title: Determination of optimum moisture content and degradation of shear strength over time for Hamilton ash materials

authors: Kuman, Doney

subjects: Hamilton Ash

abstract: Hamilton Ash is one of the widely used materials in earthwork projects throughout the Waikato region. Regardless of its frequent and widespread usage, limited research has been undertaken regarding its geotechnical properties. It has been 
observed that after the completion of fill construction, Hamilton Ash unit tends to saturate overtime thus leading to a gradual loss of shear strength and ultimately resulting in ground instability. Common instabilities associated with soil fills are settlement and subsidence which in excess can result in distortion and damage to 
structures, services and infrastructures that are founded on the material subjected to the movement. The compaction stage, wherein the problem originates, can be optimized if better understanding of the geotechnical properties of Hamilton Ash is attained. 

My project focused on determining the key compaction parameters of Hamilton Ash which are the optimum moisture content and maximum dry density. Further investigation into the degrading influence of prolonged exposure to moisture on the shear strength is also undertaken. The use of laboratory compaction tests have been recognized as mandatory procedures in determining the crucial parameters for field-based applications. The Standard Proctor test was implemented in this research to help determine the key compaction parameters; Optimum Moisture Content and Maximum Dry Density of Hamilton Ash. Prior to the compaction tests, the important index properties of the soil that influence compaction were identified via several soil characterization tests, namely Atterberg Limits, grainsize and mineralogical analyses. Laboratory vane shear testing was also employed in this research to determine the strength of the compacted soil before and after wetting periods.

text: Determination of optimum moisture content and degradation of shear strength over time for Hamilton ash materials Hamilton Ash is one of the widely used materials in earthwork projects throughout the Waikato region. Regardless of its frequent and widespread usage, limited research has been undertaken regarding its geotechnical properties. It has been 
observed that after the completion of fill construction, Hamilton Ash unit tends to saturate overtime thus leading to a gradual loss of shear strength and ultimately resulting in ground instability. Common instabilities associated with soil fills are settlement and subsidence which in excess can result in distortion and damage to 
structures, services and infrastructures that are founded on the material subjected to the movement. The compaction stage, wherein the problem originates, can be optimized if better understanding of the geotechnical properties of Hamilton Ash is attained. 

My project focused on determining the key compaction parameters of Hamilton Ash which are the optimum moisture content and maximum dry density. Further investigation into the degrading influence of prolonged exposure to moisture on the shear strength is also undertaken. The use of laboratory compaction tests have been recognized as mandatory procedures in determining the crucial parameters for field-based applications. The Standard Proctor test was implemented in this research to help determine the key compaction parameters; Optimum Moisture Content and Maximum Dry Density of Hamilton Ash. Prior to the compaction tests, the important index properties of the soil that influence compaction were identified via several soil characterization tests, namely Atterberg Limits, grainsize and mineralogical analyses. Laboratory vane shear testing was also employed in this research to determine the strength of the compacted soil before and after wetting periods.

year: 2019

title: Predicting glucose level with an adapted branch predictor

authors: Koutny, Tomas

subjects: computer science

abstract: Background and objective
Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit.

Methods
We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction.

Results
To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%.

Conclusions
When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

text: Predicting glucose level with an adapted branch predictor Background and objective
Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit.

Methods
We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction.

Results
To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%.

Conclusions
When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

year: 2022

title: COVID-19 and blind spaces: Responding to digital (in)accessibility and social isolation during lockdown for blind, deafblind, low vision, and vision impaired persons in Aotearoa New Zealand

authors: Graham, Rebekah Sarah

abstract: In this chapter we, as Indigenous (Māori) and non-Indigenous (Pākehā) academics, practitioners, and disability advocates critically examine the experiences of people who are blind, deafblind, low vision and vision impaired (BLV) during the COVID-19 lockdown of Aotearoa New Zealand. We pay particular attention to the ways in which access (or lack thereof) to digital spaces facilitated experiences of social inclusion (and exclusion). Digital connectivity was heavily relied upon during lockdown in Aotearoa New Zealand to disseminate health information and to connect people socially. The use of digital technologies during lockdown extended the interpersonal space of the home beyond the physicality of the urban home people were confined to, drawing people together across the country and creating a sense of shared experience. However, these experiences were not shared equally across the citizenry. Conversations during lockdown with disability advocates and practitioners working with BLV persons identified that. For example, the government website on COVID19 was initially inaccessible for BLV persons and there were assumptions of digital accessibility and literacy within the domestic space of the home. Taken together, these exacerbated existing inequities for BLV persons, some of whom experience multiple domains of exclusion (for example, Indigenous BLV persons with reduced access to services and for whom English is their second language). Drawing on Lefebvre’s (1991) work on the dialectics of space and Lewin’s (1936/2013) construction of the life-space, we consider the interplay of space and experience with regards to the digital and physical space(s) available and inhabited during the shared social experience of lockdown. We discuss possibilities for co-creating socially inclusive (digital) spaces of belonging that reflect and refract life-space relational connections across social groups. Our reflections on the lockdown experience include opportunities post-pandemic for policy makers and practitioners to address digital accessibility inequities.

text: COVID-19 and blind spaces: Responding to digital (in)accessibility and social isolation during lockdown for blind, deafblind, low vision, and vision impaired persons in Aotearoa New Zealand In this chapter we, as Indigenous (Māori) and non-Indigenous (Pākehā) academics, practitioners, and disability advocates critically examine the experiences of people who are blind, deafblind, low vision and vision impaired (BLV) during the COVID-19 lockdown of Aotearoa New Zealand. We pay particular attention to the ways in which access (or lack thereof) to digital spaces facilitated experiences of social inclusion (and exclusion). Digital connectivity was heavily relied upon during lockdown in Aotearoa New Zealand to disseminate health information and to connect people socially. The use of digital technologies during lockdown extended the interpersonal space of the home beyond the physicality of the urban home people were confined to, drawing people together across the country and creating a sense of shared experience. However, these experiences were not shared equally across the citizenry. Conversations during lockdown with disability advocates and practitioners working with BLV persons identified that. For example, the government website on COVID19 was initially inaccessible for BLV persons and there were assumptions of digital accessibility and literacy within the domestic space of the home. Taken together, these exacerbated existing inequities for BLV persons, some of whom experience multiple domains of exclusion (for example, Indigenous BLV persons with reduced access to services and for whom English is their second language). Drawing on Lefebvre’s (1991) work on the dialectics of space and Lewin’s (1936/2013) construction of the life-space, we consider the interplay of space and experience with regards to the digital and physical space(s) available and inhabited during the shared social experience of lockdown. We discuss possibilities for co-creating socially inclusive (digital) spaces of belonging that reflect and refract life-space relational connections across social groups. Our reflections on the lockdown experience include opportunities post-pandemic for policy makers and practitioners to address digital accessibility inequities.

year: 2021