Response of Rice Varieties to Bound Residues of Metsulfuron-Methyl in a Paddy Soil

Metsulfuron-methyl is one of the widely used sulfonylurea herbicides. However, approximately half of the applied metsulfuron-methyl may remain as bound residues in soil. To characterize the response of rice plants to residual metsulfuron-methyl in soil, the activities of acetolactate synthase （ALS）, superoxide dismutase （SOD）, peroxidase （POD）, and catalase （CAT） were investigated in two rice varieties that differed in susceptibility to the herbicide. Changes in the activity of these enzymes in leaves and roots of Xiushui 63, a sensitive rice variety, were greater than those in a resistant variety Zhenong 952. Irrespective of variety, changes in the enzyme activity were greater in the roots than in the leaves. The activities of ALS and CAT decreased, while the SOD activity increased with the increase in the amounts of bound residues of metsulfuron-methyl （BRM） in soil. The POD activity increased at the BRM level of 0.025 mg kg^-1, but decreased at the BRM level of 0.05 mg kg^-1. The results showed that the bound residues of sulfonylurea herbicides may affect metabolism of rice plants.

Coarsening Kinetics of γ' Precipitates in Dendritic Regions of a Ni_3 Al Base Alloy

Coarsening behavior of γ＇ precipitates in the dendritic regions of a Ni 3 Al base alloy containing chromium,molybdenum,zirconium and boron was investigated.Annealing treatment was performed up to 50 h at 900,1000 and 1100℃.The alloy was produced by vacuum-arc remelting technique.Results show that coarsening of the γ＇ precipitates in this complex alloy containing high volume fractions of γ＇ phase follows Lifshitz-Slyozov-Wagner（LSW） theory.Coarsening activation energy of the γ＇ precipitates was evaluated to be about 253.5 kJ.mol-1 which shows that the growth phenomenon is controlled by volume diffusion of aluminum.With an innovative approach,diffusion coefficient of the solute element（s） and the interfacial energy between γ＇ precipitates and γ＇（matrix） were estimated at 900,1000 and 1100℃.Accordingly,the interfacial energies at 900,1000 and 1100℃ are 4.49±1.48,2.08±0.69 and 0.98±0.32 mJ.m-2,respectively.Also the diffusivities of solute element（s） at these temperatures are 3.41±1.08,30±9.5 and 145.15±45.85（10-15 m-2.s-1）,respectively.

Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

The sampling of the training data is a bottleneck in the development of artificial intelligence(AI)models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices.Active learning(AL)approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples.Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence.To judge the relevance of a data instance,query rules are defined.In this paper,we propose an AL methodology based on a physics-based query rule.Given some industrial objectives from the physical process where the AI model is implied in,the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points.Therefore,the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are,while coarse everywhere else where the data instances are of no interest in the industrial context studied.