Linear attention coupled Fourier neural operator for simulation of three-dimensional turbulence

Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly-nonlinear with high degrees of freedom and the corresponding simulation is memory-intensive. Recently, the attention mechanism has been shown as a promising approach to boost the performance of neural networks on turbulence simulation. However, the standard self-attention mechanism uses $O(n^2)$ time and space with respect to input dimension $n$, and such quadratic complexity has become the main bottleneck for attention to be applied on 3D turbulence simulation. In this work, we resolve this issue with the concept of linear attention network. The linear attention approximates the standard attention by adding two linear projections, reducing the overall self-attention complexity from $O(n^2)$ to $O(n)$ in both time and space. The linear attention coupled Fourier neural operator (LAFNO) is developed for the simulation of 3D turbulence. Numerical simulations show that the linear attention mechanism provides 40\% error reduction at the same level of computational cost, and LAFNO can accurately reconstruct a variety of statistics and instantaneous spatial structures of 3D turbulence. The linear attention method would be helpful for the improvement of neural network models of 3D nonlinear problems involving high-dimensional data in other scientific domains.Comment: 28 pages, 14 figure

Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator

Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net enhanced Fourier neural operator (IU-FNO) for stable and efficient predictions on the long-term large-scale dynamics of turbulence. The IU-FNO model employs implicit recurrent Fourier layers for deeper network extension and incorporates the U-net network for the accurate prediction on small-scale flow structures. The model is systematically tested in large-eddy simulations of three types of 3D turbulence, including forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The numerical simulations demonstrate that the IU-FNO model is more accurate than other FNO-based models including vanilla FNO, implicit FNO (IFNO) and U-Net enhanced FNO (U-FNO), and dynamic Smagorinsky model (DSM) in predicting a variety of statistics including the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of flow field. Moreover, IU-FNO improves long-term stable predictions, which has not been achieved by the previous versions of FNO. Besides, the proposed model is much faster than traditional LES with DSM model, and can be well generalized to the situations of higher Taylor-Reynolds numbers and unseen flow regime of decaying turbulence.Comment: 45 pages, 21 figure

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible

Experimental Realization of Acoustic Chern Insulator

Topological insulators are new states of matter in which the topological phase originates from symmetry breaking. Recently, time-reversal invariant topological insulators were demonstrated for classical wave systems, such as acoustic systems, but limited by inter-pseudo-spin or inter-valley backscattering. This challenge can be effectively overcome via breaking the time-reversal symmetry. Here, we report the first experimental realization of acoustic topological insulators with nonzero Chern numbers, viz., acoustic Chern insulator (ACI), by introducing an angular-momentum-biased resonator array with broken Lorentz reciprocity. High Q-factor resonance is leveraged to reduce the required speed of rotation. Experimental results show that the ACI featured with a stable and uniform metafluid flow bias supports one-way nonreciprocal transport of sound at the boundaries, which is topologically immune to the defect-induced scatterings. Our work opens up opportunities for exploring unique observable topological phases and developing practical nonreciprocal devices in acoustics.Comment: 16 pages, 4 figure

Relative Quantification of Protein-Protein Interactions Using a Dual Luciferase Reporter Pull-Down Assay System

The identification and quantitative analysis of protein-protein interactions are essential to the functional characterization of proteins in the post-proteomics era. The methods currently available are generally time-consuming, technically complicated, insensitive and/or semi-quantitative. The lack of simple, sensitive approaches to precisely quantify protein-protein interactions still prevents our understanding of the functions of many proteins. Here, we develop a novel dual luciferase reporter pull-down assay by combining a biotinylated Firefly luciferase pull-down assay with a dual luciferase reporter assay. The biotinylated Firefly luciferase-tagged protein enables rapid and efficient isolation of a putative Renilla luciferase-tagged binding protein from a relatively small amount of sample. Both of these proteins can be quantitatively detected using the dual luciferase reporter assay system. Protein-protein interactions, including Fos-Jun located in the nucleus; MAVS-TRAF3 in cytoplasm; inducible IRF3 dimerization; viral protein-regulated interactions, such as MAVS-MAVS and MAVS-TRAF3; IRF3 dimerization; and protein interaction domain mapping, are studied using this novel assay system. Herein, we demonstrate that this dual luciferase reporter pull-down assay enables the quantification of the relative amounts of interacting proteins that bind to streptavidin-coupled beads for protein purification. This study provides a simple, rapid, sensitive, and efficient approach to identify and quantify relative protein-protein interactions. Importantly, the dual luciferase reporter pull-down method will facilitate the functional determination of proteins

