In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models ...have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier–Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes.
Compared to efficient green and near‐infrared light‐emitting diodes (LEDs), less progress has been made on deep‐blue perovskite LEDs. They suffer from inefficient domain various number of PbX6− ...layers (n) control, resulting in a series of unfavorable issues such as unstable color, multipeak profile, and poor fluorescence yield. Here, a strategy involving a delicate spacer modulation for quasi‐2D perovskite films via an introduction of aromatic polyamine molecules into the perovskite precursor is reported. With low‐dimensional component engineering, the n1 domain, which shows nonradiative recombination and retarded exciton transfer, is significantly suppressed. Also, the n3 domain, which represents the population of emission species, is remarkably increased. The optimized quasi‐2D perovskite film presents blue emission from the n3 domain (peak at 465 nm) with a photoluminescence quantum yield (PLQY) as high as 77%. It enables the corresponding perovskite LEDs to deliver stable deep‐blue emission (CIE (0.145, 0.05)) with an external quantum efficiency (EQE) of 2.6%. The findings in this work provide further understanding on the structural and emission properties of quasi‐2D perovskites, which pave a new route to design deep‐blue‐emissive perovskite materials.
A quasi‐two‐dimensional perovskite film with stable domain distribution is prepared based on a new spacer. The film containing pure bromide perovskite exhibits enhanced deep‐blue fluorescence with quantum yield of 77% by low‐dimensional component engineering. As a result, the corresponding light‐emitting diodes deliver stable deep‐blue emission with a peak external quantum efficiency of 2.6%.
In order to improve the accuracy and reliability of EEG emotion recognition and avoid the problems of poor decomposition effect and long time consumption caused by manual parameter selection, this ...paper constructs an EEG emotion recognition model based on optimized variational modal decomposition. Aiming at the modal aliasing problem existing in traditional decomposition methods, the KH algorithm is used to search for the optimal penalty factor and the number of decomposition layers of the VMD, and KH-VMD decomposition is performed on the EEG signals in the DEAP dataset. The time-domain, frequency-domain, and nonlinear features of IMFs under different time windows are extracted, respectively, and the Catboost classifier completes the construction of the EEG emotion recognition model and emotion classification. Considering the two conditions of the complexity of the network structure of the KH-VMD model and the average classification accuracy of different brain regions in different music environments, the WEE features of the target EEG can constitute the optimal classification network by taking the WEE features of the target EEG as the input of the KH-VMD classification model. At this time, the average classification accuracy that can be obtained with differentiated brain regions and differentiated music environments is 0.8314 and 0.8204. After 8 weeks of music therapy, the experimental group’s low anxiety scores of pleasure and arousal on the Negative Picture SAM scale were 3.11 and 3.2, which were significantly lower than those of the control group’s low-anxiety subjects. The experimental group with high anxiety had anxiety scores and sleep quality scores that were 5.23 and 3.01 points lower than before the intervention. Therefore, music therapy can effectively alleviate psychological anxiety and enhance sleep quality.
Field inversion is often encountered in data-driven computational modeling to infer latent spatial–varying parameters from available observations. The ensemble Kalman method is emerging as a useful ...tool for solving field inversion problems due to its derivative-free merits. However, the method is computationally prohibitive for large-scale field inversion with high-dimensional observation data, which necessitates developing a practical efficient implementation strategy. In this work, we propose a parallel implementation of the ensemble Kalman method with total variation regularization for large-scale field inversion problems. It is achieved by partitioning the computational domain into non-overlapping subdomains and performing local ensemble Kalman updates at each subdomain parallelly. In doing so, the computational complexity of the ensemble-based inversion method is significantly reduced to the level of local subdomains. Further, the total variation regularization is employed to smoothen the physical field over the entire domain, which can reduce the inference discrepancy caused by missing covariances near subdomain interfaces. The capability of the proposed method is demonstrated in three field inversion problems with increasing complexity, i.e., the diffusion problem, the scalar transport problem and the Reynolds averaged Navier-Stokes closure problem. The numerical results show that the proposed method can significantly improve computational efficiency with satisfactory inference accuracy.
