Structure and dynamics of the interface between a binary hard-sphere crystal of NaCl type and its coexisting binary fluid

Molecular dynamics simulations are performed to study the [100] and [111] orientations of the crystal-melt interface between an ordered two-component hard sphere with a NaCl structure and its coexisting binary hard-sphere fluid. The diameter ratio of the two types of hard spheres making up the mixture is taken to be 0.414. This work complements our earlier interface simulations [J. Chem. Phys.116, 3410] for the same diameter ratio at lower pressures where the smaller component is immiscible in the solid and the fluid mixture coexists with a pure FCC crystal of large particles. Density profiles and diffusion coefficient profiles are presented for the AB interfacial system. We find that for this system, the transition from crystal-like to fluid-like behavior of both the density and diffusion constant profiles occurs over a narrower region than that seen in our previous studies [J. Chem. Phys. 116, 3410] of the FCC/binary fluid system. But similar to what was found in the FCC/binary fluid interface the transition region for the large particle diffusion constant is shifted about the size of the large particles toward the fluid phase relative to that for the small particles.Comment: 8 page

Adjusting the melting point of a model system via Gibbs-Duhem integration: application to a model of Aluminum

Model interaction potentials for real materials are generally optimized with respect to only those experimental properties that are easily evaluated as mechanical averages (e.g., elastic constants (at T=0 K), static lattice energies and liquid structure). For such potentials, agreement with experiment for the non-mechanical properties, such as the melting point, is not guaranteed and such values can deviate significantly from experiment. We present a method for re-parameterizing any model interaction potential of a real material to adjust its melting temperature to a value that is closer to its experimental melting temperature. This is done without significantly affecting the mechanical properties for which the potential was modeled. This method is an application of Gibbs-Duhem integration [D. Kofke, Mol. Phys.78, 1331 (1993)]. As a test we apply the method to an embedded atom model of aluminum [J. Mei and J.W. Davenport, Phys. Rev. B 46, 21 (1992)] for which the melting temperature for the thermodynamic limit is 826.4 +/- 1.3K - somewhat below the experimental value of 933K. After re-parameterization, the melting temperature of the modified potential is found to be 931.5K +/- 1.5K.Comment: 9 pages, 5 figures, 4 table

Interaction of quasilocal harmonic modes and boson peak in glasses

The direct proportionality relation between the boson peak maximum in glasses, $\omega_b$, and the Ioffe-Regel crossover frequency for phonons, $\omega_d$, is established. For several investigated materials $\omega_b = (1.5\pm 0.1)\omega_d$. At the frequency $\omega_d$ the mean free path of the phonons $l$ becomes equal to their wavelength because of strong resonant scattering on quasilocal harmonic oscillators. Above this frequency phonons cease to exist. We prove that the established correlation between $\omega_b$ and $\omega_d$ holds in the general case and is a direct consequence of bilinear coupling of quasilocal oscillators with the strain field.Comment: RevTex, 4 pages, 1 figur

Environmental Benefits of Biochar

Understanding and improving environmental quality by reducing soil nutrient leaching losses, reducing bioavailability of environmental contaminants, sequestering C, reducing greenhouse gas emissions, and enhancing crop productivity in highly weathered or degraded soils, has been the goal of agroecosystem researchers and producers for years. Biochar, produced by pyrolysis of biomass, may help attain these goals. The desire to advance understanding of the environmental and agronomic implication of biochar utilization led to the organization of the 2010 ASA-CSSA-SSSA Environmental Quality Division session entitled Biochar Effects on the Environment and Agricultural Productivity (Long Beach, CA; Oct. 31-Nov. 3). Twenty-five presentations from this session, and sessions at the 2010 U.S. Biochar Initiative Conference (Ames, IA; June 27-30) and Biochar Symposium 2010 (organized by Dr. Bruno Glaser; University of Bayreuth, Bayreuth, Germany, July 8-9), make up a special collection of review and technical research papers focused on biochar creation and utilization. Individual contributions deal with improvement of the biochar knowledge base, current information gaps, and future biochar research needs. The prospect of biochar utilization is promising, as biochars may be custom designed for specific environmental applications

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Search for black holes and other new phenomena in high-multiplicity final states in proton-proton collisions at root s=13 TeV

Peer reviewe

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

Peer reviewe

Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search

Peer reviewe