Code verification examples of a fully geometrical nonlinear membrane element using the method of manufactured solutions

This paper presents an effective method to perform Code Verification of a software which is designed for structural analysis using membranes. The focus lies on initially curved structures with large deformations in steady and unsteady regimes. The material is assumed to be linear elastic isotropic. Code Verification is a part of efforts to guarantee the code’s correctness and to obtain finally predictive capability of the code. The Method of Manufactured Solutions turned out to be an effective tool to perform Code Verification, especially for initially curved structures. Here arbitrary invented geometries and analytical solutions are chosen. The computer code must approach this solution asymptotically. The observed error reduction with systematic mesh refinement (i.e. observed order of accuracy) must be in the range of the formal order of accuracy, e.g. derived by a Taylor series expansion. If these two orders match in the asymptotic range, the implemented numerical algorithms are working as intended. The given examples provide a complete hierarchical benchmark suite for the reader to assess other codes, too. In the present case several membrane states were tested successfully and the used code Carat++ assessed to converge - as intended - second order accurately in space and time for all kind of shapes and solution

DEEP LEARNING-BASED OBSTACLE DETECTION AND DISTANCE ESTIMATION USING OBJECT BOUNDING BOX

In this paper, a system consisting of deep learning (DL)-based object detection followed by neural network based object distance estimation is considered. The accuracy of object distance estimation strongly depends on the size of the bounding box (BB) of the detected object extracted by the DL-based object detector. A method for improvement of the accuracy of object BB is proposed, which involves traditional computer vision-based edge segmentation of object BB image region. The proposed method is evaluated on the real-world images of railway scenes with obstacles on the rail tracks captured by thermal and RGB cameras. The evaluation results demonstrate the potential of traditional computer vision methods to complement state-of-the-art DL methods for accurate object detection and distance estimation

Real-space anisotropy of the superconducting gap in the charge-density wave material 2H-NbSe2

We present a scanning tunneling microscopy (STM) and ab-initio study of the anisotropic superconductivity of 2H-NbSe2 in the charge-density-wave (CDW) phase. Differential-conductance spectra show a clear double-peak structure, which is well reproduced by density functional theory simulations enabling full k- and real-space resolution of the superconducting gap. The hollow-centered (HC) and chalcogen-centered (CC) CDW patterns observed in the experiment are mapped onto separate van der Waals layers with different electronic properties. We identify the CC layer as the high-gap region responsible for the main STM peak. Remarkably, this region belongs to the same Fermi surface sheet that is broken by the CDW gap opening. Simulations reveal a highly anisotropic distribution of the superconducting gap within single Fermi sheets, setting aside the proposed scenario of a two-gap superconductivity. Our results point to a spatially localized competition between superconductivity and CDW involving the HC regions of the crystal

Scalable production of iPSC-derived human neurons to identify tau-lowering compounds by high-content screening

Lowering total tau levels is an attractive therapeutic strategy for Alzheimer's disease and other tauopathies. High-throughput screening in neurons derived from human induced pluripotent stem cells (iPSCs) is a powerful tool to identify tau-targeted therapeutics. However, such screens have been hampered by heterogeneous neuronal production, high cost and low yield, and multi-step differentiation procedures. We engineered an isogenic iPSC line that harbors an inducible neurogenin 2 transgene, a transcription factor that rapidly converts iPSCs to neurons, integrated at the AAVS1 locus. Using a simplified two-step protocol, we differentiated these iPSCs into cortical glutamatergic neurons with minimal well-to-well variability. We developed a robust high-content screening assay to identify tau-lowering compounds in LOPAC and identified adrenergic receptors agonists as a class of compounds that reduce endogenous human tau. These techniques enable the use of human neurons for high-throughput screening of drugs to treat neurodegenerative disease

The Advocate

Headlines Include: Laurels For Feerick: An Alumnus To Remember; Crime at Fordham; Who\u27s Next?, Film at 11https://ir.lawnet.fordham.edu/student_the_advocate/1007/thumbnail.jp

Original experimental data and code for the Paper ”Quantum spins and hybridization in artificially-constructed chains of magnetic adatoms on a superconductor“

Magnetic adatom chains on surfaces constitute fascinating quantum spin systems. Superconducting substrates suppress interactions with bulk electronic excitations but couple the adatom spins to a chain of subgap Yu-Shiba-Rusinov (YSR) quasiparticles. Using a scanning tunneling microscope, we investigate such correlated spin-fermion systems by constructing Fe chains adatom by adatom on superconducting NbSe2. The adatoms couple entirely via the substrate, retaining their quantum spin nature. In dimers, we observe that the deepest YSR state undergoes a quantum phase transition due to Ruderman-Kittel-Kasuya-Yosida interactions, a distinct signature of quantum spins. Chains exhibit coherent hybridization and band formation of the YSR excitations, indicating ferromagnetic coupling. Longer chains develop separate domains due to coexisting charge-density-wave order of NbSe2. Despite the spin-orbit-coupled substrate, we find no signatures of Majoranas, possibly because quantum spins reduce the parameter range for topological superconductivity. We suggest that adatom chains are versatile systems for investigating correlated-electron physics and its interplay with topological superconductivity

Quantum spins and hybridization in artificially-constructed chains of magnetic adatoms on a superconductor

Magnetic adatom chains on surfaces constitute fascinating quantum spin systems. Superconducting substrates suppress interactions with bulk electronic excitations but couple the adatom spins to a chain of subgap Yu-Shiba-Rusinov (YSR) quasiparticles. Using a scanning tunneling microscope, we investigate such correlated spin-fermion systems by constructing Fe chains adatom by adatom on superconducting NbSe$_2$. The adatoms couple entirely via the substrate, retaining their quantum spin nature. In dimers, we observe that the deepest YSR state undergoes a quantum phase transition due to Ruderman-Kittel-Kasuya-Yosida interactions, a distinct signature of quantum spins. Chains exhibit coherent hybridization and band formation of the YSR excitations, indicating ferromagnetic coupling. Longer chains develop separate domains due to coexisting charge-density-wave order of NbSe$_2$. Despite the spin-orbit-coupled substrate, we find no signatures of Majoranas, possibly because quantum spins reduce the parameter range for topological superconductivity. We suggest that adatom chains are versatile systems for investigating correlated-electron physics and its interplay with topological superconductivity

Yu–Shiba–Rusinov states in the charge-density modulated superconductor NbSe2

NbSe2 is a remarkable superconductor in which charge-density order coexists with pairing correlations at low temperatures. Here, we study the interplay of magnetic adatoms and their Yu–Shiba–Rusinov (YSR) bound states with the charge density order. Exploiting the incommensurate nature of the charge-density wave (CDW), our measurements provide a thorough picture of how the CDW affects both the energies and the wave functions of the YSR states. Key features of the dependence of the YSR states on adsorption site relative to the CDW are explained by model calculations. Several properties make NbSe2 a promising substrate for realizing topological nanostructures. Our results will be important in designing such systems