Pion transverse-momentum spectrum and elliptic anisotropy of partially coherent source

In this letter, we study the pion momentum distribution of a coherent source and investigate the influences of coherent emission on the pion transverse-momentum ($p_T$) spectrum and elliptic anisotropy. With a partially coherent source, constructed by a conventional viscous hydrodynamics model (chaotic part) and a parameterized expanding coherent source model, we reproduce the pion $p_T$ spectrum and elliptic anisotropy coefficient $v_2(p_T)$ in the peripheral Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV. It is found that the influences of coherent emission on the pion $p_T$ spectrum and $v_2(p_T)$ are related to the initial size and shape of the coherent source, largely due to the interference effect. However, the effect of source dynamical evolution on coherent emission is relatively small. The results of the partially coherent source with 33% coherent emission and 67% chaotic emission are consistent with the experimental measurements of the pion $p_T$ spectrum, $v_2(p_T)$, and especially four-pion Bose-Einstein correlations.Comment: 8 pages, 4 figure

Measuring the similarity of PML documents with RFID-based sensors

The Electronic Product Code (EPC) Network is an important part of the Internet of Things. The Physical Mark-Up Language (PML) is to represent and de-scribe data related to objects in EPC Network. The PML documents of each component to exchange data in EPC Network system are XML documents based on PML Core schema. For managing theses huge amount of PML documents of tags captured by Radio frequency identification (RFID) readers, it is inevitable to develop the high-performance technol-ogy, such as filtering and integrating these tag data. So in this paper, we propose an approach for meas-uring the similarity of PML documents based on Bayesian Network of several sensors. With respect to the features of PML, while measuring the similarity, we firstly reduce the redundancy data except information of EPC. On the basis of this, the Bayesian Network model derived from the structure of the PML documents being compared is constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin

Data driven modeling of self-similar dynamics

Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-similarity is prevalent in complex systems, hinting that large-scale complex systems can be modeled at a reduced cost. In this paper, we introduce a multiscale neural network framework that incorporates self-similarity as prior knowledge, facilitating the modeling of self-similar dynamical systems. For deterministic dynamics, our framework can discern whether the dynamics are self-similar. For uncertain dynamics, it can compare and determine which parameter set is closer to self-similarity. The framework allows us to extract scale-invariant kernels from the dynamics for modeling at any scale. Moreover, our method can identify the power law exponents in self-similar systems. Preliminary tests on the Ising model yielded critical exponents consistent with theoretical expectations, providing valuable insights for addressing critical phase transitions in non-equilibrium systems.Comment: 11 pages,5 figures,1 tabl