title: A discriminative approach to structured biological data

authors: Mutter, Stefan

subjects: computer science

abstract: This paper introduces the first author’s PhD project which has just got out of its initial stage. Biological sequence data is, on the one hand, highly structured. On the other hand there are large amounts of unlabelled data. Thus we combine probabilistic graphical models and semi-supervised learning. The former to handle structured data and latter to deal with unlabelled data. We apply our models to genotype-phenotype modelling problems. In particular we predict the set of Single Nucleotide Polymorphisms which underlie a specific phenotypical trait.

text: A discriminative approach to structured biological data This paper introduces the first author’s PhD project which has just got out of its initial stage. Biological sequence data is, on the one hand, highly structured. On the other hand there are large amounts of unlabelled data. Thus we combine probabilistic graphical models and semi-supervised learning. The former to handle structured data and latter to deal with unlabelled data. We apply our models to genotype-phenotype modelling problems. In particular we predict the set of Single Nucleotide Polymorphisms which underlie a specific phenotypical trait.

year: 2007

title: Utilising risk analysis to predict post-earthquake functionality of critical healthcare facilities

authors: Boston, Megan

abstract: Hospitals, particularly emergency departments, are vital to a community’s resilience and earthquake recovery by providing emergent and ongoing critical care. Building standards and codes have higher structural requirements for hospital structures to help ensure continued functionality; nevertheless, hospitals are often damaged, evacuated or closed following design-level earthquakes. While post-earthquake hospital functionality has been the focus of recent research, it remains challenging to predict hospital functionality in a relevant and meaningful manner. Existing methods to quantify functionality are based on patient wait time, bed count, or available services. This work focuses on improving existing hospital functionality frameworks to predict various levels of hospital functionality using a combination of fault and event trees to provide a probabilistic prediction of different levels of functionality based on available services and bed counts for hospital emergency departments and whole hospitals. Fault and event trees were created using published data on hospital damage, evacuations, and operations following earthquakes and combined with international guidelines on hospital requirements and local hospital emergency procedures. The fault and event trees relate physical damage, staff shortages, and equipment failures to various hospital and emergency room functionality levels. Event trees provide a sequence of events (availability of critical staff, structural damage, and non-structural damage) leading to various levels of hospital functionality (full, partial, or not functional). Each event has an associated probability of occurring determined by a developed fault tree specific to that event failure. The methodology was validated using an additional hospital dataset to hindcast the functionality of the hospital facility. The hindcasted results match the published functionality data, indicating that this method provides a way to predict levels of functionality, which can indicate what types of services and space will be available for treatment following an earthquake.

text: Utilising risk analysis to predict post-earthquake functionality of critical healthcare facilities Hospitals, particularly emergency departments, are vital to a community’s resilience and earthquake recovery by providing emergent and ongoing critical care. Building standards and codes have higher structural requirements for hospital structures to help ensure continued functionality; nevertheless, hospitals are often damaged, evacuated or closed following design-level earthquakes. While post-earthquake hospital functionality has been the focus of recent research, it remains challenging to predict hospital functionality in a relevant and meaningful manner. Existing methods to quantify functionality are based on patient wait time, bed count, or available services. This work focuses on improving existing hospital functionality frameworks to predict various levels of hospital functionality using a combination of fault and event trees to provide a probabilistic prediction of different levels of functionality based on available services and bed counts for hospital emergency departments and whole hospitals. Fault and event trees were created using published data on hospital damage, evacuations, and operations following earthquakes and combined with international guidelines on hospital requirements and local hospital emergency procedures. The fault and event trees relate physical damage, staff shortages, and equipment failures to various hospital and emergency room functionality levels. Event trees provide a sequence of events (availability of critical staff, structural damage, and non-structural damage) leading to various levels of hospital functionality (full, partial, or not functional). Each event has an associated probability of occurring determined by a developed fault tree specific to that event failure. The methodology was validated using an additional hospital dataset to hindcast the functionality of the hospital facility. The hindcasted results match the published functionality data, indicating that this method provides a way to predict levels of functionality, which can indicate what types of services and space will be available for treatment following an earthquake.

year: 2024

title: End-user informed demographic projections for Hamilton up to 2041

authors: Cameron, Michael Patrick

subjects: cohort component model

abstract: This report provides a set of projections of the population of Hamilton City and the larger Hamilton Zone. The projections have been calculated by means of the cohort component model. The projections can be considered alongside official Statistics New Zealand projections, but differ from the latter in terms of assumptions made about net migration. These assumptions constitute a number of scenarios that were informed by the Hamilton City Council and local consultations. These scenarios are linked to the potential impact of a number of economic development activities. The report also contains projections of the number of households, the labour force and two ethnic groups: Māori and New Zealand Europeans. In addition, a dwellings-based methodology is used to produce small area (Census Area Unit) projections. Across the scenarios, Hamilton City’s projected population growth over the next two decades ranges from 13.8 percent to 36.0 percent. This is between 1.5 to 12.2 percentage points higher than the corresponding projected national growth.

text: End-user informed demographic projections for Hamilton up to 2041 This report provides a set of projections of the population of Hamilton City and the larger Hamilton Zone. The projections have been calculated by means of the cohort component model. The projections can be considered alongside official Statistics New Zealand projections, but differ from the latter in terms of assumptions made about net migration. These assumptions constitute a number of scenarios that were informed by the Hamilton City Council and local consultations. These scenarios are linked to the potential impact of a number of economic development activities. The report also contains projections of the number of households, the labour force and two ethnic groups: Māori and New Zealand Europeans. In addition, a dwellings-based methodology is used to produce small area (Census Area Unit) projections. Across the scenarios, Hamilton City’s projected population growth over the next two decades ranges from 13.8 percent to 36.0 percent. This is between 1.5 to 12.2 percentage points higher than the corresponding projected national growth.

year: 2007-12