We present comprehensive studies of strain effects on the spin reorientation transition (SRT), the so-called Morin transition, in α-Fe2O3(0001) films. The α-Fe2O3(0001) epitaxial films were grown with a Cr2O3 buffer layer on Al2O3(0001) substrates through an oxide molecular beam epitaxy. The antiferromagnetic spin axis was monitored by using the Fe L2-edge X-ray magnetic linear dichroism. The buffer layer was found to introduce compressive strain into the α-Fe2O3(0001) film due to its 1.6% smaller in-plane lattice constant. The degree of strain is monotonically reduced with the increase of the α-Fe2O3 film thickness and becomes relaxed in the thick region (>20 nm). The transition temperature TM , which increases up to 360 K, well above the bulk TM = 263 K, for the film thickness 3 nm, gradually decreases as the film thickness increases. We also examined the Néel temperature TN in the ultra-thin region (<3 nm), which rapidly drops with the decrease of the film thickness. The correlation between TM and the strain in the α-Fe2O3(0001) epitaxial films was found to be well explained in terms of two competing energies of magnetic dipole anisotropy and single-ion magnetocrystalline anisotropy except for the ultra-thin region, in which TN is dominated by the finite-size effects.
Detecting differences between XML documents is one of most important research topics for XML. Since XML documents are generally considered to be organized in a tree structure, most previous research has attempted to detect differences using tree-matching algorithms. However, most tree-matching algorithms have inadequate performance owing to limitations in terms of the execution time, optimality and scalability. This study proposes a stream-based difference detection method in which an XML binary encoding algorithm is used to provide improved performance relative to that of previous tree-matching algorithms. A tree-structured analysis of XML is not essential in order to detect differences. We use a D-Path algorithm that has an optimal result quality for difference detection between two streams and has a lower time complexity than tree-based methods. We then modify the existing XML binary encoding method to tokenize the stream and the algorithm in order to support more operations than D-Path algorithm does. The experimental results reveal greater efficiency for the proposed method relative to tree-based methods. The execution time is at least 4 times faster than state-of-the-art tree-based methods. In addition, the scalability is much more efficient.
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