title: Seeking sigma: Time-of-the-day effects on the Bitcoin network

authors: Jahanshahloo, Hossein

subjects: Social Sciences

abstract: This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be ‘24/7’, our findings suggest that both transaction and on-chain network activity are best described to be, at best, ‘12/5’, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.

text: Seeking sigma: Time-of-the-day effects on the Bitcoin network This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be ‘24/7’, our findings suggest that both transaction and on-chain network activity are best described to be, at best, ‘12/5’, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.

year: 2022-10-01

title: Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand.

authors: Pathayala Ralalage, Sumedha Kusum Amarasena

subjects: Wetlands

abstract: Wetlands are among the world's most valuable ecosystems. They provide numerous ecological and socio-economic benefits. However, wetlands continue to disappear due to the increasing demand for wetland resources. In New Zealand, more than 90% of the original extent of wetlands has been lost since the mid-eighteenth century. Therefore, legislation has been identified for the protection of wetlands as a matter of national importance.

Geographic Information System (GIS) and Remote Sensing (RS) techniques have proven helpful for mapping and monitoring wetland resources. This study aims to understand how RStechniques can classify wetlands in the Lake Whangape catchment, Waikato. The parameters that can be extracted from available data and their effectiveness in the classification process are also studied. 

Four types of input data are collectively employed in the study. The data types are optical RS data, Synthetic Aperture Radar (SAR) data, a Digital Elevation Model (DEM), and wetland polygons provided by the Waikato Regional Council (WRC). All the steps including, accessing satellite scenes and data processing were performed within Google Earth Engine (GEE) computing platform using JavaScript language. The classification process for this study includes feature extraction, feature selection, model training, classification, and validation. Finally, the accuracy of the classification results is checked visually and statistically. 

The classification was carried out in two stages. In Stage one, open water, wetland, and non-wetland areas are classified (simple classification). The combined wetlands class is separated into marsh and swamp in the second stage (detailed classification). Based on the results, the Topographic Position Index (TPI) is the most influential parameter in identifying wetlands, while the Modified Normalized Water Index (MNDWI) successfully identifies open water. The overall accuracy reached 91% at the simple classification stage. However, the detailed classification results received comparatively low classification accuracies (the overall accuracy is 76%).

text: Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand. Wetlands are among the world's most valuable ecosystems. They provide numerous ecological and socio-economic benefits. However, wetlands continue to disappear due to the increasing demand for wetland resources. In New Zealand, more than 90% of the original extent of wetlands has been lost since the mid-eighteenth century. Therefore, legislation has been identified for the protection of wetlands as a matter of national importance.

Geographic Information System (GIS) and Remote Sensing (RS) techniques have proven helpful for mapping and monitoring wetland resources. This study aims to understand how RStechniques can classify wetlands in the Lake Whangape catchment, Waikato. The parameters that can be extracted from available data and their effectiveness in the classification process are also studied. 

Four types of input data are collectively employed in the study. The data types are optical RS data, Synthetic Aperture Radar (SAR) data, a Digital Elevation Model (DEM), and wetland polygons provided by the Waikato Regional Council (WRC). All the steps including, accessing satellite scenes and data processing were performed within Google Earth Engine (GEE) computing platform using JavaScript language. The classification process for this study includes feature extraction, feature selection, model training, classification, and validation. Finally, the accuracy of the classification results is checked visually and statistically. 

The classification was carried out in two stages. In Stage one, open water, wetland, and non-wetland areas are classified (simple classification). The combined wetlands class is separated into marsh and swamp in the second stage (detailed classification). Based on the results, the Topographic Position Index (TPI) is the most influential parameter in identifying wetlands, while the Modified Normalized Water Index (MNDWI) successfully identifies open water. The overall accuracy reached 91% at the simple classification stage. However, the detailed classification results received comparatively low classification accuracies (the overall accuracy is 76%).

year: 2021

title: Pūtahitanga: The intersection of western science and mātauranga Māori in the context of Aotearoa New Zealand’s physical oceanography

authors: Stevens, Craig L.

subjects: Mātauranga Māori

abstract: Aotearoa New Zealand is a land and cultures surrounded by one of the planet’s larger marine exclusive economic zones per capita. Understanding, living within and utilising this domain requires a well-developed knowledge of the physical oceanography–fate, provenance and transfer of ocean-water, material and energy. In the Aotearoa New Zealand context, understanding and utilising the environment through science also requires that Māori have a stake in the science and a pathway to connecting with their mātauranga. The pūtahitanga (intersection) of mātauranga Māori and western science perspectives brings challenges for implementation of the Vision Mātauranga framework for physical oceanographic science. Here we discuss the drivers and key themes for the pūtahitanga and propose a mahere kaupapa/plan bound by: (i) climate, (ii) decolonisation, (iii) shared language, (iv) data sovereignty, and (v) developing a cohort who can lead the field in the coming decades.

text: Pūtahitanga: The intersection of western science and mātauranga Māori in the context of Aotearoa New Zealand’s physical oceanography Aotearoa New Zealand is a land and cultures surrounded by one of the planet’s larger marine exclusive economic zones per capita. Understanding, living within and utilising this domain requires a well-developed knowledge of the physical oceanography–fate, provenance and transfer of ocean-water, material and energy. In the Aotearoa New Zealand context, understanding and utilising the environment through science also requires that Māori have a stake in the science and a pathway to connecting with their mātauranga. The pūtahitanga (intersection) of mātauranga Māori and western science perspectives brings challenges for implementation of the Vision Mātauranga framework for physical oceanographic science. Here we discuss the drivers and key themes for the pūtahitanga and propose a mahere kaupapa/plan bound by: (i) climate, (ii) decolonisation, (iii) shared language, (iv) data sovereignty, and (v) developing a cohort who can lead the field in the coming decades.

year: 2021