Growth of Large Domain Epitaxial Graphene on the C-Face of SiC

Growth of epitaxial graphene on the C-face of SiC has been investigated. Using a confinement controlled sublimation (CCS) method, we have achieved well controlled growth and been able to observe propagation of uniform monolayer graphene. Surface patterns uncover two important aspects of the growth, i.e. carbon diffusion and stoichiometric requirement. Moreover, a new "stepdown" growth mode has been discovered. Via this mode, monolayer graphene domains can have an area of hundreds of square micrometers, while, most importantly, step bunching is avoided and the initial uniformly stepped SiC surface is preserved. The stepdown growth provides a possible route towards uniform epitaxial graphene in wafer size without compromising the initial flat surface morphology of SiC.Comment: 18 pages, 8 figure

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances

Soil nitrogen and carbon storages and carbon pool management index under sustainable conservation tillage strategy

Agricultural practices are significant to increase the soil nitrogen and organic carbon sequestration to adapt and mitigate the climate change in a recent climate change scenario. With this background, we carried out research in the Longzhong Loess Plateau region of China. This research was conducted under a randomized complete block design, with three replicates. Adopt the method of combining outdoor positioning field test with indoor index measurement to explore the soil bulk density (BD), nitrogen components (viz., nitrate nitrogen (NO3−-N), ammonia nitrogen (NH4+-N), total nitrogen (TN), microbial biomass nitrogen (MBN) and nitrogen storage (NS), and carbon components [viz., soil organic carbon (SOC), easily oxidized organic carbon (EOC), microbial biomass carbon (MBC) and carbon storage (CS), carbon pool index (CPI), carbon pool activity (A) and carbon pool activity index (AI) and carbon pool management index (CPMI)] and C/N, ratio under different tillage practices [namely., conventional tillage (CT), no tillage (NT), straw mulch with conventional tillage (CTS) and straw mulch with no tillage (NTS)]. Our results depicted that different conservation tillage systems significantly increased soil BD over conventional tillage. Compared with CT, the NTS, CTS and NT reduced soil NO3−-N, increased the soil NH4+-N, TN, MBN and NS, among them, NS under NTS, CTS and NT treatment was 25.0, 14.8 and 13.1% higher than that under CT treatment, respectively. Additionally, conservation tillage significantly increased SOC, EOC, MBC, CS, CPI, AI, CPMI and C/N, ratio than CT. Inside, CS under NTS, CTS and NT treatment was 19.4, 12.1 and 13.4% higher than that under CT treatment, respectively. Moreover, during the 3-year study period, the CPMI under NTS treatment was the largest (139.26, 140.97, and 166.17). Consequently, we suggest that NTS treatment was more sustainable strategy over other investigated conservation tillage practices and should be recommended as climate mitigation technique under climate change context

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

A High Isolation MIMO Antenna without Decoupling Structure for LTE 700 MHz

This paper presents a long-term evolution (LTE) 700 MHz band multiple-input-multiple-output (MIMO) antenna, and high isolation between the two symmetrical antenna elements is obtained without introducing extra decoupling structure. Each antenna element is a combination antenna of PIFA and a meander monopole antenna. The end of the PIFA and the meander monopole antenna are, respectively, overlapped with the 50 Ω microstrip feed line, the two overlapping areas produce additional capacitance which can be considered decoupling structures to enhance the isolation for the MIMO antenna, as well as the impedance matching of the antenna elements. The MIMO antenna is etched on FR4 PCB board with dimensions of 71 × 40 × 1.6 mm3; the edge-to-edge separation of the two antenna elements is only nearly 0.037 λ at 700 MHz. Both simulation and measurement results are used to confirm the MIMO antenna performance; the operating bandwidth is 698–750 MHz with S11≤−6 dB and S21≤−23 dB