Mining the in-use stock of energy-transition materials for closed-loop e-mobility

The decarbonization of transportation is essential to achieve a carbon neutral planetary society. However, the turn to electromobility is based on advanced technologies (e.g., lithium-ions batteries) that tied our development to many functional materials with problematic supply. In this study, we apply prospective dynamic material flow analysis to explore the potentials for closing material cycles while meeting a full transition to electric for a set of energy-transition materials (ETMs) including lithium, cobalt, nickel, manganese, and natural graphite. Three demand scenarios are applied to develop trajectories for ETM demand, their in-use stock, and derive the potentials to which recycling can substitute for virgin material extraction at the global scale to 2065. Our results estimate that ETM inflow to use could increase between 20 and 50 times by 2065. However, secondary supply will hardly enable the achievement of circularity in material cycles in the next decades so that the supply of ETMs will remain mainly based on primary material extraction. Nevertheless, from 2040 onwards, recycling volumes could meet up to more than 80% of demand and represent a viable alternative to mining. If the ideal scenario is realized, government policies could have the potential for achieving the dual goal of decarbonizing e-mobility and securing sustainable access to ETMs already in the middle of 2050s. However, the combined commitment and efforts across the value chain of policymakers, companies involved in the cycle, and consumers will be needed to fully realize the great potential for circular economy to work for e-mobility

Validity of the Hadronic Freeze-Out Curve

We analyze hadro-chemical freeze-out in central Pb+Pb collisions at CERN SPS energies, employing the hybrid version of UrQMD which models hadronization by the Cooper-Frye mechanism, and matches to a final hadron-resonance cascade. We fit the results both before and after the cascade stage using the Statistical Hadronization Model, to assess the effect of the cascade phase. We observe a strong effect on antibaryon yields except anti-{\Omega}, resulting in a shift in T and {\mu}_B. We discuss the implications for the freeze-out curve.Comment: 5 pages, 8 figures. To appear in the proceedings of Quark Matter 2011, the XXII International Conference on Ultrarelativistic Nucleus-Nucleus Collision

Do columnar defects produce bulk pinning?

From magneto-optical imaging performed on heavy-ion irradiated YBaCuO single crystals, it is found that at fields and temperatures where strong single vortex pinning by individual irradiation-induced amorphous columnar defects is to be expected, vortex motion is limited by the nucleation of vortex kinks at the specimen surface rather than by half-loop nucleation in the bulk. In the material bulk, vortex motion occurs through (easy) kink sliding. Depinning in the bulk determines the screening current only at fields comparable to or larger than the matching field, at which the majority of moving vortices is not trapped by an ion track.Comment: 5 pages, 5 figures, submitted to Physical Review Letter

High-Fidelity Readout in Circuit Quantum Electrodynamics Using the Jaynes-Cummings Nonlinearity

We demonstrate a qubit readout scheme that exploits the Jaynes-Cummings nonlinearity of a superconducting cavity coupled to transmon qubits. We find that in the strongly-driven dispersive regime of this system, there is the unexpected onset of a high-transmission "bright" state at a critical power which depends sensitively on the initial qubit state. A simple and robust measurement protocol exploiting this effect achieves a single-shot fidelity of 87% using a conventional sample design and experimental setup, and at least 61% fidelity to joint correlations of three qubits.Comment: 5 pages, 4 figure

Towards Interpretable Deep Learning Models for Knowledge Tracing

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain

New Low-Mass Stars and Brown Dwarfs with Disks in Lupus

Using the Infrared Array Camera and the Multiband Imaging Photometer aboard the {\it Spitzer Space Telescope}, we have obtained images of the Lupus 3 star-forming cloud at 3.6, 4.5, 5.8, 8.0, and 24 \micron. We present photometry in these bands for the 41 previously known members that are within our images. In addition, we have identified 19 possible new members of the cloud based on red 3.6-8.0 \micron colors that are indicative of circumstellar disks. We have performed optical spectroscopy on 6 of these candidates, all of which are confirmed as young low-mass members of Lupus 3. The spectral types of these new members range from M4.75 to M8, corresponding to masses of 0.2-0.03 $M_\odot$ for ages of $\sim1$ Myr according to theoretical evolutionary models. We also present optical spectroscopy of a candidate disk-bearing object in the vicinity of the Lupus 1 cloud, 2M 1541-3345, which Jayawardhana & Ivanov recently classified as a young brown dwarf ($M\sim0.03$ $M_\odot$) with a spectral type of M8. In contrast to their results, we measure an earlier spectral type of M5.75$\pm$0.25 for this object, indicating that it is probably a low-mass star ($M\sim0.1$ $M_\odot$). In fact, according to its gravity-sensitive absorption lines and its luminosity, 2M 1541-3345 is older than members of the Lupus clouds ($\tau\sim1$ Myr) and instead is probably a more evolved pre-main-sequence star that is not directly related to the current generation of star formation in Lupus.Comment: 18 pages, 3 tables, 6 figure

Determination of the crystal structure of CuSnTi by full profile Rietveld analysis

The crystal structure of the new ternary phase CuSnTi is determined by full profile Rietveld analysis of the powder diffractogram. 104 reflections were refined to a final RBragg value of 5.60%. CuSnTi crystallizes with the spacegroup P63/mmc and is isostructural to InNi2. The lattice parameters are a=0.439 555(5) nm and c=0.601 505(9) n

Comparison of embedded and added motor imagery training in patients after stroke: Results of a randomised controlled pilot trial

Copyright @ 2012 Schuster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Motor imagery (MI) when combined with physiotherapy can offer functional benefits after stroke. Two MI integration strategies exist: added and embedded MI. Both approaches were compared when learning a complex motor task (MT): ‘Going down, laying on the floor, and getting up again’. Methods: Outpatients after first stroke participated in a single-blinded, randomised controlled trial with MI embedded into physiotherapy (EG1), MI added to physiotherapy (EG2), and a control group (CG). All groups participated in six physiotherapy sessions. Primary study outcome was time (sec) to perform the motor task at pre and post-intervention. Secondary outcomes: level of help needed, stages of MT-completion, independence, balance, fear of falling (FOF), MI ability. Data were collected four times: twice during one week baseline phase (BL, T0), following the two week intervention (T1), after a two week follow-up (FU). Analysis of variance was performed. Results: Thirty nine outpatients were included (12 females, age: 63.4 ± 10 years; time since stroke: 3.5 ± 2 years; 29 with an ischemic event). All were able to complete the motor task using the standardised 7-step procedure and reduced FOF at T0, T1, and FU. Times to perform the MT at baseline were 44.2 ± 22s, 64.6 ± 50s, and 118.3 ± 93s for EG1 (N = 13), EG2 (N = 12), and CG (N = 14). All groups showed significant improvement in time to complete the MT (p < 0.001) and degree of help needed to perform the task: minimal assistance to supervision (CG) and independent performance (EG1+2). No between group differences were found. Only EG1 demonstrated changes in MI ability over time with the visual indicator increasing from T0 to T1 and decreasing from T1 to FU. The kinaesthetic indicator increased from T1 to FU. Patients indicated to value the MI training and continued using MI for other difficult-to-perform tasks. Conclusions: Embedded or added MI training combined with physiotherapy seem to be feasible and benefi-cial to learn the MT with emphasis on getting up independently. Based on their baseline level CG had the highest potential to improve outcomes. A patient study with 35 patients per group could give a conclusive answer of a superior MI integration strategy.The research project was partially funded by the Gottfried und Julia Bangerter-Rhyner Foundation