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The Markowitz model is an established approach to portfolio optimization that constructs efficient frontiers allowing users to make optimal tradeoffs between risk and return. However, a limitation of this approach is that it assumes future asset returns and covariances will be identical to the asset's historical data, or that these model parameters can be accurately estimated, a notion which often does not hold in practice. Markowitz efficient frontiers are square root second-order polynomials that can be represented by three parameters, thus providing a significant dimensionality reduction of the lookback covariances and growth of the assets. Using this dimensionality reduction, we propose an extension to the Markowitz model that accounts for the nonstationary behavior of the portfolio assets' return and covariance without the necessity to forecast the complex covariance matrix and assets growths, something that has proven to be extremely difficult. Our methodology allows users to forecast the three efficient frontier coefficients using a time-series regression. By observing similar efficient frontiers, this forecasted efficient frontier can be used to select optimal assets mean-variance tradeoffs (asset weights). For exploratory testing we employ a set of assets that span a large portion of the market to demonstrate and validate this new approach.

Social Network Services (SNS) are systems that allow users to build social relations with one another, with one of the largest SNSs being Facebook, totaling 2.6 billion active monthly users in 2020. Their live streaming service, Facebook Live, is one of the fastest-growing branches of the company, allowing creators to synchronously broadcast original content to the public. However, in the rapidly growing world of technology, Facebook Live faces fierce competition from other live streaming platforms (Twitch, YouTube Live, etc.) and well as other video-on-demand providers (Netflix, Hulu, TikTok, etc.). To better understand current issues and future directions, our team focused on the Facebook Live platform, to develop a three to five-year strategic plan for the platform. We focus on Facebook Live’s growth opportunities from a multitude of perspectives, including the competitive landscape, interface modification, future projections based on historical trends, and competitive analysis. Our approach utilizes the systems analysis process, focusing top-down on objectives and metrics. To produce a comprehensive strategy for future operations, we employ analytical methods ranging from quantitative data analysis to qualitative exploration of industry trends. These quantitative methods include statistical analysis, time-series forecasting, and natural language processing. Qualitative methods include domain research into the history, current state, and possible future for live-streaming. Forthcoming, the results for the complete analysis will be synthesized into a multi-recommendation strategic report to provide Facebook with flexible guidance for continuing operations. We also present a comments summarization and visualization feature for viewers and creators, three to five-year market forecasts after COVID-19 lockdowns, and attractive emerging markets including education and morning shows.

Whole-body vibration and muscle fatigue have both been shown to delay the trunk muscle reflex response and increase trunk muscle activation, leading to an increased risk of low back injuries. However, the effects of whole-body vibration on previously fatigued trunk muscles have never been tested, despite tests showing that prolonged exposure to whole-body vibration can lead to muscle fatigue. The purpose of this research was to investigate the effects of muscle fatigue on muscle latency, muscle activation and perceived discomfort when exposed to whole-body vibration. The results showed that a fatigued muscle state resulted in increased muscle latency, muscle activation and perceived discomfort, which all escalate the risk of low back injuries. Additionally, the ISO 2631-1 comfort ratings did not increase with fatigue, showing a disconnect between these comfort ratings and the perceived discomfort ratings in a fatigued muscle state.

The introductory chapter provides a brief exploration of the history of childhood, and childhood representations, in Western popular thinking over the last 500 years. It also provides a brief discussion of the implications of these representations on policy, the study of children, and adult social cognition. In Chapter 2, two experiments explore the potential effects of including children in representations of outgroups on attitudes towards the outgroup, with inconclusive findings. In Chapter 3, three experiments explore the effects of priming the category of children on impressions of a novel ambiguous target (the Donald paradigm). Methodological issues and inconsistent findings mar the interpretation of effects, but an improved set of category labels for future studies of child category priming are considered in the general discussion. In Chapter 4 I take a step back and systematically explore the ways in which different childhood age groups (babies, toddlers, children, and teenagers) are represented. The first stage of data collection determined the typical age boundaries identified for children and the labels by which we delineate these different age groups. The next stage identified the emotions, beliefs, and behaviours relevant to attitudes to these groups in an open-ended listing exercise. There were differences in the content and endorsement of attitude components towards the age groups, with broadly more negative components towards older child groups than younger ones. In addition, there were fewer nurturing related components but more reparative behaviours as the age of the child increased. Chapter 5 built on these findings by developing the Child Attitude Component Scale (CACS) and testing the convergent and discriminant validity of this scale. Scores on the CACS were related but distinct from scores on measures about beliefs about humanity in general, such as the Humanity Esteem Scale and Polarity Scale. The CACS was also distinct from individual differences in emotional regulation and appraisal, self-esteem, social desirability responding and beliefs about social hierarchy. Potential spheres for testing the CACS as a predictive tool in situations concerning children are discussed alongside limitations and future directions in Chapter 6.