title: Tensile properties of ovine skeletal muscle fibres: Thermal denaturation and selective removal of titin.

authors: Marsh, Julie May

abstract: The stiffness of chemically skinned single ovine skeletal psoas major muscle fibres is affected by many post-mortem events. These same events produce highly variable eating quality attributes in whole meat. This thesis focuses on three aspects of these events; sarcomere length, titin degradation and heat. Each of these factors was experimentally manipulated in vitro and the effect on the stiffness of single muscle fibres was measured. A technique was developed to apply strain to the fibre while recording the corresponding stress output. To study the effect of sarcomere length on single fibre stiffness before and after heating to 80°C (cooking), each fibre was set to one of six sarcomere lengths; 1.6, 1.8, 2.1, 2.7, 3.0, and 4.2 μm. Oscillations of 5 Hz were designed to stretch each fibre by 5 nm/half sarcomere. At sarcomere lengths below resting length (2.1 μm), the stiffness of uncooked fibres remained relatively unchanged, but appeared to increase in cooked fibres in proportion to the sarcomere length prior to cooking. The effect of selective titin degradation on the structural characteristics of single fibres, before and after cooking, was also examined using a low concentration of trypsin to selectively digest titin. The stiffness of raw fibres was unaffected by titin removal, but heating to 80°C caused significant changes in the stiffness of trypsintreated fibres. Two fixed sarcomere lengths were subsequently used to demonstrate the temperature dependence of fibre stiffness; l.6μm and 2.lμm. Although the fibre stiffness response at both sarcomere lengths was similar during heating, when the temperature was reduced from 80°C the response of fibres with a sarcomere length of 2.1 μm exhibited lower stiffness responses to each temperature than fibres with a sarcomere length of 1.6 μm. The length of the I-band appears to be important, especially after cooking. As shown in Chapter 2, the length of the I-band contributed to the final stiffness of the thermally denatured fibres, independent of the number of cross-bridges that had formed prior to cooking. As the I-band increased in length in the raw state, the more compliant the fibres became after cooking. This thesis provides information on, and a better understanding of, the structural and mechanical properties of single muscle fibres as a result of three different postmortem factors. Understanding how these factors affect the meat on a cellular level can help us to better control the environmental and postmortem influences on whole meat quality.

text: Tensile properties of ovine skeletal muscle fibres: Thermal denaturation and selective removal of titin. The stiffness of chemically skinned single ovine skeletal psoas major muscle fibres is affected by many post-mortem events. These same events produce highly variable eating quality attributes in whole meat. This thesis focuses on three aspects of these events; sarcomere length, titin degradation and heat. Each of these factors was experimentally manipulated in vitro and the effect on the stiffness of single muscle fibres was measured. A technique was developed to apply strain to the fibre while recording the corresponding stress output. To study the effect of sarcomere length on single fibre stiffness before and after heating to 80°C (cooking), each fibre was set to one of six sarcomere lengths; 1.6, 1.8, 2.1, 2.7, 3.0, and 4.2 μm. Oscillations of 5 Hz were designed to stretch each fibre by 5 nm/half sarcomere. At sarcomere lengths below resting length (2.1 μm), the stiffness of uncooked fibres remained relatively unchanged, but appeared to increase in cooked fibres in proportion to the sarcomere length prior to cooking. The effect of selective titin degradation on the structural characteristics of single fibres, before and after cooking, was also examined using a low concentration of trypsin to selectively digest titin. The stiffness of raw fibres was unaffected by titin removal, but heating to 80°C caused significant changes in the stiffness of trypsintreated fibres. Two fixed sarcomere lengths were subsequently used to demonstrate the temperature dependence of fibre stiffness; l.6μm and 2.lμm. Although the fibre stiffness response at both sarcomere lengths was similar during heating, when the temperature was reduced from 80°C the response of fibres with a sarcomere length of 2.1 μm exhibited lower stiffness responses to each temperature than fibres with a sarcomere length of 1.6 μm. The length of the I-band appears to be important, especially after cooking. As shown in Chapter 2, the length of the I-band contributed to the final stiffness of the thermally denatured fibres, independent of the number of cross-bridges that had formed prior to cooking. As the I-band increased in length in the raw state, the more compliant the fibres became after cooking. This thesis provides information on, and a better understanding of, the structural and mechanical properties of single muscle fibres as a result of three different postmortem factors. Understanding how these factors affect the meat on a cellular level can help us to better control the environmental and postmortem influences on whole meat quality.

