title: A perturbative approach to a nonlinear advection-diffusion equation of particle transport

authors: Walter, Dominik

subjects: Science & Technology

abstract: We explore analytical techniques for modeling the nonlinear cosmic ray transport in various astrophysical environments which is of significant current research interest. While nonlinearity is most often described by coupled equations for the dynamics of the thermal plasma and the cosmic ray transport or for the transport of the plasma waves and the cosmic rays, we study the case of a single but nonlinear advection-diffusion equation. The latter can be approximately solved analytically or semi-analytically, with the advantage that these solutions are easy to use and, thus, can facilitate a quantitative comparison to data. In the present study, we extend our previous work in a twofold manner. First, instead of employing an integral method to the case of pure nonlinear diffusion, we apply an expansion technique to the advection-diffusion equation. We use the technique systematically to analyze the effect of nonlinear diffusion for the cases of constant and spatially varying advection combined with time-varying source functions. Second, we extend the study from the one-dimensional, Cartesian geometry to the radially symmetric case, which allows us to treat more accurately the nonlinear diffusion problems on larger scales away from the source.

text: A perturbative approach to a nonlinear advection-diffusion equation of particle transport We explore analytical techniques for modeling the nonlinear cosmic ray transport in various astrophysical environments which is of significant current research interest. While nonlinearity is most often described by coupled equations for the dynamics of the thermal plasma and the cosmic ray transport or for the transport of the plasma waves and the cosmic rays, we study the case of a single but nonlinear advection-diffusion equation. The latter can be approximately solved analytically or semi-analytically, with the advantage that these solutions are easy to use and, thus, can facilitate a quantitative comparison to data. In the present study, we extend our previous work in a twofold manner. First, instead of employing an integral method to the case of pure nonlinear diffusion, we apply an expansion technique to the advection-diffusion equation. We use the technique systematically to analyze the effect of nonlinear diffusion for the cases of constant and spatially varying advection combined with time-varying source functions. Second, we extend the study from the one-dimensional, Cartesian geometry to the radially symmetric case, which allows us to treat more accurately the nonlinear diffusion problems on larger scales away from the source.

year: 2020

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: Operational forest restoration plan for Kukutaaruhe Gully: Part 2

authors: Elliot Noe, Elizabeth

abstract: Kukutaaruhe Gully is a minor branch of Hamilton’s extensive gully system, and runs from the corner of Bankwood and Clarkin Road, behind Fairfield College, connecting with Donny Park in the north and out into the Waikato River. The Kukutaaruhe Education Trust was established to support the implementation of the Fairfield Project, which is a partnership between Ngati Wairere, local community and Fairfield College. The project’s goals include the establishment of an ecological and environmental education centre on the approximately 12 ha of land behind Fairfield College and restoration of the adjacent Kukutaaruhe Gully. In 2019, the University of Waikato’s Environmental Research Institute was contracted by the Kukutaaruhe Education Trust to create an Operational Forest Restoration plan for Kukutaaruhe Gully. This is one in a three-part series created together under one contract:
• Part 1 of 3 is an Overview of the Kukutaaruhe Gully Restoration Initiative
• Part 2 of 3 is this document, the Operational Forest Restoration plan for Kukutaaruhe Gully
• Part 3 of 3 is an Ecological Restoration Plan Template.

text: Operational forest restoration plan for Kukutaaruhe Gully: Part 2 Kukutaaruhe Gully is a minor branch of Hamilton’s extensive gully system, and runs from the corner of Bankwood and Clarkin Road, behind Fairfield College, connecting with Donny Park in the north and out into the Waikato River. The Kukutaaruhe Education Trust was established to support the implementation of the Fairfield Project, which is a partnership between Ngati Wairere, local community and Fairfield College. The project’s goals include the establishment of an ecological and environmental education centre on the approximately 12 ha of land behind Fairfield College and restoration of the adjacent Kukutaaruhe Gully. In 2019, the University of Waikato’s Environmental Research Institute was contracted by the Kukutaaruhe Education Trust to create an Operational Forest Restoration plan for Kukutaaruhe Gully. This is one in a three-part series created together under one contract:
• Part 1 of 3 is an Overview of the Kukutaaruhe Gully Restoration Initiative
• Part 2 of 3 is this document, the Operational Forest Restoration plan for Kukutaaruhe Gully
• Part 3 of 3 is an Ecological Restoration Plan Template.

year: 2020-10