Current-Induced Step Bending Instability on Vicinal Surfaces

We model an apparent instability seen in recent experiments on current induced step bunching on Si(111) surfaces using a generalized 2D BCF model, where adatoms have a diffusion bias parallel to the step edges and there is an attachment barrier at the step edge. We find a new linear instability with novel step patterns. Monte Carlo simulations on a solid-on-solid model are used to study the instability beyond the linear regime.Comment: 4 pages, 4 figure

The electron pressure in the atmospheres of late-type dwarfs

Over-ionization of potassium and sodium in late-type dwarf star

On generalized cluster algorithms for frustrated spin models

Standard Monte Carlo cluster algorithms have proven to be very effective for many different spin models, however they fail for frustrated spin systems. Recently a generalized cluster algorithm was introduced that works extremely well for the fully frustrated Ising model on a square lattice, by placing bonds between sites based on information from plaquettes rather than links of the lattice. Here we study some properties of this algorithm and some variants of it. We introduce a practical methodology for constructing a generalized cluster algorithm for a given spin model, and investigate apply this method to some other frustrated Ising models. We find that such algorithms work well for simple fully frustrated Ising models in two dimensions, but appear to work poorly or not at all for more complex models such as spin glasses.Comment: 34 pages in RevTeX. No figures included. A compressed postscript file for the paper with figures can be obtained via anonymous ftp to minerva.npac.syr.edu in users/paulc/papers/SCCS-527.ps.Z. Syracuse University NPAC technical report SCCS-52

Dynamic behaviors in directed networks

Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks

Effect of biochar and nitrogen on soil characteristics, growth and yield of radish (Raphanus sativus L.) at Paklihawa, Rupandehi condition of Nepal

An experiment on effect of biochar and nitrogen on soil characteristics, growth and yield of radish (Raphanus sativus L.) was conducted at Institute of Agriculture and Animal Science (IAAS), Paklihawa, Rupandehi, from November 2019 to February 2020. The experiment was laid in Randomized Complete Block Design with two factors: nitrogen and biochar, each factor having four levels (biochar: 0 t/ha, 5 t/ha, 10 t/ha and 15 t/ha and nitrogen: 0 kg/ha, 50 kg/ha, 100 kg/ha, 200 kg/ha), resulting in sixteen treatment combinations. Biochar application was found to be effective in improving soil bulk density, pH, soil organic matter and soil nitrogen and potassium content. Application of nitrogen fertilizer (200 kg/ha) and biochar (15 t/ha) alone, and in combination, showed significantly higher root dry matter (15.83 gm, 16.63 gm and 20.57 gm), biological yield (80 t/ha, 63.75 t/ha, and 95.75) and root yield (26.74 t/ha, 24.06 t/ha and 30.32 t/ha). In comparison to the sole effects of the highest dose of nitrogen fertilizer (200 kg/ha) and the highest dose of biochar (15 t/ha), their combined application showed the increased yield in radish root by 13.38% and 26.01%, respectively, indicating that the combined effect of biochar and nitrogen is more productive for the growth and yield in radish crop as compared to the sole effect of nitrogen and biochar

Cluster update and recognition

We present a fast and robust cluster update algorithm that is especially efficient in implementing the task of image segmentation using the method of superparamagnetic clustering. We apply it to a Potts model with spin interactions that are are defined by gray-scale differences within the image. Motivated by biological systems, we introduce the concept of neural inhibition to the Potts model realization of the segmentation problem. Including the inhibition term in the Hamiltonian results in enhanced contrast and thereby significantly improves segmentation quality. As a second benefit we can - after equilibration - directly identify the image segments as the clusters formed by the clustering algorithm. To construct a new spin configuration the algorithm performs the standard steps of (1) forming clusters and of (2) updating the spins in a cluster simultaneously. As opposed to standard algorithms, however, we share the interaction energy between the two steps. Thus the update probabilities are not independent of the interaction energies. As a consequence, we observe an acceleration of the relaxation by a factor of 10 compared to the Swendson and Wang procedure.Comment: 4 pages, 2 figure

On U_q(SU(2))-symmetric Driven Diffusion

We study analytically a model where particles with a hard-core repulsion diffuse on a finite one-dimensional lattice with space-dependent, asymmetric hopping rates. The system dynamics are given by the \mbox{U$_{q}$[SU(2)]}-symmetric Hamiltonian of a generalized anisotropic Heisenberg antiferromagnet. Exploiting this symmetry we derive exact expressions for various correlation functions. We discuss the density profile and the two-point function and compute the correlation length $\xi_s$ as well as the correlation time $\xi_t$. The dynamics of the density and the correlations are shown to be governed by the energy gaps of a one-particle system. For large systems $\xi_s$ and $\xi_t$ depend only on the asymmetry. For small asymmetry one finds $\xi_t \sim \xi_s^2$ indicating a dynamical exponent $z=2$ as for symmetric diffusion.Comment: 10 pages, LATE