Cavitation-induced ignition of cryogenic hydrogen-oxygen fluids

The Challenger disaster and purposeful experiments with liquid hydrogen (H2) and oxygen (Ox) tanks demonstrated that cryogenic H2/Ox fluids always self-ignite in the process of their mixing. Here we propose a cavitation-induced self-ignition mechanism that may be realized under these conditions. In one possible scenario, self-ignition is caused by the strong shock waves generated by the collapse of pure Ox vapor bubble near the surface of the Ox liquid that may initiate detonation of the gaseous H2/Ox mixture adjacent to the gas-liquid interface. This effect is further enhanced by H2/Ox combustion inside the collapsing bubble in the presence of admixed H2 gas

Convex recovery of a structured signal from independent random linear measurements

This chapter develops a theoretical analysis of the convex programming method for recovering a structured signal from independent random linear measurements. This technique delivers bounds for the sampling complexity that are similar with recent results for standard Gaussian measurements, but the argument applies to a much wider class of measurement ensembles. To demonstrate the power of this approach, the paper presents a short analysis of phase retrieval by trace-norm minimization. The key technical tool is a framework, due to Mendelson and coauthors, for bounding a nonnegative empirical process.Comment: 18 pages, 1 figure. To appear in "Sampling Theory, a Renaissance." v2: minor corrections. v3: updated citations and increased emphasis on Mendelson's contribution

The Gaussian graphical model in cross-sectional and time-series data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in 3 kinds of psychological datasets: datasets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means---the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc

Moderated Network Models

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification

Variable-Range Hopping of Spin Polarons: Magnetoresistance in a Modified Mott Regime

We analize electrical conductivity controlled by hopping of bound spin polarons in disordered solids with wide distributions of electron energies and polaron shifts (barriers). By means of percolation theory and Monte Carlo simulations we have shown that in such materials at low temperatures, when hopping occurs in the vicinity of the Fermi level, a hard polaron gap does not manifest itself in the transport properties. This happens because as temperature decreases the hopping polaron trades the decreasing electron and polaron barriers for increasing hopping distance. As a result, in the absence of the Coulomb correlation effects, in this variable-range variable-barrier hopping regime, the electrical resistivity as a function of temperature obeys a non-activation law, which differs from the standard Mott law

Deciphering the stem cell machinery as a basis for understanding the molecular mechanism underlying reprogramming

Stem cells provide fascinating prospects for biomedical applications by combining the ability to renew themselves and to differentiate into specialized cell types. Since the first isolation of embryonic stem (ES) cells about 30 years ago, there has been a series of groundbreaking discoveries that have the potential to revolutionize modern life science. For a long time, embryos or germ cell-derived cells were thought to be the only source of pluripotency—a dogma that has been challenged during the last decade. Several findings revealed that cell differentiation from (stem) cells to mature cells is not in fact an irreversible process. The molecular mechanism underlying cellular reprogramming is poorly understood thus far. Identifying how pluripotency maintenance takes place in ES cells can help us to understand how pluripotency induction is regulated. Here, we review recent advances in the field of stem cell regulation focusing on key transcription factors and their functional interplay with non-coding RNAs