Comparing Learning Platform Impact on Low Vision Education for Occupational Therapists

This pilot study examines the impact of face-to-face, remote, and hybrid learning platforms on satisfaction, confidence, and knowledge-application of occupational therapy practitioners during a synchronous low vision continuing education program. Fifteen participants were divided into three groups, each corresponding to one learning platform. They engaged in two 45-min learning sessions and completed pre, post, and follow-up surveys to measure the impact of the learning platform on the dependent variables of satisfaction, confidence, and knowledge application. No significant differences were found between learning platforms for the three variables, but improvements from pre to follow-up survey were found to be significant for confidence and knowledge application for all groups. These findings indicate that similar education provided to occupational therapy practitioners may result in improved confidence and knowledge application to clinical practice from the beginning to the end of the educational program, despite the learning platform. Flexibility with online learning options increased participation and adherence rates. Synchronous remote and hybrid learning platforms may be as effective as traditional face-to-face methods, specifically with increasing practitioner confidence and knowledge application. Remote options may reduce peer interactions but increase flexibility and convenience with scheduling for program scalability and accessibility

Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e. duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naive approach, utilizing one random sample from the input distribution (i.e. disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real-time applications.Comment: 15 pages, 6 figure

Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure

Event History Analysis of Dynamic Communication Networks

Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions amongst nodes. We propose to model dynamic directed communication networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic results of the resulting estimation are established. Numerical results are performed to demonstrate effectiveness of our proposal

Explaining the complexities of Hong Kong’s financial securities legislation to enhance trust among small investors through individual case reports as stories

This research project set out with the purpose of contributing to trusting financial securities regulation in Hong Kong for small investors when the system is highly complex. The aim of this particular project, to contribute to that purpose, was to explore sets of cases reported on by the regulators and by the media. The cases were selected on the basis of their variety; that variety being what it is that frightens people about investing which includes being cheated; not understanding the rules; worried about safeguards against contraventions such as money laundering, corruption, insider dealing and more negative images. It is a boundaried study, focusing on financial regulation and practices in Hong Kong. However it surfaces commonalties or learnings for other financial centers. The research was motivated by a need for small investors to understand how the system works, its safeguards and its rate of response to addressing anomalies so that they can invest with more confidence. This document is to contribute to communicating a complex system to the ordinary investor and young trainees or young legal practitioners charged with helping small investors who may have a grievance. It uses legal cases as a form of storytelling or more precisely ‘parabling’ – the good and the bad, the David and Goliath, which serve to illustrate the anomalies in a way that helps people not only to have more understanding of the systems but also to quickly identify with what can go wrong even for the big players. This research was carried out in part during Hong Kong’s social unrest and before China’s changes to Basic Law (2020) and before the global pandemic of 2020. With these events this research has become historical in a short space of time although nothing significant had changed in financial securities regulation at the time of writing. The document is in three parts to support navigation and cross-reference: Part 1 is the main context, literature and cases studies. Part 2 contains all the bodies involved in the different cases relating to financial securities and Part 3 contains the Appendices

Understanding consumer responses to special event entertainment (SEE) in shopping centres

This paper reviews the literature on the use of entertainment in shopping centres and outlines the constructs believed to impact upon consumer’s responses to special event entertainment. Special event entertainment (SEE) refers to entertainment events or activities that are offered on an occasional, temporary or discontinued basis in shopping centres. Examples of SEE include school holiday entertainment and fashion shows (Parsons, 2003; Sit, Merrilees, & Birch, 2003). Using SEE, shopping centre management seeks to entice consumer patronage, increase patron traffic or promote the shopping centre brand. Despite the popularity of SEE in shopping centres, very little academic research (e.g. Parsons, 2003; Sit, Merrilees, & Birch, 2003) has either conceptually or empirically examined how consumers perceive or respond to SEE. This research presents a conceptual model that examines the determinants and outcomes of consumer responses to SEE, In particular, consumer responses to SEE are represented by SEE proneness and overall appreciation of SEE. These SEE responses are proposed to be determined by sensation-seeking tendencies and perceived value of SEE. Eight propositions are presented to explain the relationships of SEE responses with their determinants and outcomes. These relationships will be empirically tested in future research. Research implications of the conceptual model are also presented