A stochastic averaging method on the strongly nonlinear Duffing-Rayleigh oscillator under Gaussian colored noise excitation

A modified stochastic averaging method on a Duffing-Rayleigh oscillator with strongly nonlinearity subject to Gaussian colored noise excitation was proposed. The so-called He’s energy balance method was applied to obtain the averaged frequency of the conservative system. Subsequently, the stochastic averaging method of strong nonlinearity was used. The modified method can offer more concise approximate expressions of the drift and diffusion coefficients without weakening the accuracy of predicting the responses of the systems too much. The stationary responses of probability density of amplitudes, together with joint probability density of displacement and velocity are studied to verify the presented approach. The reliability of the systems was also investigated to offer further support. Digital simulations were carried out and the output of that are coincide with the theoretical approximations well

Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

The robot fish is affected by many unknown internal and external interference factors when it performs path tracking in unknown waters. It was proposed that a path tracking method based on the EFWA-ADRC (enhanced fireworks algorithmauto disturbance rejection control) to obtain high-quality tracking effect. ADRC has strong adaptability and robustness. It is an effective method to solve the control problems of nonlinearity, uncertainty, strong interference, strong coupling and large time lag. For the optimization of parameters in ADRC, the enhanced fireworks algorithm (EFWA) is used for online adjustment. It is to improve the anti-interference of the robot fish in the path tracking process. The multi-joint bionic robot fish was taken as the research object in the paper. It was established a path tracking error model in the Serret-Frenet coordinate system combining the mathematical model of robotic fish. It was focused on the forward speed and steering speed control rate. It was constructed that the EFWA-ADRC based path tracking system. Finally, the simulation and experimental results show that the control method based on EFWAADRC and conventional ADRC makes the robotic fish track the given path at 2:8s and 3:3s respectively, and the tracking error is kept within plus or minus 0:09m and 0:1m respectively. The new control method tracking steady-state error was reduces by 10% compared with the conventional ADRC. It was proved that the proposed EFWA-ADRC controller has better control effect on the controlled system, which is subject to strong interference

A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation

Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we employ our metric to automatically search for the optimal hyper-parameter set, achieving superior performance compared to manually tuned sets across four common benchmarks. Codes will be available soon

Constraining the Woods-Saxon potential in fusion reactions based on the neural network

The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an important role in the synthesis of superheavy elements. In this work, the neural network, which combines with the calculations of the fusion cross sections via the Hill-Wheeler formula, is developed to optimize the parameters of the Woods-Saxon potential by comparing the experimental values. The correlations between the parameters of Woods-Saxon potential and the reaction partners, which can be quantitatively fitted to a sigmoid-like function with the mass numbers, have been displayed manifestly for the first time. This study could promote the accurate estimation of nucleus-nucleus interaction potential in low energy heavy-ion collisions.Comment: 6 pages, 5 figure

WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation

In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital communication. The web design process is complex and often time-consuming, especially for those with limited expertise. In this paper, we introduce Web Rendering Parameters Generation (WebRPG), a new task that aims at automating the generation for visual presentation of web pages based on their HTML code. WebRPG would contribute to a faster web development workflow. Since there is no existing benchmark available, we develop a new dataset for WebRPG through an automated pipeline. Moreover, we present baseline models, utilizing VAE to manage numerous elements and rendering parameters, along with custom HTML embedding for capturing essential semantic and hierarchical information from HTML. Extensive experiments, including customized quantitative evaluations for this specific task, are conducted to evaluate the quality of the generated results.Comment: Accepted at ECCV 2024. The dataset and code can be accessed at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/WebRP

Quantifying angular distributions in multinucleon transfer reactions with a semi-classical method

The multinucleon transfer (MNT) process in low-energy heavy ion collisions can be utilized to produce unknown nuclei far beyond the stability line. However, the reaction products exhibit broad angular and energy distributions, which could lower the experimental detection efficiency. We present a classical approach that employs a parameterized angular distribution to describe the complex issue. By analyzing limited experimental data on angular distribution, we proposed a three-parameter formula to calculate the angular distribution and identified the dependencies of the parameters. We also discuss the sensitivity of these parameters within this method. A comprehensive comparison with microscopic models and experimental data across a wide range of conditions is conducted. The proposed formula offers an efficient and straightforward way to determine the angular distribution of MNT products.6 pages, 6 figur

Protective Effect of Chinese Cabbage Outer Leaves Soluble Dietary Fiber on Intestinal Barrier Damage in Mice

Objective: The intervention effect of soluble dietary fiber from the outer leaves of Chinese cabbage (CSF) on cyclophosphamide (CPA) induced intestinal barrier damage in mice were studied to provide a theoretical basis for the high-value utilization of CSF resources. Methods: Sixty male ICR mice were randomly divided into five groups: Negative control, model control, and 125, 250, and 500 mg/kg·bw CSF interventions. Structure of the intestinal epithelial tissue, serum and intestinal biochemical indices, tight binding proteins, short chain fatty acids (SCFAs), and intestinal flora were evaluated to determine the protective effect of CSF on intestinal barrier injury in mice. Results: CSF improved the integrity and arrangement of the villi and villus length in the small intestine, as well as the ratio of villus length to crypt depth. The serum levels of lipopolysaccharides (LPS), D-lactate acid (D-LA), and diamine oxidase (DAO) and intestinal permeability were reduced. The expression of zonula occludens-1 (ZO-1), Claudin-1, and Occludin proteins in the small intestinal tissue of model control mice was significantly reversed. The number of goblet cells and intraepithelial lymphocytes in jejunum and ileum were increased, and the expression level of SIgA, β-DF, and LZM were up-regulated. 16S rDNA sequencing was performed to detect the intestinal flora of mice, and it was found that CSF effectively restored the species diversity of the intestinal flora and had a significant regulatory effect on the structure and composition of the intestinal flora. In addition, SCFAs and total SCFAs in the CSF-treated group were significantly or extremely significantly restored. Conclusion: CSF exhibits potential protective effects against CPA-induced intestinal barrier injury in mice through physical, immune, and biological intestinal barriers

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Studies on several problems in nuclear physics by using machine learning

BackgroundMachine learning, which has been widely applied to scientific research in recent years, can be used to investigate the inherent correlations within a large number of complex data.PurposeWe evaluate the performances of two types of machine-learning algorithms for correcting nuclear mass models, reconstructing the impact parameter in heavy-ion collisions, and extracting the symmetry energy slope parameter. We also discuss the extrapolation and generalization ability of the machine-learning models.MethodFor correcting the nuclear mass models, 10 characteristic quantities are fed into the LightGBM to mimic the residual between the experimental and the theoretical binding energies. For impact parameter or symmetry energy, two types of observables constructed based on the particle information simulated by using the UrQMD transport model for setting up the different impact parameters or symmetry energy slope parameters are used as inputs to a conventional neural network and the LightGBM to extract the original information.ResultAnalysis of these nuclear physics problems reveals the potential applicability of machine-learning methods.ConclusionsMachine-learning methods can be used to investigate new physical problems, thereby promoting the development of both theory and experiment