title: Carbon emissions efficiency and economics of combined heat and power in New Zealand

authors: Atkins, Martin John

abstract: Combined Heat and Power (CHP) or cogeneration, is a common and often cost effective method to maximise the efficiency and utilisation of fossil fuels. Greenhouse Gas (GHG) emissions from the electricity generated using CHP is also an important factor to consider, especially within the framework of emissions reduction and uptake of renewable generation. This paper will present a detailed analysis of the economics of industrial CHP within New Zealand and examine the potential of CHP to contribute to GHG emissions reduction. An emissions factor from electricity generation using CHP is defined based on the marginal efficiency of electricity generation. The economics of CHP in New Zealand can be favourable under certain conditions although the emissions of generation using fossil fuels in all cases was higher than grid purchased electricity, due to high levels of renewable generation. A reduction in emissions can occur in countries that have medium to high Grid Emissions Factors (GEF) such as the US, UK, Australia, India, and China. Countries with GEF less than around 0.2 t CO2-eq /MW el would need to utilise biomass to achieve large emissions reductions using CHP.

text: Carbon emissions efficiency and economics of combined heat and power in New Zealand Combined Heat and Power (CHP) or cogeneration, is a common and often cost effective method to maximise the efficiency and utilisation of fossil fuels. Greenhouse Gas (GHG) emissions from the electricity generated using CHP is also an important factor to consider, especially within the framework of emissions reduction and uptake of renewable generation. This paper will present a detailed analysis of the economics of industrial CHP within New Zealand and examine the potential of CHP to contribute to GHG emissions reduction. An emissions factor from electricity generation using CHP is defined based on the marginal efficiency of electricity generation. The economics of CHP in New Zealand can be favourable under certain conditions although the emissions of generation using fossil fuels in all cases was higher than grid purchased electricity, due to high levels of renewable generation. A reduction in emissions can occur in countries that have medium to high Grid Emissions Factors (GEF) such as the US, UK, Australia, India, and China. Countries with GEF less than around 0.2 t CO2-eq /MW el would need to utilise biomass to achieve large emissions reductions using CHP.

year: 2017

title: Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

authors: Chen, Yifan

subjects: Science & Technology

abstract: We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted “activator” nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

text: Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted “activator” nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

year: 2019

title: Explaining Maori under-achievement in standardised reading tests: The role of social and individual characteristics

authors: Lock, Kelly J.

subjects: education

abstract: In New Zealand, Maori students have lower educational attainment levels than their Pakeha counterparts, which is a major contributor to the earnings inequality between these two groups. This study attempts to identify determinants of the gap in educational attainment using data on reading test scores for a sample of 3031 15‐year‐old Maori and Pakeha students. Using a decomposition method that is commonly applied to income inequality analyses, the determinants of the reading literacy test score gap between Maori and Pakeha students are identified. The main contributors to this gap are family factors, student's opinions, and school factors (including decile). An understanding of the determinants of the test score gap provides insights into how effective policy makers can be at reducing this inequality.

text: Explaining Maori under-achievement in standardised reading tests: The role of social and individual characteristics In New Zealand, Maori students have lower educational attainment levels than their Pakeha counterparts, which is a major contributor to the earnings inequality between these two groups. This study attempts to identify determinants of the gap in educational attainment using data on reading test scores for a sample of 3031 15‐year‐old Maori and Pakeha students. Using a decomposition method that is commonly applied to income inequality analyses, the determinants of the reading literacy test score gap between Maori and Pakeha students are identified. The main contributors to this gap are family factors, student's opinions, and school factors (including decile). An understanding of the determinants of the test score gap provides insights into how effective policy makers can be at reducing this inequality.

year: 2008