The Force Balance of Electrons During Kinetic Anti-parallel Magnetic Reconnection

Fully kinetic simulations are applied to the study of 2D anti-parallel reconnection, elucidating the dynamics by which the electron fluid maintains force balance within both the electron diffusion region (EDR) and the ion diffusion region (IDR). Inside the IDR, magnetic field-aligned electron pressure anisotropy ($p_{e\parallel}\gg p_{e\perp})$ develops upstream of the EDR. Compared to previous investigations, the use of modern computer facilities allows for simulations at the natural proton to electron mass ratio $m_i/m_e=1836$. In this high-$m_i/m_e$-limit the electron dynamics changes qualitatively, as the electron inflow to the EDR is enhanced and mainly driven by the anisotropic pressure. Using a coordinate system with the $x$-direction aligned with the reconnecting magnetic field and the $y$-direction aligned with the central current layer, it is well-known that for the much studied 2D laminar anti-parallel and symmetric scenario the reconnection electric field at the $X$-line must be balanced by the $\partial p_{exy}/ \partial x$ and $\partial p_{eyz}/ \partial z$ off-diagonal electron pressure stress components. We find that the electron anisotropy upstream of the EDR imposes large values of $\partial p_{exy}/ \partial x$ within the EDR, and along the direction of the reconnection $X$-line this stress cancels with the stress of a previously determined theoretical form for $\partial p_{eyz}/ \partial z$. The electron frozen-in law is instead broken by pressure tensor gradients related to the direct heating of the electrons by the reconnection electric field. The reconnection rate is free to adjust to the value imposed externally by the plasma dynamics at larger scales.Comment: Submitted to Physics of Plasmas, 11 October 202

Spin Relaxation in Graphene with self-assembled Cobalt Porphyrin Molecules

In graphene spintronics, interaction of localized magnetic moments with the electron spins paves a new way to explore the underlying spin relaxation mechanism. A self-assembled layer of organic cobalt-porphyrin (CoPP) molecules on graphene provides a desired platform for such studies via the magnetic moments of porphyrin-bound cobalt atoms. In this work a study of spin transport properties of graphene spin-valve devices functionalized with such CoPP molecules as a function of temperature via non-local spin-valve and Hanle spin precession measurements is reported. For the functionalized (molecular) devices, we observe a slight decrease in the spin relaxation time ({\tau}s), which could be an indication of enhanced spin-flip scattering of the electron spins in graphene in the presence of the molecular magnetic moments. The effect of the molecular layer is masked for low quality samples (low mobility), possibly due to dominance of Elliot-Yafet (EY) type spin relaxation mechanisms

Co-ordinate Control for Fuel Cell And Photovoltaic Cell

The usual natural fuel energy resources such as petroleum, natural gas, and coal are getting shortage rapidly by fulfilling the high demand of the energy sector in the world. Also, affect the environment and leads to the greenhouse effect and serious pollution problem. Therefore renewable energy sources like solar, wind, tidal etc. are gaining more attention as an alternative energy. The hierarchical structure of the convention power system is experiencing a paradigm shift into a deregulated system. As a result many small generators are been connected to the system at the distribution level. In order to supply a reliable and sustainable power to select customers it becomes mandatory to connect renewable and dispatchable sources. This would result in a hybrid system which can operate in autonomous or non-autonomous mode. This project aims at analysing the performance of one such autonomous system for varying demand. PV and fuel cell sources are considered to form the hybrid system under concern. An overall coordinated controller has been analysed to ensure power sharing among the different sources used in the hybrid system also incorporating variations in the solar irradiations. Also a simulation for generation of pulses for appropriate switching of control between the different sources through MATLAB has been attempted in this work

Nonlinear analog spintronics with van der Waals heterostructures

The current generation of spintronic devices, which use electron-spin relies on linear operations for spin-injection, transport and detection processes. The existence of nonlinearity in a spintronic device is indispensable for spin-based complex signal processing operations. Here we for the first time demonstrate the presence of electron-spin dependent nonlinearity in a spintronic device, and measure up to 4th harmonic spin-signals via nonlocal spin-valve and Hanle spin-precession measurements. We demonstrate its application for analog signal processing over pure spin-signals such as amplitude modulation and heterodyne detection operations which require nonlinearity as an essential element. Furthermore, we show that the presence of nonlinearity in the spin-signal has an amplifying effect on the energy-dependent conductivity induced nonlinear spin-to-charge conversion effect. The interaction of the two spin-dependent nonlinear effects in the spin transport channel leads to a highly efficient detection of the spin-signal without using ferromagnets. These effects are measured both at 4K and room temperature, and are suitable for their applications as nonlinear circuit elements in the fields of advanced-spintronics and spin-based neuromorphic computing.Comment: 14 pages, 8 figure

Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning

Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed because military decision-makers must be able to justify their decisions. Adversarial attacks arise because many ML algorithms are very sensitive to certain kinds of input perturbations. In this paper, we argue that these two issues are conceptually linked, and insights in one can provide insights in the other. We illustrate these ideas with relevant examples from the literature and our own experiments

Thermoelectric spin voltage in graphene

In recent years, new spin-dependent thermal effects have been discovered in ferromagnets, stimulating a growing interest in spin caloritronics, a field that exploits the interaction between spin and heat currents. Amongst the most intriguing phenomena is the spin Seebeck effect, in which a thermal gradient gives rise to spin currents that are detected through the inverse spin Hall effect. Non-magnetic materials such as graphene are also relevant for spin caloritronics, thanks to efficient spin transport, energy-dependent carrier mobility and unique density of states. Here, we propose and demonstrate that a carrier thermal gradient in a graphene lateral spin valve can lead to a large increase of the spin voltage near to the graphene charge neutrality point. Such an increase results from a thermoelectric spin voltage, which is analogous to the voltage in a thermocouple and that can be enhanced by the presence of hot carriers generated by an applied current. These results could prove crucial to drive graphene spintronic devices and, in particular, to sustain pure spin signals with thermal gradients and to tune the remote spin accumulation by varying the spin-injection bias