Unveiling the Re,Cr,and I diffusion in saturated compacted bentonite using machine-learning methods

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.

A code-based approach for labeling in complex irregular regions

Labeling information in a complex irregular region is a useful procedure occurring frequently in sheet metal and the furniture industry which will be beneficial in parts management.A fast code-based labeler(FCBL) is proposed to accomplish this objective in this paper.The region is first discretized,and then encoded by the Freeman encoding technique for providing the 2D regional information by 1D codes with redundancies omitted.We enhance the encoding scheme to make it more suitable for our complex problem.Based on the codes,searching algorithms are designed and can be extended with customized constraints.In addition,by introducing a smart optimal direction estimation,the labeling speed and accuracy of FCBL are significantly improved.Experiments with a large range of real data gained from industrial factories demonstrate the stability and millisecond-level speed of FCBL.The proposed method has been integrated into a shipbuilding CAD system,and plays a very important role in ship parts labeling process.

喷油嘴喷孔直径和长度自动测量方法的开发和应用（英文）

目的:直喷内燃机工作过程中,燃油通过喷油嘴上的微小喷孔(直径为100–200μm)输送到燃烧室内进行燃烧做功。为定量研究喷孔直径和长度等结构参数对燃油输运过程的影响,首先需要实现对这些结构参数的高精度测量。创新点:对喷油嘴喷孔入口和出口等重要结构特征进行精确地定义,实现喷油嘴微小喷孔的直径和长度的高精度自动测量。方法:1.基于同步辐射X射线CT扫描技术实现喷油嘴顶部结构的三维重构;2.结合几何学原理,从这些离散的三维数字结构数据中提取宏观几何特征参数。结论:1.对喷油嘴喷孔结构特征参数进行了精确地定义;2.基于微米级精度建立了喷油嘴喷孔直径和长度的自动测量方法,实现喷孔沿其轴线从入口到出口的直径分布的自动测量。