ALBERT

Beschreibung: Boron Nitride (BN) is a two dimensional insulator with excellent chemical, thermal, mechanical, and optical properties, which make it especially attractive for logic device applications. Nevertheless, its insulating properties and reliability as a dielectric material have never been analyzed in-depth. Here, we present the first thorough characterization of BN as dielectric film using nanoscale and device level experiments complementing with theoretical study. Our results reveal that BN is extremely stable against voltage stress, and it does not show the reliability problems related to conventional dielectrics like HfO 2 , such as charge trapping and detrapping, stress induced leakage current, and untimely dielectric breakdown. Moreover, we observe a unique layer-by-layer dielectric breakdown, both at the nanoscale and device level. These findings may be of interest for many materials scientists and could open a new pathway towards two dimensional logic device applications.

Beschreibung: Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R2) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_RNSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_RNSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.

Beschreibung: A virtual synchronous machine (VSM) is a converter which, compared to other types of converters, has more friendly interactions with the power grid because it is able to simulate the external characteristics of a synchronous machine, which can provide virtual inertia and damping. When the grid voltage is unbalanced, there will be negative sequence current and power oscillations. There will also be double-frequency ripples on the DC bus, which affect the normal operation of the DC power source or load. In order to solve these problems, a comprehensive control strategy is proposed in this paper. The principle of a VSM operated as a current source converter, also called VISMA, is used in the design. A complex coefficient filter is applied to separate the positive and negative sequence components of the grid voltage. By analyzing the reasons of power oscillations under unbalanced voltage, the electrical simulation part of the VSM is improved to achieve several objectives: to suppress negative sequence current and DC voltage ripples. Additionally, the rated voltage in the reactive control part is adaptively adjusted to stabilize the system. The validity of the proposed control strategy is verified by simulation and experiment.

Beschreibung: The 1-km Institute of Urban Meteorology (IUM) operational model has a high temperature bias, especially at night, and a high wind speed bias in urbanized areas, limiting the ability of IUM to provide accurate, high-resolution prediction of thermal stress and air quality for the densely populated Beijing-Tianjin metro region. This study provides an assessment of the IUM WRF-based operational model setups and performs a diagnostic analysis to isolate the contributions of model physics parameterization schemes to operational forecast bias over complex urban regions. Results show that non-TKE PBL schemes perform better than their counterpart TKE-based schemes at night, reducing the warm bias by about 1 °C in non-urban areas. However, the best performing urban PBL scheme still produces ~ 2 °C warm bias. Considering aerosol effects in the solar radiation scheme improves downward solar radiation and surface energy budgets, but has negligible effect on the simulated temperature. Urban canopy models and the specification of various urban model parameters have comparable or even more significant contributions to forecast biases in temperature and wind speed than PBL schemes. The predicted PBL height using an optimized urban-parameter table is lower by about 100 – 200 meters, which is about 50 – 100% of the inter-urban scheme effect on the PBL height. Overall, the Building Effect Parameterization urban scheme with the default parameter table, or a parameter table with less urban heat storage, is recommended for the best results in urban areas and shows that most of the urban areas of Beijing and Tianjin have a greater than 4 °C improvement in absolute temperature bias and more than 1 m s -1 improvement in absolute wind speed bias.

Beschreibung: ABSTRACT Model simulations of the boundary-layer structures and summer rainfall in cities are heavily affected by urban morphology, which are defined by a number of urban canopy parameters (UCPs). In the default Weather Research Forecasting (WRF) model/urban canopy model (UCM) system, UCPs are specified in a lookup table as function of three different urban land-use types, which cannot totally capture the heterogeneity in building heights (BHs). In this article, a method of using a fractal dimension (FD) was developed to define new categories of urban BH for characterizing the vertical urban canopy morphology. Combined with the default three urban land-use categories in WRF/UCM system, the BH categorization improves the description of the urban canopy characteristics. WRF/UCM multi-day simulations showed that the new method produced lower air temperatures and higher 10-m wind speeds in densely urbanized areas in Beijing, and performed better in capturing the temperature variations and spatial distributions of the 10-m wind speed. A rainfall case was also simulated by WRF/UCM using the two methods, and the new BH method yielded a more accurate simulation of precipitation. The difference of 24-h total precipitation between WRF simulations using the two methods can reach 40 mm, indicating a high sensitivity to urban surface representations. Results showed that the introduction of FD-based BH categorization is a simple and yet efficient way to improve WRF/UCM performance in the areas where detailed information on BHs is not available.

Beschreibung: For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.