2,461 research outputs found

    Induced-charge electroosmosis around conducting and Janus cylinder in microchip

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    The induced-charge elecetroosmosis around conducting/Janus cylinder with arbitrary Debye thickness is studied numerically, when an direct current weak electric filed is suddenly applied in a confined microchannel. It’s found that there are four large circulations around the conducting cylinder, and the total flux in the microchannel is zero; there are two smaller circulations around the Janus cylinder, and they are compressed to wall. A bulk flux, which has a parabolic relation with the applied electric field, is also predicted

    A lightweight privacy-preserving CNN feature extraction framework for mobile sensing

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    Interference of Two-Dimensional Bose-Einstein Condensates in Micro-Gravity

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    We investigate the interference of two-dimensional Bose-Einstein condensates in micro-gravity, which influenced by the interaction strength, initial momentum, gravitational potential and phase difference. We demonstrate that the gravitational potential from the Earth can change the density distribution and phase distribution of the condensate's wave function. As time evolves, a portion of the gravitational potential energy of the microscopic particles can be converted into kinetic energy, which changes the motion of the microscopic particles, and leads to the varying of the density and phase distribution of the wave function. Nevertheless, the influences of the Earth's gravity on the wave function can be eliminated by the micro-gravity environment, which confirmed by many micro-gravity cold atom experiments. Our results present the influences of gravity and other parameters on interference of Bose-Einstein condensates, which help us to reveal the intrinsic natures of the related theoretical predictions and experimental phenomena. Furthermore, our work builds a bridge between the related physical phenomena and our physical intuition about the Bose-Einstein condensates in micro-gravity environment

    A Social Referral Mechanism for Job Reference Recommendation

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    Recently, with the popularity of various social media, this new trend of information technologies has impacted our lives, redefined the way we interact with each other, and facilitated the communication and influence cross different social groups, such as enhancing the power of social search and appraisal. _x000D_ In this research, we mainly focus on this mystery process of information exchanges existing long ago on the base of sociology and apply this power in the field of job seeking. Considering the factors of both willingness and influence, we generate the list of proper reference candidates to desired job for job seekers to provide more job-related information or to be referrals. Integrating the knowledge of human resources management, we implement this social referral application with the support of information technologies and strive to enrich the service of social media, turning the passively searching for job seeking to actively consulting for exclusively job information._x000D

    Diffractive Efficiency Prediction of Surface Relief Grating Waveguide Using Artificial Neural Network

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    This study aims to develop lightweight and comfortable wearable devices using surface-relief grating, which can be designed to meet different diffraction conditions. However, extensive calculations must be performed to obtain the impact of the variation in the structural dimensions. The finite element method is used to solve the diffractive efficiency and then replaced by trained artificial neural networks with a single hidden layer containing 25 neurons. By using raw data with geometric parameters as the features, the performance of the network is investigated with different numbers of raw data; in addition, the regression analysis shows a high R-value of approximately 0.999. The predicted results are compared with those calculated from the simulation. The diffraction efficiency tendencies vary with the different geometric parameters, which show a high level of agreement between the predicted and calculated data; this confirms that the proposed method supports and reduces the burden of extensive calculations
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