Using molecular dynamics simulations, we study a spherically-symmetric "two-scale" Jagla potential with both repulsive and attractive ramps. This potential displays a liquid-liquid phase transition with a positively sloped coexistence line ending at critical point well above the equilibrium melting line. We study the dynamic behavior in the vicinity of this liquid-liquid critical point. We find that the dynamics in the more ordered high density phase (HDL) are much slower then the dynamics in the less ordered low density phase (LDL). Moreover, the behavior of the diffusion constant and relaxation time in the HDL phase follows approximately an Arrhenius law, while in the LDL phase the slope of the Arrhenius fit increases upon cooling. On the other hand, if we cool the system at constant pressure above the critical pressure behavior of the dynamics smoothly changes with temperature. It resembles the behavior of the LDL at high temperatures and resembles the behavior of the HDL at low temperatures. This dynamic crossover happens in the vicinity of the Widom line (the extension of the coexistence line into the one-phase region) which also has a positive slope. Our work suggests a possible general relation between a liquid-liquid phase transition and the change in dynamics.

Micromachined switchable metamaterial with dual resonance Appl. Phys. Lett. 101, 151902 (2012) Non-resonant terahertz field enhancement in periodically arranged nanoslits J. Appl. Phys. 112, 074318 (2012) Ultra-low power electrically reconfigurable magnetoelectric microwave devices J. Appl. Phys. 112, 073915 (2012) Broadband and ultrathin screen with magnetic substrate for microwave reflectivity reduction Appl. Phys. Lett. 101, 154101 (2012) Tunable, narrow-band, all-metallic microwave absorber

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Abstract. We address the problem of detecting whether an engine is misfiring by using machine learning techniques on transformed audio data collected from a smartphone. We recorded audio samples in an uncontrolled environment and extracted Fourier, Wavelet and Mel-frequency Cepstrum features from normal and abnormal engines. We then implemented Fisher score and Relief score based variable ranking to obtain an informative reduced feature set for training/testing classification algorithms. Using this feature set, we were able to obtain a model accuracy of over 99% using a linear SVM on outsample data. This application of machine learning to vehicle subsystem monitoring simplifies traditional engine diagnostics, aiding vehicle owners in the maintenance process and opening up new avenues for pervasive mobile sensing and automotive diagnostics.

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