title: For flux's sake: General considerations for energy-flux calculations in ecological communities

authors: Jochum, Malte

subjects: Science & Technology

abstract: Global change alters ecological communities with consequences for ecosystem processes. Such processes and functions are a central aspect of ecological research and vital to understanding and mitigating the consequences of global change, but also those of other drivers of change in organism communities. In this context, the concept of energy flux through trophic networks integrates food- web theory and biodiversity- ecosystem functioning theory and connects biodiversity to multitrophic ecosystem functioning. As such, the energy- flux approach is a strikingly effective tool to answer central questions in ecology and global- change research. This might seem straight forward, given that the theoretical background and software to efficiently calculate energy flux are readily available. However, the implementation of such calculations is not always straight forward, especially for those who are new to the topic and not familiar with concepts central to this line of research, such as food- web theory or metabolic theory. To facilitate wider use of energy flux in ecological research, we thus provide a guide to adopting energy- flux calculations for people new to the method, struggling with its implementation, or simply looking for background reading, important resources, and standard solutions to the problems everyone faces when starting to quantify energy fluxes for their community data. First, we introduce energy flux and its use in community and ecosystem ecology. Then, we provide a comprehensive explanation of the single steps towards calculating energy flux for community data. Finally, we discuss remaining challenges and exciting research frontiers for future energy- flux research.

text: For flux's sake: General considerations for energy-flux calculations in ecological communities Global change alters ecological communities with consequences for ecosystem processes. Such processes and functions are a central aspect of ecological research and vital to understanding and mitigating the consequences of global change, but also those of other drivers of change in organism communities. In this context, the concept of energy flux through trophic networks integrates food- web theory and biodiversity- ecosystem functioning theory and connects biodiversity to multitrophic ecosystem functioning. As such, the energy- flux approach is a strikingly effective tool to answer central questions in ecology and global- change research. This might seem straight forward, given that the theoretical background and software to efficiently calculate energy flux are readily available. However, the implementation of such calculations is not always straight forward, especially for those who are new to the topic and not familiar with concepts central to this line of research, such as food- web theory or metabolic theory. To facilitate wider use of energy flux in ecological research, we thus provide a guide to adopting energy- flux calculations for people new to the method, struggling with its implementation, or simply looking for background reading, important resources, and standard solutions to the problems everyone faces when starting to quantify energy fluxes for their community data. First, we introduce energy flux and its use in community and ecosystem ecology. Then, we provide a comprehensive explanation of the single steps towards calculating energy flux for community data. Finally, we discuss remaining challenges and exciting research frontiers for future energy- flux research.

year: 2021-09-14

title: confstream: automated algorithm selection and configuration of stream clustering algorithms

authors: Carnein, Matthias

subjects: computer science

abstract: Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.

text: confstream: automated algorithm selection and configuration of stream clustering algorithms Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.

year: 2020

title: Ceremonious storytelling: Exploring experiences within the publicly funded health service

authors: Mellsop-Kupe, Jessie

subjects: Māori health

abstract: Background: Chronic disease is defined as a condition impacting organs or organ systems causing dysfunction or dysregulation that lasts longer than six months or remains, despite medical intervention or treatment.  Research has shown that barriers, including those of a financial, physical, institutional and patient orientated nature significantly impact on outcomes. Māori are significantly over-represented in chronic health condition statistics, particularly relating to cardiac, respiratory, renal and mental health.  Further, Māori are more likely to have a negative experience engaging with primary and secondary health services, leading to disempowerment and dis-engagement. Therefore, research which explores how quality of care for Māori can be improved is of pressing value.

Objective: This study seeks to explore the experiences of Māori and non-Māori health-care consumers and providers within the publicly funded health service of Aotearoa. 
 
Participants: Advertising across primary and secondary care settings resulted in a total of 37 responses from health care consumers (31 respondents contributing to pūrākau) and 37 responses from health professionals (27 respondents contributing to pūrākau).

Methods: This study utilised kaupapa Māori methodological approaches, frameworks and models to guide and intertwine qualitative and quantitative research methods.  Participants were able to engage with any subset of questions, defining their responses through pūrākau, which supports the development of narratives within questions that are outside the limits of other worldviews.  Māori methods such as pūrākau are flexible, enabling the development of research methods that are culturally aware and safe for Indigenous and non-Indigenous people globally

Results: Key themes found within pūrākau highlighted the importance of meaningful engagement, person focused, trauma informed care to positively impact willingness and ability to engage with primary, secondary and tertiary services.  The study reported that consumers had a strong willingness to engage with services.

Conclusion: Health is personal and impacts people in various ways.  Access to health services, while funded in Aotearoa, still create barriers for some people and perpetuates the gap between prevention and diagnosis of chronic disease.  Kaupapa Māori methods within research support Māori engagement and definition within research through historical ways of being, knowing and conducting research.  Participants of this study detailed experiences within health services, highlighting willingness and ability to engage with health services.  Prior research defined the development of the Levesque access and accessibility framework, which correlates with the findings within this study."

text: Ceremonious storytelling: Exploring experiences within the publicly funded health service Background: Chronic disease is defined as a condition impacting organs or organ systems causing dysfunction or dysregulation that lasts longer than six months or remains, despite medical intervention or treatment.  Research has shown that barriers, including those of a financial, physical, institutional and patient orientated nature significantly impact on outcomes. Māori are significantly over-represented in chronic health condition statistics, particularly relating to cardiac, respiratory, renal and mental health.  Further, Māori are more likely to have a negative experience engaging with primary and secondary health services, leading to disempowerment and dis-engagement. Therefore, research which explores how quality of care for Māori can be improved is of pressing value.

Objective: This study seeks to explore the experiences of Māori and non-Māori health-care consumers and providers within the publicly funded health service of Aotearoa. 
 
Participants: Advertising across primary and secondary care settings resulted in a total of 37 responses from health care consumers (31 respondents contributing to pūrākau) and 37 responses from health professionals (27 respondents contributing to pūrākau).

Methods: This study utilised kaupapa Māori methodological approaches, frameworks and models to guide and intertwine qualitative and quantitative research methods.  Participants were able to engage with any subset of questions, defining their responses through pūrākau, which supports the development of narratives within questions that are outside the limits of other worldviews.  Māori methods such as pūrākau are flexible, enabling the development of research methods that are culturally aware and safe for Indigenous and non-Indigenous people globally

Results: Key themes found within pūrākau highlighted the importance of meaningful engagement, person focused, trauma informed care to positively impact willingness and ability to engage with primary, secondary and tertiary services.  The study reported that consumers had a strong willingness to engage with services.

Conclusion: Health is personal and impacts people in various ways.  Access to health services, while funded in Aotearoa, still create barriers for some people and perpetuates the gap between prevention and diagnosis of chronic disease.  Kaupapa Māori methods within research support Māori engagement and definition within research through historical ways of being, knowing and conducting research.  Participants of this study detailed experiences within health services, highlighting willingness and ability to engage with health services.  Prior research defined the development of the Levesque access and accessibility framework, which correlates with the findings within this study."

year: 2024-07-27