A new solution approach to polynomial LPV system analysis and synthesis

Based on sum-of-squares (SOS) decomposition, we propose a new solution approach for polynomial LPV system analysis and control synthesis problems. Instead of solving matrix variables over a positive definite cone, the SOS approach tries to find a suitable decomposition to verify the positiveness of given polynomials. The complexity of the SOS-based numerical method is polynomial of the problem size. This approach also leads to more accurate solutions to LPV systems than most existing relaxation methods. Several examples have been used to demonstrate benefits of the SOS-based solution approach

Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis

Earthquake early warning (EEW) systems are currently operating nationwide in Japan and are in beta-testing in California. Such a system detects an earthquake initiation using online signals from a seismic sensor network and broadcasts a warning of the predicted location and magnitude a few seconds to a minute or so before an earthquake hits a site. Such a system can be used synergistically with installed structural health monitoring (SHM) systems to enhance pre-event prognosis and post-event diagnosis of structural health. For pre-event prognosis, the EEW system information can be used to make probabilistic predictions of the anticipated damage to a structure using seismic loss estimation methodologies from performance-based earthquake engineering. These predictions can support decision-making regarding the activation of appropriate mitigation systems, such as stopping traffic from entering a bridge that has a predicted high probability of damage. Since the time between warning and arrival of the strong shaking is very short, probabilistic predictions must be rapidly calculated and the decision making automated for the mitigation actions. For post-event diagnosis, the SHM sensor data can be used in Bayesian updating of the probabilistic damage predictions with the EEW predictions as a prior. Appropriate Bayesian methods for SHM have been published. In this paper, we use pre-trained surrogate models (or emulators) based on machine learning methods to make fast damage and loss predictions that are then used in a cost-benefit decision framework for activation of a mitigation measure. A simple illustrative example of an infrastructure application is presented

Fatalism and Savings

We examine the impact of fatalism, the belief that one has little or no control over future events, on the decision of whether or not to save. We develop a model that predicts that fatalism decreases savings for moderately risk averse individuals, increases savings for highly risk averse individuals, and otherwise has no impact. Furthermore, fatalism decreases effort in learning about savings and investment options. We use data from National Longitudinal Survey of Youth (NLSY) and find general support for the theoretical predictions of the model. The results are robust to the inclusion of a number of additional control variables.fatalism, savings, risk aversion

Are Pregnant Women Happier? Racial Differences in the Relationsip Between Pregnancy and Life Satisfaction

This paper uses data from the Behavioral Risk Factor Surveillance System (BRFSS) to study the relationship between pregnancy and life satisfaction for women of childbearing age. The results show strong differences by race. Pregnancy has the strongest positive correlation with happiness for Whites, a smaller but still statistically significant positive correlation for Hispanics, and no relationship for Blacks. The results cannot be explained by differences in other demographics such age, income, education, or marital status. Within each racial group, the results hold across different categories for all these characteristics. There is evidence that racial differences in the effects of pregnancy on emotional and social support from others can partly explain this result.pregnancy, life satisfaction, racial differences

VCU CCTR Mobile App: Android & iOS

In today’s day and age, data should be accessible at all times. The biggest break-through for data accessibility is mobile technologies such as phones and tablets. The CCTR provides a continuum of informatics research and services to support translational and clinical research. Clinical Trials represent one of the central themes for the Center for Clinical and Translational Research, but they do not have a mobile app for the VCU community to access CCTR’s informatics resources. This project aims to promote the expanded informatics research and services available to VCU students, faculty and staff as well as patients interested in discovering more about clinical research at VCU. The CCTR wants to extend current research data management systems and traditional webpages to mobile technologies. This will enable the CCTR to provide the CCTR user community with seamless access to its current and developing infomratics resources. The project followed the agile development methodology. Each week, we created new features for the mobile app, slowly adding onto the initial app we created. The major goal of the project was to be able to pull data from the Forte API. Extra features were added on later on for the overall user-friendliness. The app primarily focused on function over form. In the end, we tried to stick with VCU colors. Over the course of the project, we encountered a few issues along the way. None of us have had experience programming for the android OS. We were familiar with java, but the android library had many more requirements to get everything working. We needed to learn to program for the android OS and also learn new technology associated with mobile app programming. Another issue we came across was scalability, getting the app to comply with VCU branding seemed simple at first, but when we started adding in logos, we encountered a lot of errors. The logos had to be refactored to fit 100% with the application. The CCTR now has a fully functional Clinical Trials Android Application. Over the course of the final semester, additional features will be prioritized based on complexity, and importance to the CCTR and included in the mobile apps. Features that impact the access of information and benefits the CCTR will be added as the project progresses. The final goal will be to create both an Android and an iOS app. Before the apps can be officially finished, a live instance of data will be needed that utilizes VCU resources for accessing data about VCU’s clinical trials.https://scholarscompass.vcu.edu/capstone/1020/thumbnail.jp

Design of high thermal conductivity polymers based on a Bayesian machine learning approach

Open House, ISM in National Center of Sciences Building, 2019.6.05統計数理研究所オープンハウス（学術総合センター）、R1.6.5ポスター発

Well-being across America

This paper uses new Behavioral Risk Factor Surveillance System data to provide the first estimates of well-being across the states of America. From this sample of 1.3 million US citizens, we analyze measures of life satisfaction and mental health. Adjusting for people's characteristics, states such as Louisiana and DC have high psychological well-being levels while California and West Virginia have low well-being; there is no correlation between states' well-being and their GDP per capita. Correcting for people's incomes, satisfaction with life is lowest in the rich states. We discuss implications for the arbitrage theory that regions provide equal utility and compensating differentials.compensating differentials, BRFSS, happiness, geography, GHQ, Easterlin Paradox, mental health, depression, life course