Response characteristics of MHOST (MARC Hot-Section Technology) for 3-D inelastic analysis of hot-section components

The advantages of a newly developed code are demonstrated by comparisons of the analysis with existing theoretical data as well as with other available finite element programs. The new program shows promise to significantly reduce the computer time. It also permits accurate and efficient structural analysis of engine hot section components

Sensor Selection Based on Generalized Information Gain for Target Tracking in Large Sensor Networks

In this paper, sensor selection problems for target tracking in large sensor networks with linear equality or inequality constraints are considered. First, we derive an equivalent Kalman filter for sensor selection, i.e., generalized information filter. Then, under a regularity condition, we prove that the multistage look-ahead policy that minimizes either the final or the average estimation error covariances of next multiple time steps is equivalent to a myopic sensor selection policy that maximizes the trace of the generalized information gain at each time step. Moreover, when the measurement noises are uncorrelated between sensors, the optimal solution can be obtained analytically for sensor selection when constraints are temporally separable. When constraints are temporally inseparable, sensor selections can be obtained by approximately solving a linear programming problem so that the sensor selection problem for a large sensor network can be dealt with quickly. Although there is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small, numerical examples suggest that the algorithm is near-optimal in many cases. Finally, when the measurement noises are correlated between sensors, the sensor selection problem with temporally inseparable constraints can be relaxed to a Boolean quadratic programming problem which can be efficiently solved by a Gaussian randomization procedure along with solving a semi-definite programming problem. Numerical examples show that the proposed method is much better than the method that ignores dependence of noises.Comment: 38 pages, 14 figures, submitted to Journa

Relativistic Equation of State for Core-Collapse Supernova Simulations

We construct the equation of state (EOS) of dense matter covering a wide range of temperature, proton fraction, and density for the use of core-collapse supernova simulations. The study is based on the relativistic mean-field (RMF) theory, which can provide an excellent description of nuclear matter and finite nuclei. The Thomas--Fermi approximation in combination with assumed nucleon distribution functions and a free energy minimization is adopted to describe the non-uniform matter, which is composed of a lattice of heavy nuclei. We treat the uniform matter and non-uniform matter consistently using the same RMF theory. We present two sets of EOS tables, namely EOS2 and EOS3. EOS2 is an update of our earlier work published in 1998 (EOS1), where only the nucleon degree of freedom is taken into account. EOS3 includes additional contributions from $\Lambda$ hyperons. The effect of $\Lambda$ hyperons on the EOS is negligible in the low-temperature and low-density region, whereas it tends to soften the EOS at high density. In comparison with EOS1, EOS2 and EOS3 have an improved design of ranges and grids, which covers the temperature range $T=0.1$--$10^{2.6}$ MeV with the logarithmic grid spacing $\Delta \log_{10}(T/\rm{[MeV]})=0.04$ (92 points including T=0), the proton fraction range $Y_p=0$--0.65 with the linear grid spacing $\Delta Y_p = 0.01$ (66 points), and the density range $\rho_B=10^{5.1}$--$10^{16}\,\rm{g\,cm^{-3}}$ with the logarithmic grid spacing $\Delta \log_{10}(\rho_B/\rm{[g\,cm^{-3}]}) = 0.1$ (110 points).Comment: 43 pages, 10 figure

Relativistic Equation of State of Nuclear Matter for Supernova Explosion

We construct the equation of state (EOS) of nuclear matter at finite temperature and density with various proton fractions within the relativistic mean field (RMF) theory for the use in the supernova simulations. The Thomas-Fermi approximation is adopted to describe the non-uniform matter where we consider nucleus, alpha-particle, proton and neutron in equilibrium. We treat the uniform matter and non-uniform matter consistently using the RMF theory. We tabulate the outcome as the pressure, free energy, entropy etc, with enough mesh points in wide ranges of the temperature, proton fraction, and baryon mass density.Comment: 22 pages, LaTeX, 9 ps-figures, Submitted to Prog.Theor.Phy

A multi-protein receptor-ligand complex underlies combinatorial dendrite guidance choices in C. elegans.

Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors