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AC losses in conductor-on-rounded-core (CORC) cables of YBCO high-temperature superconducting (HTS) tapes are a significant challenge in HTS power applications. This study employs two finite element analysis (FEA) models to investigate the contributions from different AC loss components and provide approaches for reducing AC losses in cables. An FEA model based on T-A formula treats the cross-section of thin superconducting layers as 1D lines and, therefore, only can predict the AC loss generated by the perpendicular magnetic field. In contrast, the model based on H-formulation can be performed on the actual 2D rectangular cross-section HTS tapes to provide the total AC losses generated by magnetic fluxes penetrating from both the edges and surfaces of HTS tapes, although this model requires more computing time and memory. Both 1D and 2D simulation approaches were employed to offer a comprehensive understanding of the effects of cable design and operational parameters on the AC loss components in a 2-layer CORC cable. The research results given in this paper are therefore not only valuable to suggest strategies for reducing AC loss in multi-layer cables but also for developing more accurate and effective methods to calculate AC loss in CORC HTS cables.

There has been a recent surge of interest in UTe$_2$ due to its unconventional magnetic field (H) reinforced spin-triplet superconducting phases persisting at fields far above the simple Pauli limit for H $\parallel$ [010]. Magnetic fields in excess of 35 T then induce a field-polarized magnetic state via a first-order-like phase transition. More controversially, for field orientations close to H $\parallel$ [011] and above 40 T, electrical resistivity measurements suggest that a further superconducting state may exist. However, no Meissner effect or thermodynamic evidence exists to date for this phase making it difficult to exclude a simple low-resistance metallic state. In this paper, we describe a study using thermal, electrical, and magnetic probes in magnetic fields of up to 55 T applied between the [010] ($b$) and [001] ($c$) directions. Our MHz conductivity data reveal the field-induced state of low or vanishing electrical resistance; simultaneous magnetocaloric effect measurements (i.e. changes in sample temperature due to changing magnetic field), show the first definitive evidence for adiabaticity and thermal behavior characteristic of bulk field-induced superconductivity.

Extreme pressures and high magnetic fields can affect materials in profound and fascinating ways. However, large pressures and fields are often mutually incompatible; the rapidly changing fields provided by pulsed magnets induce eddy currents in the metallic components used in conventional pressure cells, causing serious heating, forces and vibration. Here we report a diamond-anvil-cell made mainly out of insulating composites that minimizes inductive heating while retaining sufficient strength to apply pressures of up to 9 GPa. Any residual metallic components are made of low-conductivity metals and patterned to reduce eddy currents. The simple design enables rapid sample or pressure changes, desired by pulsed-magnetic-field-facility users. The pressure cell has been used in pulsed magnetic fields of up to 65 T with no noticeable heating at cryogenic temperatures. Several measurement techniques are possible inside the cell at temperatures as low as 500 mK.

La(Fe,Si)$_{13}$-based compounds are considered to be very promising magnetocaloric materials for magnetic refrigeration applications. Many studies have focused on this material family but only in bulk form. In this paper, we report the fabrication of thick films of La(Fe,Si)$_{13}$, both with and without post-hydriding. These films exhibit magnetic and structural properties comparable to those of bulk materials. We also observe that the ferromagnetic phase transition has a negative thermal hysteresis, a phenomenon not previously found in this material but which may have its origins in the availability of a strain energy reservoir, as in the cases of other materials in which negative thermal hysteresis has been found. Here, it appears that the substrate acts to store strain energy. Our exploratory study demonstrates the viability of thick films of the La(Fe,Si)$_{13}$ phase and motivates further work in the area, while showing that additional perspectives can be gained from reducing the dimensionality of magnetocaloric materials in which the magneto-volume effect is large., Comment: 16 pages, 3 figures

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks., Comment: Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability., Comment: Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper

Numerical modeling of superconductors is widely recognized as a powerful tool for interpreting experimental results, understanding physical mechanisms and predicting the performance of high-temperature superconductor (HTS) tapes, wires and devices. This is especially true for ac loss calculation, since a sufficiently low ac loss value is imperative to make these materials attractive for commercialization. In recent years, a large variety of numerical models, based on different techniques and implementations, have been proposed by researchers around the world, with the purpose of being able to estimate ac losses in HTSs quickly and accurately. This article presents a literature review of the methods for computing ac losses in HTS tapes, wires and devices. Technical superconductors have a relatively complex geometry (filaments, which might be twisted or transposed, or layers) and consist of different materials. As a result, different loss contributions exist. In this paper, we describe the ways of computing such loss contributions, which include hysteresis losses, eddy current losses, coupling losses, and losses in ferromagnetic materials. We also provide an estimation of the losses occurring in a variety of power applications., Comment: IEEE Transactions on Applied Superconductivity, 2013