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.
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.
•Ochrobactrum anthropic LJ81 could remove sole and mixed nitrogen source.•The removal rate of ammonia could be promoted by adding nitrite and nitrate.•No nitrite was accumulated during the SND ...process.•The NirK and NirS genes were co-existed in strain LJ81.
Nitrogen contaminants are widespread presence in municipal wastewater, heterotrophic nitrification and aerobic denitrification (HN-AD) bacteria have advantages of dealing with multiple nitrogen. Strain LJ81 was isolated from domestic sludge, identified as Ochrobactrum anthropic, which was oxygen-dependent and could survive in a wide range of pH values. Results showed that strain LJ81 could achieve simultaneous nitrification and denitrification (SND) under aerobic condition, whilst more than 80% of initial nitrogen was converted into gaseous nitrogen. The removal rates of ammonia increased from 3.75 to 3.85 and 5.70 mg-N L−1 h−1 by adding nitrite and nitrate, respectively, while the nitrate denitrification was the rate-limiting step of SND process. Moreover, adding chlorate could inhibit not only the cell growth slightly but also denitrification of nitrate. All results indicated that O. anthropic strain LJ81 exhibited excellent performance on nitrogen removal without nitrite accumulation under aerobic condition.
Earlier studies have reaffirmed the pivotal role played by the private sectors in China's development, so the growth of private enterprises deserves more attention. This paper carries out a series of ...empirical studies based on the motives for holding cash. It is found that private enterprises were forced to maintain a high level of cash holdings to deal with financing constraints, while SOEs accumulate cash proactively for larger financial flexibility. In such context, credit discrimination remains an urgent problem pending Chinese financial system reform solutions.
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.
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.
A Sc(OTf) 3 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.
Two new azaphilone compounds, daldinins G (1) and H (2), together with nine known compounds daldinin D (3), sargassopenilline B (4), austalide V (5), austalide K (6), austalide P (7), austalide P ...acid (8), austalide H (9), 13‐O‐deacetyaustalide I (10), and 17‐O‐demethylaustalide B (11), were isolated from the soft coral‐derived fungus Penicillium glabrum glmu003. The new structures of 1 and 2 were elucidated on the basis of 1D and 2D NMR, mass spectra, and electronic circular dichroism (ECD) data analysis. Compound 5 showed weak inhibitory activity against pancreatic lipase (PL) with IC50 value of 23.9 μg/mL.
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