Application of value management in project briefing

Author name used in this publication: Qiping Shen2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Possible ΔΔ\Delta\Delta dibaryons in the quark cluster model

In the framework of RGM, the binding energy of one channel $\Delta\Delta_{(3,0)}$($d^*$) and $\Delta\Delta_{(0,3)}$ are studied in the chiral SU(3) quark cluster model. It is shown that the binding energies of the systems are a few tens of MeV. The behavior of the chiral field is also investigated by comparing the results with those in the SU(2) and the extended SU(2) chiral quark models. It is found that the symmetry property of the $\Delta\Delta$ system makes the contribution of the relative kinetic energy operator between two clusters attractive. This is very beneficial for forming the bound dibaryon. Meanwhile the chiral-quark field coupling also plays a very important role on binding. The S-wave phase shifts and the corresponding scattering lengths of the systems are also given.Comment: LeTex with 2 ps figure

OPERA superluminal neutrinos and Kinematics in Finsler spacetime

The OPERA collaboration recently reported that muon neutrinos could be superluminal. More recently, Cohen and Glashow pointed that such superluminal neutrinos would be suppressed since they lose their energies rapidly via bremsstrahlung. In this Letter, we propose that Finslerian nature of spacetime could account for the superluminal phenomena of particles. The Finsler spacetime permits the existence of superluminal behavior of particles while the casuality still holds. A new dispersion relation is obtained in a class of Finsler spacetime. It is shown that the superluminal speed is linearly dependent on the energy per unit mass of the particle. We find that such a superluminal speed formula is consistent with data of OPERA, MINOS and Fermilab-1979 neutrino experiments as well as observations on neutrinos from SN1987a.Comment: 10 pages, 2 figures. Viewpoints of Finslerian special relativity on OPERA superluminal neutrino

Exploration of the Survival Probability and Shape Evolution of Crushable Particles during One-Dimensional Compression Using Dyed Gypsum Particles

Observing the fragmentation of individual particles within granular assemblies is a subject of evident theoretical and practical importance. A new technique using dyed gypsum particles (DGPs) to match the broken particles to their parents was adopted in this study. An image-based method of acquiring the shape information of particles from two orthogonal views was proposed. The mass survival probability and shape characteristics of the children particles were analyzed after a series of one-dimensional compression tests on the DGPs. It was found that medium-sized particles in the polydisperse samples underwent more breakage than the other particles, and this might have been attributed to the combined effects of the particle crushing strength and the coordination number. The shape evolution of broken particles and surviving particles showed opposite trends. Because the particles after the test within a given size range consisted of both the broken and surviving particles, their overall shape characteristics did not show a consistent trend. Furthermore, individual particle crushing tests on the children particles suggested that the breakage-induced shape irregularity did not change the Weibull modulus, but had a substantial effect on the magnitude of the survival probability

A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored