title: Identifying the volcanic eruption depicted in a Neolithic Painting at Çatalhöyük, Central Anatolia, Turkey

authors: Schmitt, Axel K.

abstract: A mural excavated at the Neolithic Çatalhöyük site (Central Anatolia, Turkey) has been interpreted as the oldest known map. Dating to ~6600 BCE, it putatively depicts an explosive summit eruption of the Hasan Dağı twin-peaks volcano located ~130 km northeast of Çatalhöyük, and a birds-eye view of a town plan in the foreground. This interpretation, however, has remained controversial not least because independent evidence for a contemporaneous explosive volcanic eruption of Hasan Dağı has been lacking. Here, we document the presence of andesitic pumice veneer on the summit of Hasan Dağı, which we dated using (U-Th)/He zircon geochronology. The (U-Th)/He zircon eruption age of 8.97±0.64 ka (or 6960±640 BCE; uncertainties 2σ) overlaps closely with ¹⁴C ages for cultural strata at Çatalhöyük, including level VII containing the “map” mural. A second pumice sample from a surficial deposit near the base of Hasan Dağı records an older explosive eruption at 28.9±1.5 ka. U-Th zircon crystallization ages in both samples range from near-eruption to secular equilibrium (>380 ka). Collectively, our results reveal protracted intrusive activity at Hasan Dağı punctuated by explosive venting, and provide the first radiometric ages for a Holocene explosive eruption which was most likely witnessed by humans in the area. Geologic and geochronologic lines of evidence thus support previous interpretations that residents of Çatalhöyük artistically represented an explosive eruption of Hasan Dağı volcano. The magmatic longevity recorded by quasi-continuous zircon crystallization coupled with new evidence for late-Pleistocene and Holocene explosive eruptions implicates Hasan Dağı as a potential volcanic hazard.

text: Identifying the volcanic eruption depicted in a Neolithic Painting at Çatalhöyük, Central Anatolia, Turkey A mural excavated at the Neolithic Çatalhöyük site (Central Anatolia, Turkey) has been interpreted as the oldest known map. Dating to ~6600 BCE, it putatively depicts an explosive summit eruption of the Hasan Dağı twin-peaks volcano located ~130 km northeast of Çatalhöyük, and a birds-eye view of a town plan in the foreground. This interpretation, however, has remained controversial not least because independent evidence for a contemporaneous explosive volcanic eruption of Hasan Dağı has been lacking. Here, we document the presence of andesitic pumice veneer on the summit of Hasan Dağı, which we dated using (U-Th)/He zircon geochronology. The (U-Th)/He zircon eruption age of 8.97±0.64 ka (or 6960±640 BCE; uncertainties 2σ) overlaps closely with ¹⁴C ages for cultural strata at Çatalhöyük, including level VII containing the “map” mural. A second pumice sample from a surficial deposit near the base of Hasan Dağı records an older explosive eruption at 28.9±1.5 ka. U-Th zircon crystallization ages in both samples range from near-eruption to secular equilibrium (>380 ka). Collectively, our results reveal protracted intrusive activity at Hasan Dağı punctuated by explosive venting, and provide the first radiometric ages for a Holocene explosive eruption which was most likely witnessed by humans in the area. Geologic and geochronologic lines of evidence thus support previous interpretations that residents of Çatalhöyük artistically represented an explosive eruption of Hasan Dağı volcano. The magmatic longevity recorded by quasi-continuous zircon crystallization coupled with new evidence for late-Pleistocene and Holocene explosive eruptions implicates Hasan Dağı as a potential volcanic hazard.

year: 2014

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: Māori Psychology Workforce & Māori-Focussed Course Content Review

authors: Waitoki, Waikaremoana

abstract: The Ministry of Health (2014) acknowledges that inaccurate and unreliable workforce data collections have been an issue for some time. This is a particular issue for the psychology workforce, with there being no single data set able to accurately and reliably describe the psychology workforce in detail. The annual psychology workforce survey was discontinued post 2010, although District Health Board (DHB) Shared Services does provide a dataset pertaining to the DHB psychology workforce. This report details the paper presents data relating to the: Current Māori psychology workforce; and describes the inclusion of Māori focused content within university psychology department courses.

text: Māori Psychology Workforce & Māori-Focussed Course Content Review The Ministry of Health (2014) acknowledges that inaccurate and unreliable workforce data collections have been an issue for some time. This is a particular issue for the psychology workforce, with there being no single data set able to accurately and reliably describe the psychology workforce in detail. The annual psychology workforce survey was discontinued post 2010, although District Health Board (DHB) Shared Services does provide a dataset pertaining to the DHB psychology workforce. This report details the paper presents data relating to the: Current Māori psychology workforce; and describes the inclusion of Māori focused content within university psychology department courses.

year: 2015