Interplay of kinematic and magnetic forcing in a model of a conducting fluid with randomly driven magnetohydrodynamic equations has been studied in space dimensions d > or =2 by means of the renormalization group. A perturbative expansion scheme, parameters of which are the deviation of the spatial dimension from two and the deviation of the exponent of the powerlike correlation function of random forcing from its critical value, has been used in one-loop approximation. Additional divergences have been taken into account that arise at two dimensions and have been inconsistently treated in earlier investigations of the model. It is shown that in spite of the additional divergences, the kinetic fixed point associated with the Kolmogorov scaling regime remains stable for all space dimensions d > or =2 for rapidly enough falling off correlations of the magnetic forcing. A scaling regime driven by thermal fluctuations of the velocity field has been identified and analyzed. The absence of a scaling regime near two dimensions driven by the fluctuations of the magnetic field has been confirmed. A renormalization scheme has been put forward and numerically investigated to interpolate between the epsilon expansion and the double expansion.
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http://dx.doi.org/10.1103/PhysRevE.64.056411 | DOI Listing |
Nat Biotechnol
January 2025
Department of Automation, Tsinghua University, Beijing, China.
Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network.
View Article and Find Full Text PDFNeural Netw
January 2025
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA; Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, WA, United States. Electronic address:
We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of several selected radial basis functions. The use of tensor feedforward networks allows us to efficiently exploit auto-differentiation (in physical variables) in major Python packages while using radial basis functions can fully avoid auto-differentiation, which is rather expensive in high dimensions.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
University of Oregon, Department of Physics and Materials Science Institute, Eugene, Oregon 97403, USA.
We consider many-particle diffusion in one spatial dimension modeled as "random walks in a random environment." A shared short-range space-time random environment determines the jump distributions that drive the motion of the particles. We determine universal power laws for the environment's contribution to the variance of the extreme first passage time and extreme location.
View Article and Find Full Text PDFActa Crystallogr B Struct Sci Cryst Eng Mater
February 2025
CSIRO Division of Mineral Products, Port Melbourne, Victoria, Australia.
The crystallographic phase change from tetragonal litharge (α-PbO; P4/nmm) to orthorhombic massicot (β-PbO; Pbcm) has been studied by full-matrix Rietveld analysis of high-temperature neutron powder diffraction data collected in equal steps from ambient temperature up to 925 K and back down to 350 K. The phase transformation takes place between 850 and 925 K, with the coexisting phases having equal abundance by weight at 885 K. The product massicot remains metastable on cooling to near ambient temperature.
View Article and Find Full Text PDFHeliyon
January 2025
Jade University of Applied Sciences, Institute for Applied Photogrammetry and Geoinformatics, Ofener Str. 16, Oldenburg, 26129, Lower Saxony, Germany.
Though numerous studies acknowledge the critical role played by green spaces (GS) in bolstering sustainability in various dimensions, a majority of these investigations primarily center on the ecological aspect and urban environments. Due to the multifaceted benefits of GSs, different categories and expectations of these spaces can be identified across disciplines. Hence, no single method exists for evaluating the success of GSs in promoting sustainability due to the multifaceted benefits and variety of expectations.
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