Studying Diquark Structure of Heavy Baryons in Relativistic Heavy Ion Collisions

We propose the enhancement of $\Lambda_c$ yield in heavy ion collisions at RHIC and LHC as a novel signal for the existence of diquarks in the strongly coupled quark-gluon plasma produced in these collisions as well as in the $\Lambda_c$. Assuming that stable bound diquarks can exist in the quark-gluon plasma, we argue that the yield of $\Lambda_c$ would be increased by two-body collisions between $ud$ diquarks and $c$ quarks, in addition to normal three-body collisions among $u$, $d$ and $c$ quarks. A quantitative study of this effect based on the coalescence model shows that including the contribution of diquarks to $\Lambda_c$ production indeed leads to a substantial enhancement of the $\Lambda_c/D$ ratio in heavy ion collisions.Comment: Prepared for Chiral Symmetry in Hadron and Nuclear Physics (Chiral07), Nov. 13-16, 2007, Osaka, Japa

Complex imaging of phase domains by deep neural networks

The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data

Green Chemistry to the Resque of Disasters of the 1900 -2020 Period

There is uncertainty about several aspects of the Covid-19 origin story that scientists are trying hard to unravel, including which species passed to humans. They are trying hard because knowing how a pandemic starts is a key to stopping the next one. Green chemistry emerged from a variety of existing ideas and research efforts - characterization is one major analytical technique in our laboratories and so Scientists moved rapidly to characterize 2019-nCoV and widely disseminated their findings amongst the international research community as quickly as possible including the basic Viral Structure and Mechanism of Infection. Coronaviruses are large, enveloped, positive-stranded RNA viruses. They have the largest genome among all RNA viruses, typically ranging from 27 to 32 kb. The genome is packed inside a helical capsid formed by the nucleocapsid protein (N) and further surrounded by an envelope. One important example of this is the homology models of the novel coronavirus cysteine protease produced by Martin Stoermer etal (2020) . It is also established that disasters during the century 1900- 2020 were avoidable if the principles of green chemistry were applied to prevent future pandemics. Keywords: Coronaviruses, Covid-19, Green chemistry, RNA, Pandemic, Characterization DOI: 10.7176/JEES/11-2-03 Publication date: February 28th 202

Dislocation-induced superfluidity in a model supersolid

Motivated by recent experiments on the supersolid behavior of $^4$He, we study the effect of an edge dislocation in promoting superfluidity in a Bose crystal. Using Landau theory, we couple the elastic strain field of the dislocation to the superfluid density, and use a linear analysis to show that superfluidity nucleates on the dislocation before occurring in the bulk of the solid. Moving beyond the linear analysis, we develop a systematic perturbation theory in the weakly nonlinear regime, and use this method to integrate out transverse degrees of freedom and derive a one-dimensional Landau equation for the superfluid order parameter. We then extend our analysis to a network of dislocation lines, and derive an XY model for the dislocation network by integrating over fluctuations in the order parameter. Our results show that the ordering temperature for the network has a sensitive dependence on the dislocation density, consistent with numerous experiments that find a clear connection between the sample quality and the supersolid response.Comment: 10 pages, 6 figure

Control of carbon nanotube morphology by change of applied bias field during growth

Carbon nanotube morphology has been engineered via simple control of applied voltage during dc plasma chemical vapor deposition growth. Below a critical applied voltage, a nanotube configuration of vertically aligned tubes with a constant diameter is obtained. Above the critical voltage, a nanocone-type configuration is obtained. The strongly field-dependent transition in morphology is attributed primarily to the plasma etching and decrease in the size of nanotube-nucleating catalyst particles. A two-step control of applied voltage allows a creation of dual-structured nanotube morphology consisting of a broad base nanocone (~200 nm dia.) with a small diameter nanotube (~7 nm) vertically emanating from the apex of the nanocone, which may be useful for atomic force microscopy

Magnetic levitation force between a superconducting bulk magnet and a permanent magnet

The current density in a disk-shaped superconducting bulk magnet and the magnetic levitation force exerted on the superconducting bulk magnet by a cylindrical permanent magnet are calculated from first principles. The effect of the superconducting parameters of the superconducting bulk is taken into account by assuming the voltage-current law and the material law. The magnetic levitation force is dominated by the remnant current density, which is induced by switching off the applied magnetizing field. High critical current density and flux creep exponent may increase the magnetic levitation force. Large volume and high aspect ratio of the superconducting bulk can enhance the magnetic levitation force further.Comment: 18 pages and 8 figure

A performance comparison of the contiguous allocation strategies in 3D mesh connected multicomputers

The performance of contiguous allocation strategies can be significantly affected by the distribution of job execution times. In this paper, the performance of the existing contiguous allocation strategies for 3D mesh multicomputers is re-visited in the context of heavy-tailed distributions (e.g., a Bounded Pareto distribution). The strategies are evaluated and compared using simulation experiments for both First-Come-First-Served (FCFS) and Shortest-Service-Demand (SSD) scheduling strategies under a variety of system loads and system sizes. The results show that the performance of the allocation strategies degrades considerably when job execution times follow a heavy-tailed distribution. Moreover, SSD copes much better than FCFS scheduling strategy in the presence of heavy-tailed job execution times. The results also show that the strategies that depend on a list of allocated sub-meshes for both allocation and deallocation have lower allocation overhead and deliver good system performance in terms of average turnaround time and mean system utilization