Three particle quantization condition in a finite volume: 2. general formalism and the analysis of data

We derive the three-body quantization condition in a finite volume using an effective field theory in the particle-dimer picture. Moreover, we consider the extraction of physical observables from the lattice spectrum using the quantization condition. To illustrate the general framework, we calculate the volume-dependent three-particle spectrum in a simple model both below and above the three-particle threshold. The relation to existing approaches is discussed in detail.Comment: 36 pages, 9 figure

A machine learning study to identify spinodal clumping in high energy nuclear collisions

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition

A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

Nanodot-Cavity Electrodynamics and Photon Entanglement

Quantum electrodynamics of excitons in a cavity is shown to be relevant to quantum operations. We present a theory of an integrable solid-state quantum controlled-phase gate for generating entanglement of two photons using a coupled nanodot-microcavity-fiber structure. A conditional phase shift of $O(\pi/10)$ is calculated to be the consequence of the giant optical nonlinearity keyed by the excitons in the cavities. Structural design and active control, such as electromagnetic induced transparency and pulse shaping, optimize the quantum efficiency of the gate operation.Comment: 4 pages 3 figure

Experiment study on the effect of iron ore sinter behavior with adding biomass

This paper focused on the effect of sinter behavior with biomass.The changes of the relevant performance indexes of this sinter behavior, emission laws of harmful gases in flue gas emissions and the mechanism of emission reduction was studied in this paper.The results showed that, when the biomass amount is 0,28 %, the sinter index can meet production requirement, the porosity of sinter increased by 18,5 %, the sinter reduction degree increased by 2,66 %, the SO2 emissions of harmful gases in the flue gas reduced by about 14 %, the amount of NOx about 19 % lower

Prediction on energy yields of PV systems

Version of RecordPublishe

Performance evaluation of hybrid solar parabolic trough concentrator systems in Hong Kong

Author name used in this publication: Edward W. C. LoVersion of RecordPublishe

Collective Flow from the Intranuclear Cascade Model

The phenomenon of collective flow in relativistic heavy ion collisions is studied using the hadronic cascade model ARC. Direct comparison is made to data gathered at the Bevalac, for Au+Au at $p=1-2$ GeV/c. In contrast to the standard lore about the cascade model, collective flow is well described quantitatively without the need for explicit mean field terms to simulate the nuclear equation of state. Pion collective flow is in the opposite direction to nucleon flow as is that of anti-nucleons and other produced particles. Pion and nucleon flow are predicted at AGS energies also, where, in light of the higher baryon densities achieved, we speculate that equation of state effects may be observable.Comment: 9 pages, 2 figures include

Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions

Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study emergence of the emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite network of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion-classifier developed for this type of texts. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore robustness of these critical states, we design a network automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states