Transfer-matrix renormalization group study of the spin ladders with cyclic four-spin interactions

The temperature dependence of the specific heat and spin susceptibility of the spin ladders with cyclic four-spin interactions in the rung-singlet phase is explored by making use of the transfer-matrix renormalization group method. The values of spin gap are extracted from the specific heat and susceptibility, respectively. It is found that for different relative strength between interchain and intrachain interactions, the spin gap is approximately linear with the cyclic four-spin interaction in the region far away from the critical point. Furthermore, we show that the dispersion for the one-triplet magnon branch can be obtained by numerically fitting on the partition function.Comment: 7 pages, 7 figures, 1 tabl

Kondo effect in complex mesoscopic structures

We study the Kondo effect of a quantum dot placed in a complex mesoscopic structure. Assuming that electronic interactions are taking place solely on the dot, and focusing on the infinite Hubbard interaction limit, we use a decoupling scheme to obtain an explicit analytic approximate expression for the dot Green function, which fulfills certain Fermi-liquid relations at zero temperature. The details of the complex structure enter into this expression only via the self-energy for the non-interacting case. The effectiveness of the expression is demonstrated for the single impurity Anderson model and for the T-shaped network.Comment: 12 pages 6 figure

Topological shape optimization of multifunctional tissue engineering scaffolds with level set method

© 2016, Springer-Verlag Berlin Heidelberg. A tissue engineering scaffold provides a proper environment to support physiological loads, and enhance cell migration and delivery for re-modeling of regenerating tissue. Hence, in the design of scaffolds, it is required to control the scaffold architecture with mechanical and mass transport properties simultaneously. In this paper, a level set-based topology optimization method will be developed to systematically generate three dimensional (3D) microstructures for tissue engineering scaffolds, with the prescribed properties for mechanical stiffness, fluid porosity and permeability. To create the internal architecture for scaffolds with desired properties, the numerical homogenization method will be used to evaluate the effective properties of the microstructure for building the periodic composite media, and a parametric level set method will be introduced to find the optimized shape and topology of the microstructure. Several numerical examples are used to demonstrate the effectiveness of the proposed method in achieving scaffolds with desired multifunctional properties, within the numerically estimated cross-property bounds between the effective bulk modulus and permeability under different porosities

Batch matching of conjunctive triple patterns over linked data streams in the internet of things

The Internet of Things (IoT) envisions smart objects col lecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic technologies, such as Linked Data, which can facilitate machine- to-machine (M2M) communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-automata, to meet the high performance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-automata, the proposed system can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Ali Shemshadi, Edward Curr

Querying cohesive subgraphs by keywords

© 2018 IEEE. Keyword search problem has been widely studied to retrieve related substructures from graphs for a keyword set. However, existing well-studied approaches aim at finding compact trees/subgraphs containing the keywords, and ignore a critical measure, density, to reflect how strongly and stablely the keyword nodes are connected in the substructure. In this paper, we study the problem of finding a cohesive subgraph containing the query keywords based on the k-Truss model, and formulate it as minimal dense truss search problem, i.e., finding minimal subgraph with maximum trussness covering the keywords. We first propose an efficient algorithm to find the dense truss with the maximum trussness containing keywords based on a novel hybrid KT-Index (Keyword-Truss Index). Then, we develop a novel refinement approach to extract the minimal dense truss based on the anti-monotonicity property of k-Truss. Experimental studies on real datasets show the outperformance of our method