title: The antimicrobial efficacy of Fijian honeys against clinical isolates from diabetic foot ulcers

authors: Saraf, Rajneeta

subjects: diabetic foot ulcers

abstract: A diverse range of illnesses has been treated with honey since ancient civilizations. There has been growing interest by health care professionals in wound care products based on New Zealand Manuka honey and Australian honey of similar Leptospermum spp. In Fiji, local honeys have been used in homes to treat diabetic foot ulcers which have failed to heal by conventional therapeutic methods. This suggests that Fiji honeys may confer antimicrobial activity against the isolates from diabetic foot ulcers and this inference was tested in this study. The antimicrobial activity of 30 natural and two processed honeys was determined using some clinical isolates from diabetic foot ulcers, namely: Staphylococcus aureus, Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumoniae and Candida albicans.  The antimicrobial activity of the natural honeys, determined by an agar well diffusion assay and expressed as the concentration of phenol with equivalent activity, was found to be between 4.1 and 14.5% phenol. The mean inhibitory concentrations (MIC) of the honeys determined by an agar incorporation technique, was found to range from 4.8% to more than 9.1% (v/v) honey (9.1% being the highest concentration tested). In comparison, the activities of two processed honeys were between 4.5 - 8.9% phenol equivalence and did not inhibit the clinical isolates from diabetic foot ulcers at the highest concentration of honey tested (9.1%). The results demonstrate that Fijian honeys could be utilized as herbal remedy for the treatment of diabetic foot ulcers. However, to assess the potential of Fijian honeys on diabetic foot ulcers, there is a need for clinical trials on these wounds.

text: The antimicrobial efficacy of Fijian honeys against clinical isolates from diabetic foot ulcers A diverse range of illnesses has been treated with honey since ancient civilizations. There has been growing interest by health care professionals in wound care products based on New Zealand Manuka honey and Australian honey of similar Leptospermum spp. In Fiji, local honeys have been used in homes to treat diabetic foot ulcers which have failed to heal by conventional therapeutic methods. This suggests that Fiji honeys may confer antimicrobial activity against the isolates from diabetic foot ulcers and this inference was tested in this study. The antimicrobial activity of 30 natural and two processed honeys was determined using some clinical isolates from diabetic foot ulcers, namely: Staphylococcus aureus, Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumoniae and Candida albicans.  The antimicrobial activity of the natural honeys, determined by an agar well diffusion assay and expressed as the concentration of phenol with equivalent activity, was found to be between 4.1 and 14.5% phenol. The mean inhibitory concentrations (MIC) of the honeys determined by an agar incorporation technique, was found to range from 4.8% to more than 9.1% (v/v) honey (9.1% being the highest concentration tested). In comparison, the activities of two processed honeys were between 4.5 - 8.9% phenol equivalence and did not inhibit the clinical isolates from diabetic foot ulcers at the highest concentration of honey tested (9.1%). The results demonstrate that Fijian honeys could be utilized as herbal remedy for the treatment of diabetic foot ulcers. However, to assess the potential of Fijian honeys on diabetic foot ulcers, there is a need for clinical trials on these wounds.

year: 2009

title: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption

authors: Pasandideh, Seyedmostafa

abstract: In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

text: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

year: 2025

title: School leadership for Māori succeeding as Māori: A Mataatua perspective

authors: Barrett, Te Arani

subjects: tribal leadership

abstract: This thesis recounts how Ngāti Awa leadership principles from the past became evident in Te Kotahitanga schools. The thesis discusses Principals’ actions and reflections of fostering Māori students’ success as Māori in secondary schooling. Through case studies, it identifies the strategies engaged by these effective leaders and explores their undertakings in achieving successful outcomes for Māori with whom they have worked.
This study is an imperative to addressing the long-term levels of education under-achievement for Māori as indigenous people of New Zealand. The nexus for the study comes from Robinson (2007) who identified that ‘in many ways the question of how much impact [school] leaders have on student outcomes is a flawed one, because the answer surely depends on what it is that leaders do’. Marzano et al  (2005) also identifies ‘the relative paucity of empirical studies’ and ‘little specific guidance as to effective practices in school leadership’.
The purpose of this study is to identify the contribution effective leaders have made towards realising Durie’s (2001) landmark goals for Māori advancement, that is, for self-determining prosperity, good health and global participation. Identifying exceptional outcomes for Māori in a range of contexts establishes the criteria for selecting the case studies and therefore the foundation for the study. Successful outcomes include increased participation, better retention, improved academic achievement results and lesser disparities within the selected education sites.

text: School leadership for Māori succeeding as Māori: A Mataatua perspective This thesis recounts how Ngāti Awa leadership principles from the past became evident in Te Kotahitanga schools. The thesis discusses Principals’ actions and reflections of fostering Māori students’ success as Māori in secondary schooling. Through case studies, it identifies the strategies engaged by these effective leaders and explores their undertakings in achieving successful outcomes for Māori with whom they have worked.
This study is an imperative to addressing the long-term levels of education under-achievement for Māori as indigenous people of New Zealand. The nexus for the study comes from Robinson (2007) who identified that ‘in many ways the question of how much impact [school] leaders have on student outcomes is a flawed one, because the answer surely depends on what it is that leaders do’. Marzano et al  (2005) also identifies ‘the relative paucity of empirical studies’ and ‘little specific guidance as to effective practices in school leadership’.
The purpose of this study is to identify the contribution effective leaders have made towards realising Durie’s (2001) landmark goals for Māori advancement, that is, for self-determining prosperity, good health and global participation. Identifying exceptional outcomes for Māori in a range of contexts establishes the criteria for selecting the case studies and therefore the foundation for the study. Successful outcomes include increased participation, better retention, improved academic achievement results and lesser disparities within the selected education sites.

year: 2018