year: 2005

title: Performance evaluation of SCATS-controlled intersections in New Zealand with machine-learning delay prediction and signal-timing  optimisation

authors: Khatri, Shivu

abstract: Adaptive signal systems such as the Sydney Coordinated Adaptive Traffic System (SCATS) 
controls traffic signal system by adjusting phase splits and cycle length in real time. The 
adjustments rely only on the last few seconds of detector data, the system lacks real-time predictive 
and optimization functionalities, allowing queues and emissions to build. Fine-tuning of signal 
timing by even a small increment can derive large economic and environmental benefits for the 
wider network. This short-coming is becoming increasingly significant in New Zealand, where 
transport already contributes 39 % of national CO₂ emissions and intersection delay costs 
Auckland more than NZ $1 billion each year.
This thesis evaluates present and future performance of two representative SCATS-controlled 
intersections—Albany, Auckland and Ruakura, Hamilton—and tests whether supervised machine-
learning (ML) models can predict delay and recommend cycle lengths that improvises the native 
SCATS logic.
Field data was used to build, calibrate, and validate base SIDRA intersection models; saturation-
flow rates were matched within ±5 % of observations and all lane-movements satisfied degree-of-
saturation criteria. A Monte-Carlo routine expanded 14 SCATS peak-period volume logs into 98 
synthetic volume scenarios, which were re-run in SIDRA to obtain delay, queue, fuel, and emission 
outputs. Four machine learning models—XGBoost, Random Forest, Support Vector Regression, 
and k-Nearest Neighbours—were trained on the synthetic dataset; the best two, XGBoost and 
Random Forest were combined in an ensemble to give a delay-prediction model. 
Furthermore, baseline analysis (2024) found both sites operating at Level-of-Service D, with 
several right-turn lanes already oversaturated and 95th-percentile queues exceeding storage. Ten-
year growth projections degraded both intersections to LoS F well before the planning horizon. 
The ML ensemble predicted average control delay with MAE ≈ 4 s veh⁻¹ and, under moderate 
demand, shortened cycles by 15–46 s, cutting delay by 13 % and CO₂ by 5 % relative to SCATS
timings; though the benefits diminished under heavy oversaturation.
Data-driven machine learning models can provide cycle-by-cycle delay forecasts and substantial
performance gains, but require additional high-degree of saturations and longer cycle lengths
training data for robust operation and more generalization during severe congestion. Integration of 
such predictors and optimizers with SCATS would provide practical step toward meeting New 
Zealand’s 2035 emission-reduction targets.

text: Performance evaluation of SCATS-controlled intersections in New Zealand with machine-learning delay prediction and signal-timing  optimisation Adaptive signal systems such as the Sydney Coordinated Adaptive Traffic System (SCATS) 
controls traffic signal system by adjusting phase splits and cycle length in real time. The 
adjustments rely only on the last few seconds of detector data, the system lacks real-time predictive 
and optimization functionalities, allowing queues and emissions to build. Fine-tuning of signal 
timing by even a small increment can derive large economic and environmental benefits for the 
wider network. This short-coming is becoming increasingly significant in New Zealand, where 
transport already contributes 39 % of national CO₂ emissions and intersection delay costs 
Auckland more than NZ $1 billion each year.
This thesis evaluates present and future performance of two representative SCATS-controlled 
intersections—Albany, Auckland and Ruakura, Hamilton—and tests whether supervised machine-
learning (ML) models can predict delay and recommend cycle lengths that improvises the native 
SCATS logic.
Field data was used to build, calibrate, and validate base SIDRA intersection models; saturation-
flow rates were matched within ±5 % of observations and all lane-movements satisfied degree-of-
saturation criteria. A Monte-Carlo routine expanded 14 SCATS peak-period volume logs into 98 
synthetic volume scenarios, which were re-run in SIDRA to obtain delay, queue, fuel, and emission 
outputs. Four machine learning models—XGBoost, Random Forest, Support Vector Regression, 
and k-Nearest Neighbours—were trained on the synthetic dataset; the best two, XGBoost and 
Random Forest were combined in an ensemble to give a delay-prediction model. 
Furthermore, baseline analysis (2024) found both sites operating at Level-of-Service D, with 
several right-turn lanes already oversaturated and 95th-percentile queues exceeding storage. Ten-
year growth projections degraded both intersections to LoS F well before the planning horizon. 
The ML ensemble predicted average control delay with MAE ≈ 4 s veh⁻¹ and, under moderate 
demand, shortened cycles by 15–46 s, cutting delay by 13 % and CO₂ by 5 % relative to SCATS
timings; though the benefits diminished under heavy oversaturation.
Data-driven machine learning models can provide cycle-by-cycle delay forecasts and substantial
performance gains, but require additional high-degree of saturations and longer cycle lengths
training data for robust operation and more generalization during severe congestion. Integration of 
such predictors and optimizers with SCATS would provide practical step toward meeting New 
Zealand’s 2035 emission-reduction targets.

year: 2025

title: Te Ao Hurihuri population: Past, present & future

authors: Kukutai, Tahu

subjects: Maori

abstract: The NIDEA Te Ao Hurihuri series uses data from the New Zealand Census of Population and Dwellings to examine key aspects of Maori population change.

text: Te Ao Hurihuri population: Past, present & future The NIDEA Te Ao Hurihuri series uses data from the New Zealand Census of Population and Dwellings to examine key aspects of Maori population change.

year: 2014-07