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Policy PointsFaced with urgent threats to human health and well‐being such as climate change, calls among the academic community are getting louder to contribute more effectively to the implementation of the evidence generated by our research into public policy.As interest in knowledge translation (KT) surges, so have a number of anxieties about the field's shortcomings. Our paper is motivated by a call in the literature to render useful advice for those beginning in KT on how to advance impact at a policy level.By integrating knowledge from fields such as political science, moral psychology, and marketing, we suggest that thinking and acting like marketers, lobbyists, movements, and political scientists would help us advance on the quest to bridge the chasm between evidence and policy. [ABSTRACT FROM AUTHOR]

Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change (DLC) maneuver. This task has been employed to demonstrate the efficacy of controllers in an autonomous driving situation. In this sense, the proposed controller shows better performance than a driver model designed in CarSim at a high vehicle longitudinal velocity. The main contribution of this paper is to present an eMPC for discrete-time linear time-variant (LTV) systems so that the proposed controller can be robust against parameter variation. In a state-space representation of the vehicle, the longitudinal velocity of the vehicle is assumed to be a constant so that the whole system is linear time-invariant (LTI). However, it is inevitable that this velocity varies in an actual driving situation. Therefore, an eMPC controller is designed using an add-on unit to consider the varying parameter without modification of the eMPC solution. The CarSim simulation results of eMPC show enhanced performance compared to that of eMPC for the LTI system. [ABSTRACT FROM AUTHOR]

Signs, prompts, and symbols are a common means to change behavior in our society. Understanding the psychological mechanisms by which signage influences behavior is a critical first step to achieve the desired outcome. In the current research, we propose a theoretical model of sign-to-behavior process. The model suggests that when one encounters a sign, it is encoded to construct an action representation (comprehension process), which is then acted on unless its enactment is inhibited (decision process). We test the implications of the model in two studies. In support of our hypothesis, for unfamiliar signs, clarity of purpose predicts perceived effectiveness of a sign; however, for familiar signs, clarity of purpose does not matter. Insights gained from the studies will help to design effective signs. Practical implications of the model are discussed, and future research directions are outlined. [ABSTRACT FROM AUTHOR]