title: The power output of spine and fan magnetic reconnection solutions

authors: Craig, Ian J.D.

subjects: MHD—plasmas

abstract: The ability of three-dimensional magnetic “spine” and “fan” reconnection solutions to provide flarelike energy release is discussed. It is pointed out, on the basis of exact analytic solutions, that fast dissipation is possible only if the hydromagnetic pressure in the reconnection region becomes unbounded in the limit of small plasma resistivities. The implication is that some “saturation” of the power output is inevitable for realistic coronal plasmas. Estimates of the saturated power, based on limiting the flux pileup in the field, suggest that the geometry of the spine reconnection mechanism precludes significant flare energy release. However, the current sheet structures involved in fan reconnection seem able to release sufficient magnetic energy fast enough to account for modest flares, even under the conservative assumption of classical plasma resistivities.

text: The power output of spine and fan magnetic reconnection solutions The ability of three-dimensional magnetic “spine” and “fan” reconnection solutions to provide flarelike energy release is discussed. It is pointed out, on the basis of exact analytic solutions, that fast dissipation is possible only if the hydromagnetic pressure in the reconnection region becomes unbounded in the limit of small plasma resistivities. The implication is that some “saturation” of the power output is inevitable for realistic coronal plasmas. Estimates of the saturated power, based on limiting the flux pileup in the field, suggest that the geometry of the spine reconnection mechanism precludes significant flare energy release. However, the current sheet structures involved in fan reconnection seem able to release sufficient magnetic energy fast enough to account for modest flares, even under the conservative assumption of classical plasma resistivities.

year: 1997-08

title: Usefulness of Integrated Reporting Concepts to Investment Decision-making: Empirical Evidence from Taiwan

authors: Hsiao, Pei-Chi

subjects: Integrated reporting

abstract: The purpose of this thesis is to extend the knowledge and understanding of integrated reporting and investment decision-making processes, particularly from a non-Western perspective. A review of extant literature identified a dearth of research on integrated reporting and the investment processes of investors in Taiwan. This study contributes to knowledge by providing a greater insight into the usefulness of integrated reporting concepts to Taiwanese investors. 

This empirical study is exploratory in nature and adopts an interpretivist methodology approach. Pilot tests were conducted prior to the semi-structured interviews with 16 Taiwanese investors. The research sample reflects a wide range of investment specialities and focuses. The data collected were transcribed, open coded and explored in NVivo, and analysed manually for a deeper understanding of the data and to identify themes. Although the sample is small and diverse, participants are often found to have similar perspectives on investment decision-making and integrated reporting. 

The research findings suggest disclosures prepared in accordance with integrated reporting concepts are unlikely to become an essential source of information for Taiwanese investors at present and the near future. None of the participants were aware of integrated reporting or any of its associated developments. The values of Taiwanese investors appear to differ from the values of Western societies, resulting in less demand for non-financial disclosures in Taiwan. Taiwanese investors are primarily focused on financial information and financial value creation. Participants expressed concerns about the practicability and reliability of integrated reporting, but remain welcoming of the idea as it may enhance corporate transparency and provide new information to use in the evaluation of investment targets. However, Taiwanese investors often seek information that is already known, but undisclosed, by corporations. Integrated reporting may generate information that is not directly relevant to investment decision-making. Furthermore, many factors that influence Taiwanese investors are unable to be conveyed through corporate disclosures. Participants also prefer to obtain information from third party sources and direct communication with corporate management over corporate non-financial disclosures.

text: Usefulness of Integrated Reporting Concepts to Investment Decision-making: Empirical Evidence from Taiwan The purpose of this thesis is to extend the knowledge and understanding of integrated reporting and investment decision-making processes, particularly from a non-Western perspective. A review of extant literature identified a dearth of research on integrated reporting and the investment processes of investors in Taiwan. This study contributes to knowledge by providing a greater insight into the usefulness of integrated reporting concepts to Taiwanese investors. 

This empirical study is exploratory in nature and adopts an interpretivist methodology approach. Pilot tests were conducted prior to the semi-structured interviews with 16 Taiwanese investors. The research sample reflects a wide range of investment specialities and focuses. The data collected were transcribed, open coded and explored in NVivo, and analysed manually for a deeper understanding of the data and to identify themes. Although the sample is small and diverse, participants are often found to have similar perspectives on investment decision-making and integrated reporting. 

The research findings suggest disclosures prepared in accordance with integrated reporting concepts are unlikely to become an essential source of information for Taiwanese investors at present and the near future. None of the participants were aware of integrated reporting or any of its associated developments. The values of Taiwanese investors appear to differ from the values of Western societies, resulting in less demand for non-financial disclosures in Taiwan. Taiwanese investors are primarily focused on financial information and financial value creation. Participants expressed concerns about the practicability and reliability of integrated reporting, but remain welcoming of the idea as it may enhance corporate transparency and provide new information to use in the evaluation of investment targets. However, Taiwanese investors often seek information that is already known, but undisclosed, by corporations. Integrated reporting may generate information that is not directly relevant to investment decision-making. Furthermore, many factors that influence Taiwanese investors are unable to be conveyed through corporate disclosures. Participants also prefer to obtain information from third party sources and direct communication with corporate management over corporate non-financial disclosures.

year: 2015

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22