Fire Analysis of The Walter F. Dexter Building- Cal Poly, San Luis Obispo

The purpose of this report is to provide a fire analysis of the Walter F. Dexter Building on the California Polytechnic State University, San Luis Obispo Campus. The report will detail egress analysis, notification systems, and building construction in a prescriptive approach. The report will also include a performance based analysis of the building with a designed fire scenario. This performance based analysis will show that in one of the most likely fire scenarios for the building, there is still ample time for the occupants to escape safely

Measuring inequality using Censored data: A multiple imputation approach

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter’s (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.Income Inequality, Topcoding, Partially Synthetic Data, CPS, Current Population Survey, Generalized Beta of the Second Kind distribution

Measuring Inequality Using Censored Data: A Multiple Imputation Approach

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter’s (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.income inequality, topcoding, partially synthetic data, CPS, Current Population Survey, Generalized Beta of the Second Kind distribution

Measuring Inequality Using Censored Data: A Multiple Imputation Approach

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter's (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.Income inequality, topcoding, partially synthetic data, CPS, current population survey, generalized beta of the second kind distribution

Recent trends in top income shares in the USA: Reconciling estimates from March CPS and IRS tax return data

Although the majority of research on US income inequality trends is based on public-use March CPS data, a new wave of research using IRS tax return data reports substantially higher levels of inequality and faster growing trends. We show that these apparently inconsistent estimates are largely reconciled if the inequality measure and the income distribution are defined in the same way. Using internal CPS data for 1967–2006, we closely match IRS data-based estimates of top income shares reported by Piketty and Saez (2003). Our results imply that any inequality increases since 1993 are concentrated among the top 1 percent of the distribution.US Income Inequality, Top income shares, March CPS, IRS tax return data.

Understanding Product Interest through Mouse-Cursor Tracking Analysis

With third-party cookies being banned, alternative methods to assess users’ interests online are necessary. We propose that analyzing mouse cursor movements can help address this need. Based on the response activation model, we hypothesize that interest in a product will decrease the user’s movement speed and increase the number of submovements. We conducted an online study that monitored users’ mouse movements while they were presented with several products and navigated to a button to indicate purchase intention (yes/no). Following this, participants ranked their interest in each product. Contrary to our prediction, we found that product interest increased speed and decreased the submovement count. This suggests that current theories and metrics for mouse cursor tracking are insufficient for predicting product interest. Further research is needed to develop reliable measures for gauging user interest in products

A novel cognitive cueing approach to gait retraining in Parkinson’s disease: A pilot study

Background: Parkinson’s disease (PD) impairs gait performance, which can lead to falls and decreased quality of life. This study examined the feasibility of implementing a novel home-based intervention designed to elicit gait improvement in individuals with PD. Methods: Five participants with PD completed a two-week home-based gait retraining intervention designed around guided video feedback. Semi-structured interviews were conducted postintervention and two months postintervention to acquire feedback from the participants about their experience with the intervention. Spatiotemporal parameters of gait and functional mobility were assessed pre and postintervention and at two months postintervention. Results: Participants reported high levels of usability and expressed they believed that the intervention improved their gait and led to a fortified sense of ability and revived sense of empowerment. Comparisons of spatiotemporal and mobility parameters of gait identified that improvements occurred between preintervention and postintervention—step length (x̄ = 10.7%), gait velocity (x̄ = 15.1%), and TUG scores (x̄ = 9.8%)—and between preintervention and two months postintervention—step length (x̄ = 3.9%), gait velocity (x̄ = 9.9%), and TUG scores (x̄ = 4.2%). Conclusions: Guided home-based video training has potential to be an effective treatment strategy for improving gait impairment among individuals with PD

Typing Fast versus Typing Slow: Using Typing Dynamics to Reveal Authentic and Imposter Users

Real-time assessment of users' cognitive states has practical importance, allowing organizations to infer user behaviors. Realizing its importance, prior studies – specifically those using mouse cursor movements – have applied various theories to answer a similar question, i.e., how does a high cognitive load influence the users' device usage behavior? While numerous activities can increase cognitive load, we argue that the mechanisms behind how humans process information can more holistically be explained using Dual Process Theory (DPT) (i.e., when cognitive load is either low or high) and can be applied under a broad range of usage contexts. Using a within-participant experiment and a simple typing task, we demonstrate that DPT is robust to work by examining DPT and mouse cursor movements. Specifically, users' typing speed and task execution are significantly slower when engaged in the task (System 2) and significantly faster when completing the task with lower cognitive effort and engagement (System 1)

When is enough, enough? An Examination of Student Engagement when Watching Online Group Project Presentations

In traditional face-to-face classes, conventional wisdom suggests that delivering and watching group project presentations is a valuable learning experience. In this research, we examine the limits of student engagement and learning in an asynchronous online context. Specifically, 249 undergraduate students were assigned to perform peer evaluations of multiple ten-minute project presentations. The online learning platform collected objective viewing behavior for each student, allowing us to use viewing time as a proxy for engagement. We also collected self-reported attitudes towards the assignment, finding that while students value providing feedback, they do not consider it a valuable use of their time. Students who engage more are also likely to receive a better final course grade. Finally, students exhibit different types of viewing behavior (i.e., personas) when evaluating multiple videos. Based on these results, we provide suggestions for improving the design of online group presentation and peer-review assignments