The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher p...
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In this paper, we present a derivative-free computational method for solving fuzzy nonlinear equation. Derivative-free technique avoids computing the derivative by generating an estimate to the derivative. This is mad...
In this paper, we present a derivative-free computational method for solving fuzzy nonlinear equation. Derivative-free technique avoids computing the derivative by generating an estimate to the derivative. This is made possible by inserting the estimate of in Levenberg-Marquardt's method. Numerical experiments are carried out which shows that, the method is efficient.
The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly impr...
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The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort.
The Icefin vehicle represents a custom robotic platform design specifically targeted to the unique challenges of sub-ice deployments in Antarctica. A previous iteration of the vehicle was deployed to McMurdo, Antarcti...
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In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS model...
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In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local stencil geometry with Galilean invariance. The input features include the first-order and second-order derivatives of filtered velocity and temperature, filtered density, and its first-order derivative. It is shown that the proposed ANN-F7 model shows an advantage over the gradient model in the a priori test. Specifically, the ANN-F7 model gives larger correlation coefficients and smaller relative errors than the gradient model. In an a posteriori analysis, the ANN-F7 model performs better than the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the statistical properties of flow fields at the Taylor microscale Reynolds number Reλ ranging from 180 to 250. The DSM and DMM models lead to the typical tilted spectral distribution of velocity, where low wave numbers are too energy rich, while those near the cutoff are damped too strongly. In contrast, it is shown that the velocity spectrum predicted by the ANN-F7 model almost overlaps with the filtered direct numerical simulation data. Besides, the ANN-F7 model reconstructs the probability density functions of SGS force and divergence of SGS heat flux much better than the DSM and DMM models. An artificial neural network with reasonable physical input features can deepen our understanding of turbulence modeling.
In this paper strategies to control the speed of a generic DC motor system are discussed. They range from the conventional, like PID and state-feedback, to reference model adaptive algorithms such as MRAC, VS-MRAC and...
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Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia ...
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We study the finite-temperature behavior of the Sp(4) Yang-Mills lattice theory in four dimensions, by applying the logarithmic linear relaxation algorithm. We demonstrate the presence of coexisting (metastable) phase...
We study the finite-temperature behavior of the Sp(4) Yang-Mills lattice theory in four dimensions, by applying the logarithmic linear relaxation algorithm. We demonstrate the presence of coexisting (metastable) phases, when the system is in the proximity of the transition. We measure observables such as the free energy, the expectation value of the plaquette operator and of the Polyakov loop, as well as the specific heat, and the Binder cumulant. We use these results to obtain a high-precision measurement of the critical coupling at the confinement-deconfinement transition, and assess its systematic uncertainty, for one value of the lattice extent in the time direction. Furthermore, we perform an extensive study of the finite-volume behavior of the lattice system, by repeating the measurements for fixed lattice time extent, while increasing the spatial size of the lattice. We hence characterize the first-order transition on the lattice and present the first results in the literature on this theory for the infinite volume extrapolation of lattice quantities related to latent heat and interface tension. Gauge theories with Sp(4) group have been proposed as new dark sectors to provide a fundamental origin for the current phenomenological evidence of dark matter. A phase transition at high temperature, in such a new dark sector, occurring in the early Universe, might have left a relic stochastic background of gravitational waves. Our results represent a milestone toward establishing whether such a new physics signal is detectable in future experiments, as they enter the calculation of the parameters, α and β, controlling the power spectrum of gravitational waves. We also outline the process needed in the continuum extrapolation of our measurements and test its feasibility on one additional choice of temporal extent of the lattice.
The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obt...
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The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.
Acinetobacter baumannii is a major pathogen of nosocomial meningitis and ventriculitis. Due to very limited antibiotic treatment options, polymyxins are often used as a last-line therapy. To optimise polymyxin use in ...
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