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Preparation and thermoelectric properties of AgPb18SbTe20−xSex (x = 1, 2, 4) materials
H. Li,K.F. Cai,Y. Du,H.F. Wang,S.Z. Shen,X.L. Li,C.W. Zhou,Y.Y. Wang 한국물리학회 2012 Current Applied Physics Vol.12 No.1
AgPb18SbTe20xSex (x ¼ 1, 2, 4) bulk materials were prepared by combining hydrothermal synthesis and melting. Thermoelectric properties were measured from room temperature up to 773K. The materials showed n-type conduction and exhibited degenerate semiconductor behavior. The power factors of the materials varied greatly with increase of Se content (x). Partial substitution of Se for Te in AgPb18SbTe20resulted in remarkable reduction of thermal conductivity in the whole temperature range and increase of power factor at lower temperatures; therefore, the dimensionless figure of merit, ZT, was enhanced below 600K. A maximum ZT value of w0.82 is obtained at 523K for the AgPb18SbTe18Se2 sample.
Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection
X.K. Ai,W. Zheng,M. Zhang,D.L. Chen,C.S. Shen,B.H. Guo,B.J. Xiao,Y. Zhong,N.C. Wang,Z.J. Yang,Z.P. Chen,Z.Y. Chen,Y.H. Ding,Y. Pan Korean Nuclear Society 2024 Nuclear Engineering and Technology Vol.56 No.4
Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.
Measurement ofe+e−→π+π−ψ(2S)via initial state radiation at Belle
Wang, X. L.,Yuan, C. Z.,Shen, C. P.,Wang, P.,Abdesselam, A.,Adachi, I.,Aihara, H.,Al Said, S.,Arinstein, K.,Asner, D. M.,Ayad, R.,Bakich, A. M.,Bansal, V.,Bhuyan, B.,Bobrov, A.,Bonvicini, G.,Brač American Physical Society 2015 PHYSICAL REVIEW D - Vol.91 No.11