Using refined kano model and decision trees to discover learners’ needs for teaching videos

Multimedia Tools and Applications - With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital...     

Resolution extension and exit wave reconstruction in complex HREM

Direct methods in real and reciprocal space are developed for structural reversion. The direct method in real space involves the use of a novel method to retrieve the phase in the image plane using transport of intensity equation/maximum entropy method (TIE/MEM) and exit wave reconstruction by self-consistent propagation. Since the exit wave is restored from the complex signal in the image planes, no image model between the exit wave and image is assumed. The structural information in the reconstructed exit wave is then further extended by a "complex" maximum entropy method as a direct method in reciprocal space to extrapolate the phase to higher frequencies.     

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In this study, fully developed laminar flow and convective heat transfer in an internally finned tube heat exchanger are investigated numerically. The flow is assumed to be both hydrodynamically and thermally developed with uniform outside wall temperature. Parameters of the thickness, length, and number of fins and thermal conductivity ratio between fin and working fluid are varied to obtain the friction factor as well as Nusselt number. The results show that the heat transfer improves significantly if more fins are used; however, the pressure drop turns out to be large in this heat exchanger. In addition, it is found that the emergence of closed-loop isotherms between the areas of two neighboring fins leads to heat transfer enhancement in the internally finned tube. When the fin number is smaller than 14, there appears a maximum Nusselt number at about 0.8 of the dimensionless fin length. Finally, an experiment is conducted to verify the numerical results.     

Harris Hawk Optimization Algorithm Based on Cauchy Distribution Inverse Cumulative Function and Tangent Flight Operator

Harris Hawk Optimization (HHO) algorithm is a new population-based and nature-inspired optimization paradigm, which has strong global search ability, but its diversified local search strategies easily make it fall into local optimum. In order to enhance its search mechanism and speed of convergence, an new improved HHO algorithm based on the inverse cumulative function operator of Cauchy distribution and tangent flight operator was proposed. The proposed two operators are used as scale factors to control the step size. The walk path of Cauchy inverse cumulative integral function shows that its trajectory step length is relative to the average, which can further enhance the search stability of the algorithm. The Tangent flight has the function of balanced exploitation and exploration, and enhances the convergence ability of the algorithm. In order to verify the performance of the proposed algorithm, the 30 benchmark functions of the 2017 Institute of Electrical and Electronic Engineers (IEEE) Conference on Evolutionary Computation (CEC2017) and two practical engineering design problems are adopted to carry out the simulation experiments. On the other hand, the covariance matrix adaptation evolutionary strategies (CMA-ES), arithmetic optimization algorithm (AOA), butterfly optimization algorithm (BOA), bat algorithm (BA), whale optimization algorithm (WOA), sine cosine algorithm (SCA), and the proposed HHO algorithms were used for comparison experiments. Simulation results show that the proposed the Cauchy-distribution and Tangent-Flight Harris Hawk Optimization (CTHHO) Algorithm has strong optimization capability.