership on Fish Farm Households’ Income: The Case of Ghana

The emergence of agricultural cooperatives is extensively viewed as a necessary institutional arrangement that can help farmers in developing countries overcome the constraints that impede them from improving sustainable agricultural production and acquiring new marketing opportunities. Therefore, this study examines the determinants of cooperative membership and its impact on fish farm household income, using data collected from two regions in Ghana. An endogenous switching regression (ESR) model is utilized to address the potential sample selection bias issue. The results show that household heads’ decisions to join cooperatives are affected by their access to credit, off-farm work, education level, and peer influence. Cooperative membership can increase both household and farm income by 28.54% and 34.75%, respectively. Moreover, we show that different groups of households’ cooperative impacts on farm and household income are heterogeneous. Our findings highlight the importance of cooperative patronization and provide implications that can improve households’ welfare