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: Re-energising Knowledge Management: Communication challenges, interdisciplinary intersections, and paradigm change

authors: Jones, Rachel Anne

subjects: Knowledge Management

abstract: Knowledge Management (KM) in the 1990s was a key upwardly-mobile management discipline. Indeed, a proliferation of articles suggested KM had the potential to make a radical departure from conventional views of organisational assets and resources, and even held the promise of transforming economies. Instead, however, KM has tended to become incorporated as a subset of traditional management. This thesis suggests that, as a result, knowledge has been perceived simply as another resource to be managed for competitive advantage. It further argues that KM need not subscribe to conventional views of management and that knowledge need not be just another resource to be exploited, hoarded, and traded. Instead, it contends that knowledge is an outcome of the process of connecting to one another in new ways and explores the field’s still-unrealised potential for generating fresh approaches relevant to contemporary conditions. In seeking to revive the excitement, and rekindle the potential, that originally surrounded the field, the thesis intervenes in current debates in KM. It attends to, and expands, the existing discourses of KM while presenting the case for a re-energised understanding of the communication of knowledge. Exploring intersections with other disciplines as well as KM’s own multidisciplinary base, it proposes transdisciplinary research as a productive focus for KM. In making these recommendations for KM’s future, the thesis seeks to make the field more responsive to current complex and dynamic academic, organisational, and social contexts. Its overall goal is not only to ensure KM’s ongoing relevance and effectiveness as a field, but to direct KM towards fulfilling its early potential.

text: Re-energising Knowledge Management: Communication challenges, interdisciplinary intersections, and paradigm change Knowledge Management (KM) in the 1990s was a key upwardly-mobile management discipline. Indeed, a proliferation of articles suggested KM had the potential to make a radical departure from conventional views of organisational assets and resources, and even held the promise of transforming economies. Instead, however, KM has tended to become incorporated as a subset of traditional management. This thesis suggests that, as a result, knowledge has been perceived simply as another resource to be managed for competitive advantage. It further argues that KM need not subscribe to conventional views of management and that knowledge need not be just another resource to be exploited, hoarded, and traded. Instead, it contends that knowledge is an outcome of the process of connecting to one another in new ways and explores the field’s still-unrealised potential for generating fresh approaches relevant to contemporary conditions. In seeking to revive the excitement, and rekindle the potential, that originally surrounded the field, the thesis intervenes in current debates in KM. It attends to, and expands, the existing discourses of KM while presenting the case for a re-energised understanding of the communication of knowledge. Exploring intersections with other disciplines as well as KM’s own multidisciplinary base, it proposes transdisciplinary research as a productive focus for KM. In making these recommendations for KM’s future, the thesis seeks to make the field more responsive to current complex and dynamic academic, organisational, and social contexts. Its overall goal is not only to ensure KM’s ongoing relevance and effectiveness as a field, but to direct KM towards fulfilling its early potential.

year: 2011

title: Nga Wahi Ipurangi - Digital Places

authors: Reilly, Alan Howard

subjects: mLearning

abstract: The Waipa district, unbeknown to many New Zealanders, is home to some of the country’s most significant historical sites and stories.  As a Learning Experiences Outside the Classroom (LEOTC) educator, one of my roles is facilitating the understanding of these places and stories with school students.  
Currently, historical sites of national significance in this area are geographically hard to recognise, and have little or no physical interpretation. The ability of students to learn at these sites relies heavily on the educator’s oratory abilities.  Recent development in mobile technology can now provide access to interpretive internet-based content anytime, anywhere and this research investigates potential for mobile technology to be used as a tool to provide meaningful learning experiences that connect students to our unique history in the environment that these events actually took place in.
This research compares different mobile learning methods’ effectiveness in increasing student understanding and retention of social history content. A mobile learning (mLearning) programme was created for the purpose of this research, with the internet-hosted content divided equally between 3 mobile learning methods and one presenter-based method. 
Three groups consisting of Yr 5-6, Yr 7-8 and Yr 9-10 school students participated in the programme. Students completed multi-choice tests before, immediately after and 4 months following the trial programme. The multi choice results were used to compare improvement in student understanding and retention. Focus group interviews were conducted immediately and 4 months after the trial and were used to explore students metacognitive reflections of the learning methods used. 
The results of the study suggest that the mobile audio method was the most effective at increasing student understanding of the learning content and also had the highest level of content retention.  Results also suggest that environmental and technological distraction may have negatively influenced the effectiveness of the two visual mobile learning methods. Distraction may account for the differences between the learning methods effectiveness suggested by the multi-choice testing. Focus group interviews indicate the importance of mobile learning in an authentic context.
Discussion around the reduction, and where possible, elimination of the identified distractions; appropriateness and feasibility of new programme development including: game/trail based mLearning; the importance of authentic contexts for Māori students; student involvement in design; student-centred methods; connection to the classroom; and the relevance of social media are explored.
This research provides a clear direction for future programme development, aligning with the Ministry of Education’s LEOTC review priorities that emphasise raising student achievement for priority students; improving student engagement; and innovative, future focused learning.

text: Nga Wahi Ipurangi - Digital Places The Waipa district, unbeknown to many New Zealanders, is home to some of the country’s most significant historical sites and stories.  As a Learning Experiences Outside the Classroom (LEOTC) educator, one of my roles is facilitating the understanding of these places and stories with school students.  
Currently, historical sites of national significance in this area are geographically hard to recognise, and have little or no physical interpretation. The ability of students to learn at these sites relies heavily on the educator’s oratory abilities.  Recent development in mobile technology can now provide access to interpretive internet-based content anytime, anywhere and this research investigates potential for mobile technology to be used as a tool to provide meaningful learning experiences that connect students to our unique history in the environment that these events actually took place in.
This research compares different mobile learning methods’ effectiveness in increasing student understanding and retention of social history content. A mobile learning (mLearning) programme was created for the purpose of this research, with the internet-hosted content divided equally between 3 mobile learning methods and one presenter-based method. 
Three groups consisting of Yr 5-6, Yr 7-8 and Yr 9-10 school students participated in the programme. Students completed multi-choice tests before, immediately after and 4 months following the trial programme. The multi choice results were used to compare improvement in student understanding and retention. Focus group interviews were conducted immediately and 4 months after the trial and were used to explore students metacognitive reflections of the learning methods used. 
The results of the study suggest that the mobile audio method was the most effective at increasing student understanding of the learning content and also had the highest level of content retention.  Results also suggest that environmental and technological distraction may have negatively influenced the effectiveness of the two visual mobile learning methods. Distraction may account for the differences between the learning methods effectiveness suggested by the multi-choice testing. Focus group interviews indicate the importance of mobile learning in an authentic context.
Discussion around the reduction, and where possible, elimination of the identified distractions; appropriateness and feasibility of new programme development including: game/trail based mLearning; the importance of authentic contexts for Māori students; student involvement in design; student-centred methods; connection to the classroom; and the relevance of social media are explored.
This research provides a clear direction for future programme development, aligning with the Ministry of Education’s LEOTC review priorities that emphasise raising student achievement for priority students; improving student engagement; and innovative, future focused learning.

year: 2013