Andreev experiments on superconductor/ferromagnet point contacts

Andreev reflection is a smart tool to investigate the spin polarisation P of the current through point contacts between a superconductor and a ferromagnet. We compare different models to extract P from experimental data and investigate the dependence of P on different contact parameters.Comment: 14 pages, 5 figures, accepted for publication in Fizika Nizkikh Temperatu

Grain size analysis in permanent magnets from Kerr microscopy images using machine learning techniques

Understanding the relationships between composition, structure, processing and properties helps in the development of improved materials for known applications as well as for new applications. Materials scientists, chemists and physicists have researched these relationships for many years, until the recent past, by characterizing the bulk properties of functional materials and describing them with theoretical models. Magnets are widly used in electric vehicles (EV), hybrid electric vehicles (HEV), data storage, power generation and transmission, sensors etc. The search for novel magnetic phases requires an efficient quantitative microstructure analysis of microstructural information like phases, grain distribution and micromagnetic structural information like domain patterns, and correlating the information with intrinsic magnetic parameters of magnet samples. The information out of micromagnetic domains helps in obtaining the optimized microstructures in magnets that have good intrinsic magnetic properties. This paper is aimed at introducing the use of a traditional machine learning (ML) model with a higher dimensional feature set and a deep learning (DL) model to classify various regions in sintered NdFeB magnets based on Kerr-microscopy images. The obtained results are compared against reference data, which is generated manually by subject experts. Additionally, the results were compared against the approach for grain analysis, which is based on the electron backscatter diffraction (EBSD) technique. Further, the challenges faced by the traditional machine learning model for classifying microstructures in Kerr micrographs are discussed

Mesh adaptivity for quasi-static phase-field fractures based on a residual-type a posteriori error estimator

In this work, we consider adaptive mesh refinement for a monolithic phase-field description for fractures in brittle materials. Our approach is based on an a posteriori error estimator for the phase-field variational inequality realizing the fracture irreversibility constraint. The key goal is the development of a reliable and efficient residual-type error estimator for the phase-field fracture model in each time-step. Based on this error estimator, error indicators for local mesh adaptivity are extracted. The proposed estimator is based on a technique known for singularly perturbed equations in combination with estimators for variational inequalities. These theoretical developments are used to formulate an adaptive mesh refinement algorithm. For the numerical solution, the fracture irreversibility is imposed using a Lagrange multiplier. The resulting saddle-point system has three unknowns: displacements, phase-field, and a Lagrange multiplier for the crack irreversibility. Several numerical experiments demonstrate our theoretical findings with the newly developed estimators and the corresponding refinement strategy.Comment: This is the preprint version of an accepted article to be published in the GAMM-Mitteilungen 2019. https://onlinelibrary.wiley.com/journal/1522260

A Damping of the de Haas-van Alphen Oscillations in the superconducting state

Deploying a recently developed semiclassical theory of quasiparticles in the superconducting state we study the de Haas-van Alphen effect. We find that the oscillations have the same frequency as in the normal state but their amplitude is reduced. We find an analytic formulae for this damping which is due to tunnelling between semiclassical quasiparticle orbits comprising both particle-like and hole-like segments. The quantitative predictions of the theory are consistent with the available data.Comment: 7 pages, 5 figure

Local Anisotropy of Fluids using Minkowski Tensors

Statistics of the free volume available to individual particles have previously been studied for simple and complex fluids, granular matter, amorphous solids, and structural glasses. Minkowski tensors provide a set of shape measures that are based on strong mathematical theorems and easily computed for polygonal and polyhedral bodies such as free volume cells (Voronoi cells). They characterize the local structure beyond the two-point correlation function and are suitable to define indices $0\leq \beta_\nu^{a,b}\leq 1$ of local anisotropy. Here, we analyze the statistics of Minkowski tensors for configurations of simple liquid models, including the ideal gas (Poisson point process), the hard disks and hard spheres ensemble, and the Lennard-Jones fluid. We show that Minkowski tensors provide a robust characterization of local anisotropy, which ranges from $\beta_\nu^{a,b}\approx 0.3$ for vapor phases to $\beta_\nu^{a,b}\to 1$ for ordered solids. We find that for fluids, local anisotropy decreases monotonously with increasing free volume and randomness of particle positions. Furthermore, the local anisotropy indices $\beta_\nu^{a,b}$ are sensitive to structural transitions in these simple fluids, as has been previously shown in granular systems for the transition from loose to jammed bead packs

Growth and Geographic Variation in Hospitalizations with Resistant Infections, United States, 2000–2005

From 2000 through 2005, hospitalizations with resistant infections (methicillin-resistant Staphylococcus aureus, Clostridium difficile–associated disease, vancomycin-resistant enterococcus, Pseudomonas aeruginosa, and Candida infection) nearly doubled, from 499,702 to 947,393. Regional variations noted in the aggregate and by individual infection may help clarify modifiable risk factors driving these infections