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Low activation ferritic martensitic steel is considered as one of the candidate structural materials for the fourth generation reactor and fusion reactor. Low activation ferritic martensitic steel is based on Fe-Cr alloy, in which W is an important solute element. Irradiation embrittlement at low temperature is one of the important problems limiting the service of low-activation ferrite martensitic steels. The precipitated clusters during the service process cause hardening and embrittlement of the material due to the obstruction of dislocation movement by the precipitated clusters. An in-depth understanding of cluster precipitation behavior during service is helpful to understand the irradiation embrittlement of low-activation ferrite martensitic steels at low temperature. In order to simulate the precipitation kinetics of Fe-Cr alloy, by introducing composition-dependent pair potential, some improvements were made on the MIET_AKMC software developed by ourselves. The precipitation kinetics of coherent Cr-rich precipitates/clusters in Fe-Cr(8%, 10%, 16%, 20%)-W(0%, 1%, 2%) alloys during thermal ageing was simulated using the atomic kinetic Monte Carlo (AKMC) method. After thermal ageing, Cr precipitate into clusters to form Cr-rich precipitates/clusters, while W is still in a solid solution state. At the beginning of coarsening process, the radius of clusters, which was about (0.57±0.03) nm, was independent on simulation temperature and the initial Cr content in the box. The precipitation process of Cr cluster can be divided into three stages: nucleation, growth and coarsening. The Cr concentration in box and simulation temperature have an influence on the precipitation rate of Cr clusters. The presence of W can delay the precipitation kinetic process and the effect is most significant when Cr content is about 10%. This is due to that when Cr content is about 10%, the absolute value of the short-range order parameter for Fe-Cr has a maximum, quite independent of the composition and the temperature. The delay effect of W was related with the stronger binding energy between W and vacancy. For the binding energy between Cr and vacancy is 0.06 eV, while the binding energy between W and vacancy is 0.14 eV. From the simulation results, it can be deduced that W is benefit for materials performance, expect for solution strengthening, it can delay the precipitation process of Cr-rich clusters, which is harmful for materials performance.

Since the beginning of the 21st century, environmental protection and digital supply chains have increasingly garnered attention. As firms transition to green practices, how to achieve competitive advantage by reducing environmental costs has become one of the key concerns for many business managers. This study aims to examine how supplier and customer integration affect a firm's environmental cost performance while considering the moderating effect of digital transformation. Following a survey conducted by a professional research agency on 800 Chinese manufacturing firms specializing in green products, the research model was tested using structural equation modeling and hierarchical regression analysis. The results indicate a significant positive relationship between both supplier integration and customer integration with a firm's environmental cost performance. Additionally, positive digital transformation reinforces the relationship between supplier integration and customer integration. However, there are limitations due to the study's background, scope, and sample size. This study underscores the impact of supplier integration and customer integration on a firm's environmental cost performance, as well as the crucial moderating role of digital transformation. It contributes to the growing literature on sustainable supply chain management and provides valuable insights for relevant practitioners. [ABSTRACT FROM AUTHOR]

The identification of components iiSkwaste temiles has the proJJems of high labor cost and low efficiency. A machine learning rapid identification method based on Raman spectroscopy is a potential solution to solve this problem. However, machine learning methods typically require a large amount of data for training. In order to reduce the cost of data collection and improve the classification accuracy of models for textile fibers in small sample Raman spectroscopy datasets, a Raman spectroscopy data aupnentation method was proposed. This method enhances the data on preprocessed Raman spectroscopy datasets of 5 textile fibers by specifying Pearson correlation coefficients combined with signal-to-noise ratio formula for noise superposition and linear combination of Dirichlet distribution. The results show that after data augmentation, the SVMp°ly model achieves an average accuracy of 92. 4% in 10 rounds of 2-fold cross validation, which is 68. 2% higher than the original Raman spectroscopy dataset. This data augmentation method can enrich the diversity of samples while expanding the dataset, thereby improving the classification performance of the model. [ABSTRACT FROM AUTHOR]