Speech Sound Status of Infants during the Sixteenth, Seventeenth and Eighteenth Months

The speech sounds of twenty-nine infants from the sixteenth to the eighteenth months were transcribed in the International Phonetic Alphabet. Comparisons were made of vowel and of consonant frequencies as between each of the three months. Further comparisons were made with other age groups in order to show the developmental status of the use of speech elements of the age group

Visual Development for Wellspring

The primary focus of this thesis is the study of visual development for worldbuilding, starting with creative writing and documentation and translating the written content into visual concepts in both 2D and 3D. This project includes an original narrative, setting, and characters and explores aspects of the visual development pipeline. The content below contains work in visual research, 2D character design, 3D character sculpting, 3D printing and assembly, hard-surface modeling, matte-painting, illustration, compositing, and heavy creative writing

Network theory and CAD collections

Graph and network theory have become commonplace in modern life. So widespread in fact that most people not only understand the basics of what a network is, but are adept at using them and do so daily. This has not long been the case however and the relatively quick growth and uptake of network technology has sparked the interest of many scientists and researchers. The Science of Networks has sprung up, showing how networks are useful in connecting molecules and particles, computers and web pages, as well as people. Despite being shown to be effective in many areas, network theory has yet to be applied to mechanical engineering design. This work makes use of network science advances and explores how they can impact Computer Aided Design (CAD) data. CAD data is considered the most valuable design data within mechanical engineering and two places large collections are found are educational institutes and industry. This work begins by exploring 5 novel networks of different sized CAD collections, where metrics and network developments are assessed. From there collections from educational and industrial settings are explored in depth, with novel methods and visualisations being presented. The results of this investigation show that network science provides interesting analysis of CAD collections and two key discoveries are presented: network metrics and visualisations are shown to be effective at highlighting plagiarism in collections of students' CAD submissions. Also when used to assess collections of real world company data, network theory is shown to provide unique metrics for analysis and characterising collections of CAD and associated data

Predicting critical care needs during a pandemic

NHS Lanarkshire is the third largest Health Board in Scotland, serving a population of 655,000 across rural and urban communities in Lanarkshire. Its 12,000-strong team of staff work in communities, health centres, clinics and offices in the region and at three district general hospitals. When COVID-19 emerged, it posed huge questions for healthcare organisations globally. How much capacity would be needed to care for those who became infected? Would there be enough ventilators and other equipment to care for patients appropriately? And so much more. At NHS Lanarkshire, advice from both UK and Scottish governments had suggested that the major NHS Trusts (in England and Wales) and Health Boards (in Scotland) prepare for the worst-case scenario of a fivefold increase in demand for critical care in the Spring 2020 peak of the COVID-19 pandemic. This left NHS Lanarkshire with the challenge of trying to predict, at very short notice, the critical care resources they would actually require over the coming weeks and months. Time was of the essence for decision making and preparations required for the potential demand surges. Working in close collaboration with NHS Lanarkshire, the University of Strathclyde Business School health systems experts used Simul8 modelling software to predict critical care needs at the start of the COVID-19 pandemic

Perceptions of Self-Efficacy & Support Among Secondary Early-Career Teachers and their Principals During the COVID-19 Pandemic

In response to challenges faced by middle and high school educators during the COVID-19 pandemic, a study was conducted in the Spring of 2021 involving 33 early-career mathematics teachers and eight supervising school principals in the State of California. These participants completed detailed surveys which provided demographic information, as well as perceptions of support, efficacy and job satisfaction. Findings show a variety of associations among teacher perceptions of support and their efficacy and job satisfaction in the face of challenging circumstances. As it related to principal support and recognition, principal participants expressed confidence in their ability to support teachers as a result of the pandemic, including understanding best practices for evaluating teachers for online teaching. Additionally, while agreeing that teachers at their schools needed more professional development in instructional practice, participating school principals felt they supported their teachers as they adapted instruction due to the COVID-19 pandemic. This study aspires to inform practitioners and policymakers alike in their understanding of teachers and their principals as they responded to significant challenges as a result of the COVID-19 pandemic