•The analysis scheme of the ensemble Kalman method is parallelized based on non-overlapping domain decomposition.•The total variation regularization is utilized to alleviate the discontinuity near subdomain interfaces.•The method enables field inversion with large data amounts by partitioning observation data regionally.•The approach reduces computational costs significantly with satisfactory inversion accuracy and ease of implementation.
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, ...retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.
Nanoparticle synthesis using microorganisms and plants by green synthesis technology is biologically safe, cost-effective, and environment-friendly. Plants and microorganisms have established the ...power to devour and accumulate inorganic metal ions from their neighboring niche. The biological entities are known to synthesize nanoparticles both extra and intracellularly. The capability of a living system to utilize its intrinsic organic chemistry processes in remodeling inorganic metal ions into nanoparticles has opened up an undiscovered area of biochemical analysis. Nanotechnology in conjunction with biology gives rise to an advanced area of nanobiotechnology that involves living entities of both prokaryotic and eukaryotic origin, such as algae, cyanobacteria, actinomycetes, bacteria, viruses, yeasts, fungi, and plants. Every biological system varies in its capabilities to supply metallic nanoparticles. However, not all biological organisms can produce nanoparticles due to their enzymatic activities and intrinsic metabolic processes. Therefore, biological entities or their extracts are used for the green synthesis of metallic nanoparticles through bio-reduction of metallic particles leading to the synthesis of nanoparticles. These biosynthesized metallic nanoparticles have a range of unlimited pharmaceutical applications including delivery of drugs or genes, detection of pathogens or proteins, and tissue engineering. The effective delivery of drugs and tissue engineering through the use of nanotechnology exhibited vital contributions in translational research related to the pharmaceutical products and their applications. Collectively, this review covers the green synthesis of nanoparticles by using various biological systems as well as their applications.
High ion selectivity and permeability, as two contradictory aspects for the membrane design, highly hamper the development of osmotic energy harvesting technologies. Metal–organic frameworks (MOFs) ...with ultra‐small and high‐density pores and functional surface groups show great promise in tackling these problems. Here, we propose a facile and mild cathodic deposition method to directly prepare crack‐free porphyrin MOF membranes on a porous anodic aluminum oxide for osmotic energy harvesting. The abundant carboxyl groups of the functionalized porphyrin ligands together with the nanoporous structure endows the MOF membrane with high cation selectivity and ion permeability, thus a large output power density of 6.26 W m−2 is achieved. The photoactive porphyrin ligands further lead to an improvement of the power density to 7.74 W m−2 upon light irradiation. This work provides a promising strategy for the design of high‐performance osmotic energy harvesting systems.
A porphyrin metal–organic framework membrane has been fabricated by a facile cathodic deposition method. The high ion selectivity and permeability endow the MOF membrane with a great performance in osmotic energy harvesting, and this performance can be further improved by the photoactive porphyrin ligands upon light irradiation.
Anthropogenic environments have been implicated in enrichment and exchange of antibiotic resistance genes and bacteria. Here we study the impact of confined and controlled swine farm environments on ...temporal changes in the gut microbiome and resistome of veterinary students with occupational exposure for 3 months. By analyzing 16S rRNA and whole metagenome shotgun sequencing data in tandem with culture-based methods, we show that farm exposure shapes the gut microbiome of students, resulting in enrichment of potentially pathogenic taxa and antimicrobial resistance genes. Comparison of students' gut microbiomes and resistomes to farm workers' and environmental samples revealed extensive sharing of resistance genes and bacteria following exposure and after three months of their visit. Notably, antibiotic resistance genes were found in similar genetic contexts in student samples and farm environmental samples. Dynamic Bayesian network modeling predicted that the observed changes partially reverse over a 4-6 month period. Our results indicate that acute changes in a human's living environment can persistently shape their gut microbiota and antibiotic resistome.
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catalyzed intramolecular cyclization reaction of 2-alkyl-1,4-benzoquinone derived from D–A cyclopropane was discovered. This reaction involves single-electron transfer, proton-transfer, ...an aromatization driven spin center shift, and radical coupling processes, and offers an efficient method for the synthesis of 6-chromanols from D–A cyclopropanes.
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