Inversion formula of multifractal energy dissipation in 3D fully developed turbulence

The concept of inverse statistics in turbulence has attracted much attention in the recent years. It is argued that the scaling exponents of the direct structure functions and the inverse structure functions satisfy an inversion formula. This proposition has already been verified by numerical data using the shell model. However, no direct evidence was reported for experimental three dimensional turbulence. We propose to test the inversion formula using experimental data of three dimensional fully developed turbulence by considering the energy dissipation rates in stead of the usual efforts on the structure functions. The moments of the exit distances are shown to exhibit nice multifractality. The inversion formula between the direct and inverse exponents is then verified.Comment: 3 RevTex pages including 3 eps figure

A modified Brown algorithm for solving singular nonlinear systems with rank defects

AbstractA modified Brown algorithm for solving a class of singular nonlinear systems, F(x)=0, where x,F∈Rn, is presented. This method is constructed by combining the discreted Brown algorithm with the space transforming method. The second-order information of F(x) at a point is not required calculating, which is different from the tensor method and the Hoy's method. The Q-quadratic convergence of this algorithm and some numerical examples are given as well

Port sustainable services innovation: Ningbo port users’ expectation

Port sustainable services innovation depends on understanding and matching customers’ expectations with unique service offerings using advanced facilities. This study develops and validates a port sustainable services decision model to investigate customers’ port services expectations based on the views of companies and freight services providers located in major cities nearby Ningbo port. Thus, the study will enable better understanding of the port’s impact on sustainability of the economic and social prosperity of its immediate environment stakeholders by balancing three major aspects such as services, costs and facilities. The study employed a multi-criteria decision-making methodology Analytical Hierarchy Process (AHP) to analyze the model. The results show that while different port users have different views and expectations, port infrastructure improvement, cargo safety, and reductions in port charges are critical to attract businesses. Port-dependent companies that were investigated also cite reduced paper work and optimized e-business as well as reduced transport congestion as key areas for improvements. This study provides port administrators with insights on how to effectively improve business attractiveness for greater sustainability and competitive advantages with rival ports within the same geographical proximity. In addition it also suggests how to design cost-effective ways of meeting and even surpassing users’ expectations

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022

In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition

Long-term temporal dependence of droplets transiting through a fixed spatial point in gas-liquid twophase turbulent jets

We perform rescaled range analysis upon the signals measured by Dual Particle Dynamical Analyzer in gas-liquid two-phase turbulent jets. A novel rescaled range analysis is proposed to investigate these unevenly sampled signals. The Hurst exponents of velocity and other passive scalars in the bulk of spray are obtained to be 0.59$\pm$0.02 and the fractal dimension is hence 1.41$\pm$ 0.02, which are in remarkable agreement with and much more precise than previous results. These scaling exponents are found to be independent of the configuration and dimensions of the nozzle and the fluid flows. Therefore, such type of systems form a universality class with invariant scaling properties.Comment: 16 Elsart pages including 8 eps figure

ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures

Experimental study and random forest prediction model of microbiome cell surface hydrophobicity

[Abstract] The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale.Ministerio de Economía y Competitividad; FJCI-2015- 26071Ministerio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049National Natural Science Foundation of China; Grant No. 31172234National Natural Science Foundation of China; Grant No. 31260556Chinese Academy of Science; Grant No. XDA0502070