Experimental Observation of Efficient Nonreciprocal Mode Transitions via Spatiotemporally-Modulated Acoustic Metamaterials

In lossless acoustic systems, mode transitions are always time-reversible, consistent with Lorentz reciprocity, giving rise to symmetric sound manipulation in space-time. To overcome this fundamental limitation and break space-time symmetry, nonreciprocal sound steering is realized by designing and experimentally implementing spatiotemporally-modulated acoustic metamaterials. Relying on no slow mechanical parts, unstable and noisy airflow or complicated piezoelectric array, our mechanism uses the coupling between an ultrathin membrane and external electromagnetic field to realize programmable, dynamic control of acoustic impedance in a motionless and noiseless manner. The fast and flexible impedance modulation at the deeply subwavelength scale enabled by our compact metamaterials provides an effective unidirectional momentum in space-time to realize irreversible transition in k-{\omega} space between different diffraction modes. The nonreciprocal wave-steering functionality of the proposed metamaterial is elucidated by theoretically deriving the time-varying acoustic response and demonstrated both numerically and experimentally via two distinctive examples of unidirectional evanescent wave conversion and nonreciprocal blue-shift focusing. This work can be further extended into the paradigm of Bloch waves and impact other vibrant domains, such as non-Hermitian topological acoustics and parity-time-symmetric acoustics.Comment: 15 pages, 4 figure

Enhanced response of soil respiration to experimental warming upon thermokarst formation

As global temperatures continue to rise, a key uncertainty of terrestrial carbon (C)–climate feedback is the rate of C loss upon abrupt permafrost thaw. This type of thawing—termed thermokarst—may in turn accelerate or dampen the response of microbial degradation of soil organic matter and carbon dioxide (CO2) release to climate warming. However, such impacts have not yet been explored in experimental studies. Here, by experimentally warming three thermo-erosion gullies in an upland thermokarst site combined with incubating soils from five additional thermokarst-impacted sites on the Tibetan Plateau, we investigate how warming responses of soil CO2 release would change upon upland thermokarst formation. Our results show that warming-induced increase in soil CO2 release is ~5.5 times higher in thermokarst features than the adjacent non-thermokarst landforms. This larger warming response is associated with the lower substrate quality and higher abundance of microbial functional genes for recalcitrant C degradation in thermokarst-affected soils. Taken together, our study provides experimental evidence that warming-associated soil CO2 loss becomes stronger upon abrupt permafrost thaw, which could exacerbate the positive soil C–climate feedback in permafrost-affected regions

Radiative Forcing From the 2014–2022 Volcanic and Wildfire Injections

Volcanic and wildfire events between 2014 and 2022 injected ∼3.2 Tg of sulfur dioxide and 0.8 Tg of smoke aerosols into the stratosphere. With injections at higher altitudes and lower latitudes, the simulated stratospheric lifetime of the 2014–2022 injections is about 50% longer than the volcanic 2005–2013 injections. The simulated global mean effective radiative forcing (ERF) of 2014–2022 is −0.18 W m−2, ∼40% of the ERF of the period of 1991–1999 with a large-magnitude volcanic eruption (Pinatubo). Our climate model suggests that the stratospheric smoke aerosols generate ∼60% more negative ERF than volcanic sulfate per unit aerosol optical depth. Studies that fail to account for the different radiative properties of wildfire smoke relative to volcanic sulfate will likely underestimate the negative stratospheric forcings. Our analysis suggests that stratospheric injections offset 20% of the increase in global mean surface temperature between 2014–2022 and 1999–2002

Applications of Nanomaterials in Electrochemical Enzyme Biosensors

A biosensor is defined as a kind of analytical device incorporating a biological material, a biologically derived material or a biomimic intimately associated with or integrated within a physicochemical transducer or transducing microsystem. Electrochemical biosensors incorporating enzymes with nanomaterials, which combine the recognition and catalytic properties of enzymes with the electronic properties of various nanomaterials, are new materials with synergistic properties originating from the components of the hybrid composites. Therefore, these systems have excellent prospects for interfacing biological recognition events through electronic signal transduction so as to design a new generation of bioelectronic devices with high sensitivity and stability. In this review, we describe approaches that involve nanomaterials in direct electrochemistry of redox proteins, especially our work on biosensor design immobilizing glucose oxidase (GOD), horseradish peroxidase (HRP), cytochrome P450 (CYP2B6), hemoglobin (Hb), glutamate dehydrogenase (GDH) and lactate dehydrogenase (LDH). The topics of the present review are the different functions of nanomaterials based on modification of electrode materials, as well as applications of electrochemical enzyme biosensors

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology