title: EDTA in dairy wastewater and removal efficiency - A case study

authors: Xie, Congmin Zoe

subjects: EDTA cleaning

abstract: EDTA is the most widely used chelating agent for removal of undesirable cations in many industrial applications such as the dairy industry. It is generally considered to be non-biodegradable by conventional wastewater treatment, thereby potentially producing an environmental health risk. Under suitable conditions, however, EDTA has been reported to be effectively degraded by micro organisms during wastewater treatment. This study examined degradation of EDTA from a dairy wastewater treatment process in New Zealand operated at pH 8.0-8.2, with a three-week sludge retention time. An HPLC-UV analytical method was used to measure EDTA. A wide range of EDTA concentrations (93 to 5194 μg/L) was measured in the influent entering the wastewater treatment plant, while concentrations of 72-260 μg/L were measured in dairy effluent discharged into the local stream. About 93% of EDTA was degraded by the wastewater treatment. This study demonstrates that the wastewater treatment process at a dairy plant in New Zealand was very effective in removing EDTA.

text: EDTA in dairy wastewater and removal efficiency - A case study EDTA is the most widely used chelating agent for removal of undesirable cations in many industrial applications such as the dairy industry. It is generally considered to be non-biodegradable by conventional wastewater treatment, thereby potentially producing an environmental health risk. Under suitable conditions, however, EDTA has been reported to be effectively degraded by micro organisms during wastewater treatment. This study examined degradation of EDTA from a dairy wastewater treatment process in New Zealand operated at pH 8.0-8.2, with a three-week sludge retention time. An HPLC-UV analytical method was used to measure EDTA. A wide range of EDTA concentrations (93 to 5194 μg/L) was measured in the influent entering the wastewater treatment plant, while concentrations of 72-260 μg/L were measured in dairy effluent discharged into the local stream. About 93% of EDTA was degraded by the wastewater treatment. This study demonstrates that the wastewater treatment process at a dairy plant in New Zealand was very effective in removing EDTA.

year: 2012

title: The true relationship of NPA and MG levels

authors: Molan, Peter C.

subjects: honey

abstract: There has been up until now a major misunderstanding of the relationship between different levels of antibacterial activity in manuka honey. It has always been assumed that for example, a honey with a non-peroxide antibacterial activity (NPA) of 20 is twice as potent as one with a rating of NPA 10, but that is in fact not correct.

text: The true relationship of NPA and MG levels There has been up until now a major misunderstanding of the relationship between different levels of antibacterial activity in manuka honey. It has always been assumed that for example, a honey with a non-peroxide antibacterial activity (NPA) of 20 is twice as potent as one with a rating of NPA 10, but that is in fact not correct.

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