Bifurcation analysis of a reaction-diffusion-advection predator-prey system with delay

A diffusive predator-prey system with advection and time delay is considered. Choosing the conversion delay $\tau$ as a bifurcation parameter, we find that as $\tau$ varies, the system will generate Hopf bifurcation. Then, for the reaction diffusion model proposed in this paper, we use an improved center manifold reduction method and normal form theory to derive an algorithm for determining the direction and stability of Hopf bifurcation. Finally, we provide simulations to illustrate the effects of time delay $\tau$ and advection $\alpha$ on system behaviors

Coupler RF kick and emittance optimization of the SHINE injector

Coupler RF kick due to the asymmetric structure caused by the coupler, is more likely to lead to emittance growth in the SHINE injector with low beam energy. The calculation of coupler RF kick and resulting emittance dilution has been studied in detail in the literature. In this paper, a novel approach is provided that a lossy material is placed on the surface of the superconducting cavity to approximate the Q0 of the TESLA cavity, and a frequency solver of CST is used to simulate the electromagnetic field distribution, which is used to calculate coupler RF kick, and calibrated against the results of CST Particle Tracking Studio with a good agreement. In order to minimize the emittance growth of SHINE injector, a 1.3 GHz symmetric twin-coupler cavity is adoped in the single-cavity cryomodule, and the rotational angle and permutation of the 8 cavities in the 8-cavities cryomodule is optimized. Ultimately, the optimized emittance is lower than the design parameter

An effective fuel level data cleaning and repairing method for vehicle monitor platform

With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning towards a sustainable green transition. Fuel consumption is calculated by fuel level data collected from high precision fuel level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian Mixture Model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the Particle Swarm Optimization (PSO) algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel level data of vehicles collected over one month prove the modified GMM is superior to GMM-EM on fuel level data, and the proposed method is effective for cleaning and repairing outliers of fuel level data

CodeExp: Explanatory Code Document Generation

Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text generation models often produce only high-level summaries of code that do not capture implementation-level choices essential for these scenarios. To fill in this gap, we propose the code explanation generation task. We first conducted a human study to identify the criteria for high-quality explanatory docstring for code. Based on that, we collected and refined a large-scale code docstring corpus and formulated automatic evaluation metrics that best match human assessments. Finally, we present a multi-stage fine-tuning strategy and baseline models for the task. Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones. We envision our training dataset, human-evaluation protocol, recommended metrics, and fine-tuning strategy can boost future code explanation research. The code and annotated data are available at https://github.com/subercui/CodeExp.Comment: Accepted in Findings of EMNLP 202