title: Machine Learning for PAC1D and SESE

authors: Kurz, Jason A.

abstract: This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering the most efficient method for approximating SESE with sparse data utilization. The methods explored include; physics-informed neural networks, deep Galerkin method, deep mixed residual methods, operator network, deep operator network, Fourier neural operator, physics-informed Fourier neural operator, and physics-informed kernel neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernel neural operator showing the most potential for approximating SESE’s behavior.

text: Machine Learning for PAC1D and SESE This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering the most efficient method for approximating SESE with sparse data utilization. The methods explored include; physics-informed neural networks, deep Galerkin method, deep mixed residual methods, operator network, deep operator network, Fourier neural operator, physics-informed Fourier neural operator, and physics-informed kernel neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernel neural operator showing the most potential for approximating SESE’s behavior.

year: 2023-04-03

title: Non-South East Asians have a better running economy and different anthropometrics and biomechanics than South East Asians

authors: Patoz, Aurélien

subjects: Science & Technology

abstract: Running biomechanics and ethnicity can influence running economy (RE), which is a critical factor of running performance. Our aim was to compare RE of South East Asian (SEA) and non‑South East Asian (non‑SEA) runners at several endurance running speeds (10–14 km/h) matched for on‑road racing performance and sex. Secondly, we explored anthropometric characteristics and relationships between RE and anthropometric and biomechanical variables. SEA were 6% less economical (p = 0.04) than non‑SEA. SEA were lighter and shorter than non‑SEA, and had lower body mass indexes and leg lengths (p ≤ 0.01). In terms of biomechanics, a higher prevalence of forefoot strikers in SEA than non‑SEA was seen at each speed tested (p ≤ 0.04). Furthermore, SEA had a significantly higher step frequency (p = 0.02), shorter contact time (p = 0.04), smaller footstrike angle (p < 0.001), and less knee extension at toe‑off (p = 0.03) than non‑SEA. Amongst these variables, only mass was positively correlated to RE for both SEA (12 km/h) and non‑SEA (all speeds); step frequency, negatively correlated to RE for both SEA (10 km/h) and non‑SEA (12 km/h); and contact time, positively correlated to RE for SEA (12 km/h). Despite the observed anthropometric and biomechanical differences between cohorts, these data were limited in underpinning the observed RE differences at a group level. This exploratory study provides preliminary indications of potential differences between SEA and non‑SEA runners warranting further consideration. Altogether, these findings suggest caution when generalizing from non‑SEA running studies to SEA runners.

text: Non-South East Asians have a better running economy and different anthropometrics and biomechanics than South East Asians Running biomechanics and ethnicity can influence running economy (RE), which is a critical factor of running performance. Our aim was to compare RE of South East Asian (SEA) and non‑South East Asian (non‑SEA) runners at several endurance running speeds (10–14 km/h) matched for on‑road racing performance and sex. Secondly, we explored anthropometric characteristics and relationships between RE and anthropometric and biomechanical variables. SEA were 6% less economical (p = 0.04) than non‑SEA. SEA were lighter and shorter than non‑SEA, and had lower body mass indexes and leg lengths (p ≤ 0.01). In terms of biomechanics, a higher prevalence of forefoot strikers in SEA than non‑SEA was seen at each speed tested (p ≤ 0.04). Furthermore, SEA had a significantly higher step frequency (p = 0.02), shorter contact time (p = 0.04), smaller footstrike angle (p < 0.001), and less knee extension at toe‑off (p = 0.03) than non‑SEA. Amongst these variables, only mass was positively correlated to RE for both SEA (12 km/h) and non‑SEA (all speeds); step frequency, negatively correlated to RE for both SEA (10 km/h) and non‑SEA (12 km/h); and contact time, positively correlated to RE for SEA (12 km/h). Despite the observed anthropometric and biomechanical differences between cohorts, these data were limited in underpinning the observed RE differences at a group level. This exploratory study provides preliminary indications of potential differences between SEA and non‑SEA runners warranting further consideration. Altogether, these findings suggest caution when generalizing from non‑SEA running studies to SEA runners.

year: 2022-04-15

title: Analysis of errors in the writing of first year university students of Māori

authors: Earnshaw, Lorene

subjects: Te reo Māori

abstract: This paper presents a survey of the range of grammatical and lexical errors made in written Māori by University students taking an introductory course in Māori language. We begin by introducing, and discussing the motivation for, an error classification system which accommodates different classes of error. We then provide an analysis of errors in three different types of student writing: homework assignments, impromptu tests and examination transcripts. We conclude with some remarks about the patterns of error which we found.

text: Analysis of errors in the writing of first year university students of Māori This paper presents a survey of the range of grammatical and lexical errors made in written Māori by University students taking an introductory course in Māori language. We begin by introducing, and discussing the motivation for, an error classification system which accommodates different classes of error. We then provide an analysis of errors in three different types of student writing: homework assignments, impromptu tests and examination transcripts. We conclude with some remarks about the patterns of error which we found.

year: 2004-09