Probabilistic Surface Characterization for Safe Landing Hazard Detection and Avoidance (HDA)

Apparatuses, systems, computer programs and methods for performing hazard detection and avoidance for landing vehicles are provided. Hazard assessment takes into consideration the geometry of the lander. Safety probabilities are computed for a plurality of pixels in a digital elevation map. The safety probabilities are combined for pixels associated with one or more aim points and orientations. A worst case probability value is assigned to each of the one or more aim points and orientations

Spin-Orbit-Mediated Spin Relaxation in Graphene

We investigate how spins relax in intrinsic graphene. The spin-orbit coupling arises from the band structure and is enhanced by ripples. The orbital motion is influenced by scattering centers and ripple-induced gauge fields. Spin relaxation due to Elliot-Yafet and Dyakonov-Perel mechanisms and gauge fields in combination with spin-orbit coupling are discussed. In intrinsic graphene, the Dyakonov-Perel mechanism and spin flip due to gauge fields dominate and the spin-flip relaxation time is inversely proportional to the elastic scattering time. The spin relaxation anisotropy depends on an intricate competition between these mechanisms. Experimental consequences are discussed.Comment: Final published versio

Scattering theory of interface resistance in magnetic multilayers

The scattering theory of transport has to be applied with care in a diffuse environment. Here we discuss how the scattering matrices of heterointerfaces can be used to compute interface resistances of dirty magnetic multilayers. First principles calculations of these interface resistances agree well with experiments in the CPP (current perpendicular to the interface plane) configuration.Comment: submitted to J. Phys. D (special issue at the occasion of Prof. T. Shinjo's 60th birthday

Spin Torques in Ferromagnetic/Normal Metal Structures

Recent theories of spin-current-induced magnetization reversal are formulated in terms of a spin-mixing conductance $G^{mix}$. We evaluate $G^{mix}$ from first-principles for a number of (dis)ordered interfaces between magnetic and non-magnetic materials. In multi-terminal devices, the magnetization direction of a one side of a tunnel junction or a ferromagnetic insulator can ideally be switched with negligible charge current dissipation.Comment: 4 pages, 1 figur

Absolute spin-valve effect with superconducting proximity structures

We investigate spin dependent transport in hybrid superconductor(S)--normal-metal(N)--ferromagnet(F) structures under conditions of proximity effect. We demonstrate the feasibility of the absolute spin-valve effect for a certain interval of voltages in a system consisting of two coupled tri-layer structures. Our results are also valid for non-collinear magnetic configurations of the ferromagnets.Comment: 1 TEX file, 2 Postscript files. Accepted for publication in Physical Review Letter

Spin battery operated by ferromagnetic resonance

Precessing ferromagnets are predicted to inject a spin current into adjacent conductors via Ohmic contacts, irrespective of a conductance mismatch with, for example, doped semiconductors. This opens the way to create a pure spin source spin battery by the ferromagnetic resonance. We estimate the spin current and spin bias for different material combinations.Comment: The estimate for the magnitude of the spin bias is improved. We find that it is feasible to get a measurable signal of the order of the microwave frequency already for moderate rf intensitie

Occupational risk of nano-biomaterials: Assessment of nano-enabled magnetite contrast agent using the BIORIMA Decision Support System

The assessment of the safety of nano-biomedical products for patients is an essential prerequisite for their market authorization. However, it is also required to ensure the safety of the workers who may be unintentionally exposed to the nano-biomaterials (NBMs) in these medical applications during their synthesis, formulation into products and end-of-life processing and also of the medical professionals (e.g., nurses, doctors, dentists) using the products for treating patients. There is only a handful of workplace risk assessments focussing on NBMs used in medical applications. Our goal is to contribute to increasing the knowledge in this area by assessing the occupational risks of magnetite (Fe3O4) nanoparticles coated with PLGA-b-PEG-COOH used as contrast agent in magnetic resonance imaging (MRI) by applying the software-based Decision Support System (DSS) which was developed in the EU H2020 project BIORIMA. The occupational risk assessment was performed according to regulatory requirements and using state-of-the-art models for hazard and exposure assessment, which are part of the DSS. Exposure scenarios for each life cycle stage were developed using data from literature, inputs from partnering industries and results of a questionnaire distributed to healthcare professionals, i.e., physicians, nurses, technicians working with contrast agents for MRI. Exposure concentrations were obtained either from predictive exposure models or monitoring campaigns designed specifically for this study. Derived No-Effect Levels (DNELs) were calculated by means of the APROBA tool starting from in vivo hazard data from literature. The exposure estimates/measurements and the DNELs were used to perform probabilistic risk characterisation for the formulated exposure scenarios, including uncertainty analysis. The obtained results revealed negligible risks for workers along the life cycle of magnetite NBMs used as contrast agent for the diagnosis of tumour cells in all exposure scenarios except in one when risk is considered acceptable after the adoption of specific risk management measures. The study also demonstrated the added value of using the BIORIMA DSS for quantification and communication of occupational risks of nano-biomedical applications and the associated uncertainties

A post-merger enhancement only in star-forming Type 2 Seyfert galaxies:the deep learning view

Supermassive black holes require a reservoir of cold gas at the centre of their host galaxy in order to accrete and shine as active galactic nuclei (AGN). Major mergers have the ability to drive gas rapidly inwards, but observations trying to link mergers with AGN have found mixed results due to the difficulty of consistently identifying galaxy mergers in surveys. This study applies deep learning to this problem, using convolutional neural networks trained to identify simulated post-merger galaxies from survey-realistic imaging. This provides a fast and repeatable alternative to human visual inspection. Using this tool, we examine a sample of ~8500 Seyfert 2 galaxies (L[OIII] ~ $10^{38.5 - 42}$ erg/s) at z < 0.3 in the Sloan Digital Sky Survey and find a merger fraction of $2.19_{-0.17}^{+0.21}$% compared with inactive control galaxies, in which we find a merger fraction of $2.96_{-0.20}^{+0.26}$%, indicating an overall lack of mergers among AGN hosts compared with controls. However, matching the controls to the AGN hosts in stellar mass and star formation rate reveals that AGN hosts in the star-forming blue cloud exhibit a ~$2\times$ merger enhancement over controls, while those in the quiescent red sequence have significantly lower relative merger fractions, leading to the observed overall deficit due to the differing $M_{\ast} -$SFR distributions. We conclude that while mergers are not the dominant trigger of all low-luminosity, obscured AGN activity in the nearby Universe, they are more important to AGN fuelling in galaxies with higher cold gas mass fractions as traced through star formation