Graphical Representations and Worm Algorithms for the O(NN) Spin Model

We present a family of graphical representations for the O($N$) spin model, where $N \ge 1$ represents the spin dimension, and $N=1,2,3$ corresponds to the Ising, XY and Heisenberg models, respectively. With an integer parameter $0 \le \ell \le N/2$, each configuration is the coupling of $\ell$ copies of subgraphs consisting of directed flows and $N -2\ell$ copies of subgraphs constructed by undirected loops, which we call the XY and Ising subgraphs, respectively. On each lattice site, the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition. Then, we formulate worm-type algorithms and simulate the O($N$) model on the simple-cubic lattice for $N$ from 2 to 6 at all possible $\ell$. It is observed that the worm algorithm has much higher efficiency than the Metropolis method, and, for a given $N$, the efficiency is an increasing function of $\ell$. Beside Monte Carlo simulations, we expect that these graphical representations would provide a convenient basis for the study of the O($N$) spin model by other state-of-the-art methods like the tensor network renormalization.Comment: 10 pages, 6 figure

A Socio-Technical Metaverse Development Framework in Higher Education

The concept of the metaverse has recently generated a great deal of attention in academia and industry, with an increasing number of educational institutions expressing interest in its implementation. However, existing studies on metaverse development in higher education are still in their early stages, leaving institutions with little guidance on how to develop and implement a metaverse. Employing socio-technical theory, we propose a comprehensive nine-stage metaverse development framework (MDF) that incorporates both social and technical aspects of a metaverse initiative, thus providing a holistic approach to metaverse development. Leveraging case studies of three large universities and blending them with MDF, our study provides evidence of the applicability of our MDF and offers a better contextual understanding of metaverse development in educational settings. This paper is useful for educational institutions that are developing or considering metaverse initiatives. It contributes to the emerging literature on metaverse development in higher education

Developing rAAV production platform with enhanced productivity, scalability and biosafety

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A cooperative-based model for smart-sensing tasks in fog computing

OAPA Fog Computing is currently receiving a great deal of focused attention. Fog Computing can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in Fog Computing (FC). When considering the Green Cloud (GC), people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center (DC). In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a Cooperative-based Model for Smart-Sensing Tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882

Massive Goldstone (Higgs) mode in two-dimensional ultracold atomic lattice systems

We discuss how to reveal the massive Goldstone mode, often referred to as the Higgs amplitude mode, near the superfluid-to-insulator quantum critical point (QCP) in a system of two-dimensional ultracold bosonic atoms in optical lattices. The spectral function of the amplitude response is obtained by analytic continuation of the kinetic energy correlation function calculated by Monte Carlo methods. Our results enable a direct comparison with the recent experiment [M. Endres, T. Fukuhara, D. Pekker, M. Cheneau, P. Schauß, C. Gross, E. Demler, S. Kuhr, and I. Bloch, Nature (London) 487, 454 (2012)] and demonstrate a good agreement for temperature shifts induced by lattice modulation. Based on our numerical analysis, we formulate the necessary conditions in terms of homogeneity, detuning from the QCP and temperature in order to reveal the massive Goldstone resonance peak in spectral functions experimentally. We also propose to apply a local modulation at the trap center to overcome the inhomogeneous broadening caused by the parabolic trap confinement