title: Effect of microwave sintering parameters on the physical and mechanical properties of pure Ti and blended elemental Ti alloys

authors: Raynova, Stiliana (Stella) Rousseva

subjects: Microwave sintering

abstract: Microwave sintering (MWS) was used to consolidate hydride de-hydride Ti powder and blended elemental Ti6Al4V, Ti5Fe and Ti5Al5Mo5V3Cr (Ti5553) powder mixtures. The amount of powders used to prepare the powder compacts was scaled up to 500g.The effect of the MWS conditions on the relative density, porosity distribution, microstructure and tensile properties were studied. Furthermore, uniformity in distribution of the alloying elements was checked. For most of the materials considered, a combinations of sintering temperature of 1200ºC and 1300ºC and holding time of 5 to 30 min resulted in significantly improved density. Nevertheless sintering temperature of at least 1300ºC was required for pore coalescence and high tensile properties.

text: Effect of microwave sintering parameters on the physical and mechanical properties of pure Ti and blended elemental Ti alloys Microwave sintering (MWS) was used to consolidate hydride de-hydride Ti powder and blended elemental Ti6Al4V, Ti5Fe and Ti5Al5Mo5V3Cr (Ti5553) powder mixtures. The amount of powders used to prepare the powder compacts was scaled up to 500g.The effect of the MWS conditions on the relative density, porosity distribution, microstructure and tensile properties were studied. Furthermore, uniformity in distribution of the alloying elements was checked. For most of the materials considered, a combinations of sintering temperature of 1200ºC and 1300ºC and holding time of 5 to 30 min resulted in significantly improved density. Nevertheless sintering temperature of at least 1300ºC was required for pore coalescence and high tensile properties.

year: 2018

title: Pruning decision trees and lists

authors: Frank, Eibe

abstract: Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

text: Pruning decision trees and lists Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

year: 2000

title: How do colorectal cancer patients rate their GP: a mixed methods study

authors: Blackmore, Tania Louise

subjects: Science & Technology

abstract: Background: New Zealand (NZ) has a high incidence of colorectal cancer (CRC) and low rates of early diagnosis. With screening not yet nationwide, the majority of CRC is diagnosed through general practice. A good patient-general practitioner (GP) relationship can facilitate prompt diagnosis, but when there is a breakdown in this relationship, delays can occur. Delayed diagnosis of CRC in NZ receives a disproportionally high number of complaints directed against GPs, suggesting deficits in the patient-GP connection. We aimed to investigate patient-reported confidence and ratings of their GP following the diagnostic process. 
Methods: This study is a mixed methods analysis of responses to a structured questionnaire and free text comments from patients newly diagnosed with CRC in the Midland region of NZ. A total of 195 patients responded to the structured questionnaire, and 113 patients provided additional free text comments. Descriptive statistics were used to describe the study population and chi square analysis determined the statistical significance of factors possibly linked to delay. Free text comments were analysed using a thematic framework. 
Results: Most participants rated their GP as ‘Very good/Good’ at communication with patients about their health conditions and involving them in decisions about their care, and 6.7% of participants rated their overall level of confidence and trust in their GP as ‘Not at all’. Age, gender, ethnicity and a longer diagnostic interval were associated with lower confidence and trust. Free text comments were grouped in to three themes: 1. GP Interpersonal skills; (communication, listening, taking patient symptoms seriously), 2. Technical competence; (speed of referral, misdiagnoses, lack of physical examination), and 3. Organisation of general practice care; (appointment length, getting an appointment, continuity of care). 
Conclusions: Māori, females, and younger participants were more likely to report low confidence and trust in their GP. Participants associate a poor diagnostic experience with deficits in the interpersonal and technical skills of their GP, and health system factors within general practice. Short appointment times, access to appointments and poor GP continuity are important components of how patients assess their experience and are particularly important to ensure equal access for Māori patients.

text: How do colorectal cancer patients rate their GP: a mixed methods study Background: New Zealand (NZ) has a high incidence of colorectal cancer (CRC) and low rates of early diagnosis. With screening not yet nationwide, the majority of CRC is diagnosed through general practice. A good patient-general practitioner (GP) relationship can facilitate prompt diagnosis, but when there is a breakdown in this relationship, delays can occur. Delayed diagnosis of CRC in NZ receives a disproportionally high number of complaints directed against GPs, suggesting deficits in the patient-GP connection. We aimed to investigate patient-reported confidence and ratings of their GP following the diagnostic process. 
Methods: This study is a mixed methods analysis of responses to a structured questionnaire and free text comments from patients newly diagnosed with CRC in the Midland region of NZ. A total of 195 patients responded to the structured questionnaire, and 113 patients provided additional free text comments. Descriptive statistics were used to describe the study population and chi square analysis determined the statistical significance of factors possibly linked to delay. Free text comments were analysed using a thematic framework. 
Results: Most participants rated their GP as ‘Very good/Good’ at communication with patients about their health conditions and involving them in decisions about their care, and 6.7% of participants rated their overall level of confidence and trust in their GP as ‘Not at all’. Age, gender, ethnicity and a longer diagnostic interval were associated with lower confidence and trust. Free text comments were grouped in to three themes: 1. GP Interpersonal skills; (communication, listening, taking patient symptoms seriously), 2. Technical competence; (speed of referral, misdiagnoses, lack of physical examination), and 3. Organisation of general practice care; (appointment length, getting an appointment, continuity of care). 
Conclusions: Māori, females, and younger participants were more likely to report low confidence and trust in their GP. Participants associate a poor diagnostic experience with deficits in the interpersonal and technical skills of their GP, and health system factors within general practice. Short appointment times, access to appointments and poor GP continuity are important components of how patients assess their experience and are particularly important to ensure equal access for Māori patients.

year: 2021-04-08