Electronic structures and magnetic orders of Fe-vacancies ordered ternary iron selenides TlFe1.5_{1.5}Se2_2 and AFe1.5_{1.5}Se2_2 (A=K, Rb, or Cs)

By the first-principles electronic structure calculations, we find that the ground state of the Fe-vacancies ordered TlFe$_{1.5}$Se$_2$ is a quasi-two-dimensional collinear antiferromagnetic semiconductor with an energy gap of 94 meV, in agreement with experimental measurements. This antiferromagnetic order is driven by the Se-bridged antiferromagnetic superexchange interactions between Fe moments. Similarly, we find that crystals AFe$_{1.5}$Se$_2$ (A=K, Rb, or Cs) are also antiferromagnetic semiconductors but with a zero-gap semiconducting state or semimetallic state nearly degenerated with the ground states. Thus rich physical properties and phase diagrams are expected.Comment: Add results about AFe$_{1.5}$Se$_2$ (A=K, Rb, or Cs);4 pages and 7 figure

Dual-mode mechanical resonance of individual ZnO nanobelts

©2003 American Institute of Physics. The electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?APPLAB/82/4806/1DOI:10.1063/1.1587878The mechanical resonance of a single ZnO nanobelt, induced by an alternative electric field, was studied by in situ transmission electron microscopy. Due to the rectangular cross section of the nanobelt, two fundamental resonance modes have been observed corresponding to two orthogonal transverse vibration directions, showing the versatile applications of nanobelts as nanocantilevers and nanoresonators. The bending modulus of the ZnO nanobelts was measured to be ~52 GPa and the damping time constant of the resonance in a vacuum of 5×10–8 Torr was ~1.2 ms and quality factor Q = 500

SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering

Version information plays an important role in spreadsheet understanding, maintaining and quality improving. However, end users rarely use version control tools to document spreadsheet version information. Thus, the spreadsheet version information is missing, and different versions of a spreadsheet coexist as individual and similar spreadsheets. Existing approaches try to recover spreadsheet version information through clustering these similar spreadsheets based on spreadsheet filenames or related email conversation. However, the applicability and accuracy of existing clustering approaches are limited due to the necessary information (e.g., filenames and email conversation) is usually missing. We inspected the versioned spreadsheets in VEnron, which is extracted from the Enron Corporation. In VEnron, the different versions of a spreadsheet are clustered into an evolution group. We observed that the versioned spreadsheets in each evolution group exhibit certain common features (e.g., similar table headers and worksheet names). Based on this observation, we proposed an automatic clustering algorithm, SpreadCluster. SpreadCluster learns the criteria of features from the versioned spreadsheets in VEnron, and then automatically clusters spreadsheets with the similar features into the same evolution group. We applied SpreadCluster on all spreadsheets in the Enron corpus. The evaluation result shows that SpreadCluster could cluster spreadsheets with higher precision and recall rate than the filename-based approach used by VEnron. Based on the clustering result by SpreadCluster, we further created a new versioned spreadsheet corpus VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the other two spreadsheet corpora FUSE and EUSES. The results show that SpreadCluster can cluster the versioned spreadsheets in these two corpora with high precision.Comment: 12 pages, MSR 201