Abstract-Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from "sentinel" medical practices. Typically there is a 1-2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to "sentinel" physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity.

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In this paper we considered the existing stable adaptive fault-tolerant control system design from references [3,4] for achieving acceptable flight performance in the presence of control effector failures. This controller is developed for the overactuated case, i.e. the case when the number of control effectors exceeds the number of controlled outputs. We have shown that, in some cases, single or multiple effector failures introduce disturbances that are too large to be effectively handled by a single adaptive controller. To address this problem, in this paper we developed an adaptive fault-tolerant flight control system based on the concept of multiple models, switching and tuning from reference [11]. We have shown that such a controller yields a stable overall system, is sufficiently robust in the presence of severe control effector failures, and achieves excellent overall performance as illustrated through simulations of F/A-18A carrier landing maneuver.

Currently, advanced control systems implemented on production ground vehicles have the goal of promoting maneuverability and stability. With proper coordination of steering and braking action, these goals may be achieved even when road conditions are severe. This paper considers the effect of steering and wheel torques on the dynamics of vehicular systems. Through the input-output linearization technique, the advantages of four-wheel steering (4WS) system and independent torques control are clear from a mathematical point of view. A sliding mode controller is also designed to modify driver’s steering and braking commands to enhance maneuverability and safety. Simulation results show the maneuverability and safety are improved. Although the controller design is based on a four-wheel steering vehicle, the algorithm can also be applied to vehicles of different configurations with slight changes.

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