title: Effect of activated carbon, oxidation and UV treatment on 2-methylisoborneol and geosmin removal from treated water

authors: Yan, Junfeng

subjects: activated carbon

abstract: Geosmin and 2-methylisoborneol (MIB) are two contributors to taste and odour in water, and originate from cyanobacteria and other microorganisms in surface water.  This thesis examined the use of activated carbon, hydrogen peroxide and UV treatment on removal of these two compounds from water.

Geosmin and MIB were analysed using headspace solid phase micro-extraction (SPME) and gas chromatography coupled with mass spectrometry and flame ionization detection.  Method development examined the effect of salt addition, sample heating time, and extraction time on GC peak area.  Salt addition gave up to 40% lower GC peak areas for MIB and geosmin compared to samples without salt, while increasing sample heating time and extraction time increased GC peak area, increasing the lower detection limits.  Two minutes extraction time gave peak areas 75% of that for 10 minutes extraction time.  Both GC-MS and GC-FID were reliable methods for analysis with standard deviations being less than 5% of the average peak area obtained from the GC.

Activated carbon was effective at removing geosmin and MIB, with 500 mg GAC per L removing 90% of the geosmin and MIB.  Geosmin absorption showed a type II isotherm suggesting monolayer followed by multilayer absorption, while MIB absorption was almost linear.  Langmuir-Freundlich and Freundlich isotherms fitted the MIB data well but not as well for geosmin.  Oxidative treatment using H2O2 removed 84 % of geosmin and 49 % of MIB.  UV degradation of geosmin and MIB using the Steriflo system removed up to 31 % of MIB and 76 % of geosmin after 4 hours.  After 18 hours, geosmin had 84 % removal while MIB was only 66 %.  Addition of H2O2 increased removal for MIB and geosmin up to 89 and 90 % respectively after 18 hours.  Experimental results were simulated using a model that accounted for UV and hydrogen peroxide degradation, using one set of parameters over a range of conditions for each of MIB and geosmin.

text: Effect of activated carbon, oxidation and UV treatment on 2-methylisoborneol and geosmin removal from treated water Geosmin and 2-methylisoborneol (MIB) are two contributors to taste and odour in water, and originate from cyanobacteria and other microorganisms in surface water.  This thesis examined the use of activated carbon, hydrogen peroxide and UV treatment on removal of these two compounds from water.

Geosmin and MIB were analysed using headspace solid phase micro-extraction (SPME) and gas chromatography coupled with mass spectrometry and flame ionization detection.  Method development examined the effect of salt addition, sample heating time, and extraction time on GC peak area.  Salt addition gave up to 40% lower GC peak areas for MIB and geosmin compared to samples without salt, while increasing sample heating time and extraction time increased GC peak area, increasing the lower detection limits.  Two minutes extraction time gave peak areas 75% of that for 10 minutes extraction time.  Both GC-MS and GC-FID were reliable methods for analysis with standard deviations being less than 5% of the average peak area obtained from the GC.

Activated carbon was effective at removing geosmin and MIB, with 500 mg GAC per L removing 90% of the geosmin and MIB.  Geosmin absorption showed a type II isotherm suggesting monolayer followed by multilayer absorption, while MIB absorption was almost linear.  Langmuir-Freundlich and Freundlich isotherms fitted the MIB data well but not as well for geosmin.  Oxidative treatment using H2O2 removed 84 % of geosmin and 49 % of MIB.  UV degradation of geosmin and MIB using the Steriflo system removed up to 31 % of MIB and 76 % of geosmin after 4 hours.  After 18 hours, geosmin had 84 % removal while MIB was only 66 %.  Addition of H2O2 increased removal for MIB and geosmin up to 89 and 90 % respectively after 18 hours.  Experimental results were simulated using a model that accounted for UV and hydrogen peroxide degradation, using one set of parameters over a range of conditions for each of MIB and geosmin.

year: 2016

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: Reconnecting whānau: Pathways to recovery for Māori with bipolar disorder

authors: Waitoki, Waikaremoana

subjects: mental health

abstract: While Māori are known to experience a higher burden of mental health and addiction problems compared to non-Māori (Baxter, 2008), little exploratory research has been conducted into Māori experiences of bipolar affective disorder. Bipolar disorder is at times regarded as a “life sentence”, with little hope of recovery. The recovery- focused mental health literature, however, argues wellness is achievable for even the most intractable conditions (Lapsley, Nikora, & Black, 2002; Mental Health Commission, 2001). The aim of this research was to gather information about the experiences of Māori who were diagnosed with bipolar affective disorder. Interviews were conducted with 22 Māori wāhine (women) and tāne (men), and using thematic analyses, themes relevant to their life stories were uncovered. This research sought to contribute to the realisation of Māori potential by explicitly shifting from deficit- focused frameworks to a focus on systemic factors that influenced Māori wellbeing. Highlights were that whānau (participants) who were connected with friends, partners and family were motivated to achieve wellness and to stay well.

text: Reconnecting whānau: Pathways to recovery for Māori with bipolar disorder While Māori are known to experience a higher burden of mental health and addiction problems compared to non-Māori (Baxter, 2008), little exploratory research has been conducted into Māori experiences of bipolar affective disorder. Bipolar disorder is at times regarded as a “life sentence”, with little hope of recovery. The recovery- focused mental health literature, however, argues wellness is achievable for even the most intractable conditions (Lapsley, Nikora, & Black, 2002; Mental Health Commission, 2001). The aim of this research was to gather information about the experiences of Māori who were diagnosed with bipolar affective disorder. Interviews were conducted with 22 Māori wāhine (women) and tāne (men), and using thematic analyses, themes relevant to their life stories were uncovered. This research sought to contribute to the realisation of Māori potential by explicitly shifting from deficit- focused frameworks to a focus on systemic factors that influenced Māori wellbeing. Highlights were that whānau (participants) who were connected with friends, partners and family were motivated to achieve wellness and to stay well.

year: 2015