title: Evidence of variable performance responses to the Nike 4% shoe: Definitely not a game-changer for all recreational runners

authors: Hébert-Losier, Kim

abstract: Purpose: We compared running economy (RE) and 3-km time-trial (TT) performances of male recreational runners wearing the Nike Vaporfly 4% (NIKE), lightweight racing flats (FLAT), and their habitual footwear (OWN). Methods: Eighteen male recreational runners [age: 33.5 (11.9) y, V̇O2peak: 55.8 (4.4) mL·kg-1·min-1] attended 4 sessions ~7 days apart. The first session consisted of a V̇O2peak test to inform subsequent RE speeds set at 60, 70, and 80% of the speed eliciting V̇O2peak. In subsequent sessions, treadmill RE and 3-km TT were assessed in the three footwear in a randomised, counterbalanced crossover design. Results: RE was improved in NIKE (3.6 to 4.5%, p ≤ 0.002) and FLAT (2.4 to 4.0%, p ≤ 0.042) versus OWN across intensities, with a trivial difference between NIKE and FLAT (1.0 to 1.6%, p ≥ 0.325). NIKE 3-km TT (11:07.6 ± 0:56.6 mm:ss) was superior to OWN by 16.6 s (2.4%, p = 0.005) and FLAT by 13.0 s (1.8%, , p = 0.032), with similar times between OWN and FLAT (0.5%, , p = 0.747). Only 29% of runners were more economical across intensities and faster in NIKE. Conclusions: Overall, our findings indicate that NIKE could benefit RE in male recreational runners at relative speeds when compared to OWN, but not when compared to FLAT. More runners exhibited better TT performances in NIKE (61%) versus FLAT (22%) and OWN (17%). The high variability in individual RE (-3.1 to 12.1%) and TT (-3.8 to 8.2%) shoe-responses suggests that individualisation of running footwear prescription is warranted.

text: Evidence of variable performance responses to the Nike 4% shoe: Definitely not a game-changer for all recreational runners Purpose: We compared running economy (RE) and 3-km time-trial (TT) performances of male recreational runners wearing the Nike Vaporfly 4% (NIKE), lightweight racing flats (FLAT), and their habitual footwear (OWN). Methods: Eighteen male recreational runners [age: 33.5 (11.9) y, V̇O2peak: 55.8 (4.4) mL·kg-1·min-1] attended 4 sessions ~7 days apart. The first session consisted of a V̇O2peak test to inform subsequent RE speeds set at 60, 70, and 80% of the speed eliciting V̇O2peak. In subsequent sessions, treadmill RE and 3-km TT were assessed in the three footwear in a randomised, counterbalanced crossover design. Results: RE was improved in NIKE (3.6 to 4.5%, p ≤ 0.002) and FLAT (2.4 to 4.0%, p ≤ 0.042) versus OWN across intensities, with a trivial difference between NIKE and FLAT (1.0 to 1.6%, p ≥ 0.325). NIKE 3-km TT (11:07.6 ± 0:56.6 mm:ss) was superior to OWN by 16.6 s (2.4%, p = 0.005) and FLAT by 13.0 s (1.8%, , p = 0.032), with similar times between OWN and FLAT (0.5%, , p = 0.747). Only 29% of runners were more economical across intensities and faster in NIKE. Conclusions: Overall, our findings indicate that NIKE could benefit RE in male recreational runners at relative speeds when compared to OWN, but not when compared to FLAT. More runners exhibited better TT performances in NIKE (61%) versus FLAT (22%) and OWN (17%). The high variability in individual RE (-3.1 to 12.1%) and TT (-3.8 to 8.2%) shoe-responses suggests that individualisation of running footwear prescription is warranted.

year: 2020

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: Young people, technology and the future of te Reo Māori

authors: Keegan, Te Taka Adrian Gregory

abstract: After decades of neglect and, in some instances, suppression, te reo Māori has achieved a degree of recognition and support from the Government and people. Language strategies have been written, schooling is available in both languages, and since 1987 the language has had official status. However, despite demonstrable progress in some areas, it remains a small-minority language. Within New Zealand there are 157,000 speakers of te reo Māori, about 4.1 percent of the population (Statistics New Zealand: Te Tari Tatau, 2007), and the language is classified as 'vulnerable' by UNESCO (Mosley, 2010). The language continues to face considerable pressure from English, not just because English is a majority national language but because it is an increasingly global language, with a significant presence in culture, science, media and technology. This chapter considers the relationships between young people, technology and te reo Māori. It argues that technology is an important domain of use for te reo Māori, particularly the continued use of the language by young people.

text: Young people, technology and the future of te Reo Māori After decades of neglect and, in some instances, suppression, te reo Māori has achieved a degree of recognition and support from the Government and people. Language strategies have been written, schooling is available in both languages, and since 1987 the language has had official status. However, despite demonstrable progress in some areas, it remains a small-minority language. Within New Zealand there are 157,000 speakers of te reo Māori, about 4.1 percent of the population (Statistics New Zealand: Te Tari Tatau, 2007), and the language is classified as 'vulnerable' by UNESCO (Mosley, 2010). The language continues to face considerable pressure from English, not just because English is a majority national language but because it is an increasingly global language, with a significant presence in culture, science, media and technology. This chapter considers the relationships between young people, technology and te reo Māori. It argues that technology is an important domain of use for te reo Māori, particularly the continued use of the language by young people.

year: 2014