Parameterization of the unresolved vertical transport in the planetary boundary layer (PBL) is one of the key physics algorithms in atmospheric models. This study attempts to represent the ...subgrid-scale (SGS) turbulent transport in convective boundary layers (CBLs) at gray-zone resolutions by investigating the effects of grid-size dependency in the vertical heat transport parameterization for CBL simulations. The SGS transport profile is parameterized based on the 2013 conceptual derivation by Shin and Hong. First, nonlocal transport via strong updrafts and local transport via the remaining small-scale eddies are separately calculated. Second, the SGS nonlocal transport is formulated by multiplying a grid-size dependency function with the total nonlocal transport profile fit to the large-eddy simulation (LES) output. Finally, the SGS local transport is formulated by multiplying a grid-size dependency function with the total local transport profile, which is calculated using an eddy-diffusivity formula. The new algorithm is evaluated against the LES output and compared with a conventional nonlocal PBL parameterization. For ideal CBL cases, by considering the scale dependency in the parameterized vertical heat transport, improvements over the conventional nonlocal K-profile model appear in mean profiles, resolved and SGS vertical transport profiles with their grid-size dependency, and the energy spectrum. Real-case simulations for convective rolls show that the simulated roll structures are more robust with stronger intensity when the new algorithm is used.
Abstract
A method that enables a mass-flux cumulus parameterization scheme (CPS) to work seamlessly in various model grids across CPS gray-zone resolutions is proposed. The convective cloud-base mass ...flux, convective inhibition, and convective detrainment in the simplified Arakawa–Schubert (SAS) scheme are modified to be functions of the convective updraft fraction. The combination of two updraft fractions is used to modulate the cloud-base mass flux; the first one depends on the horizontal grid space and the other is a function of the grid-scale and convective vertical velocity. The convective inhibition and detrainment of hydrometeors are also modified to be a function of the grid-size-dependent convective updraft fraction.
A set of sensitivity experiments with the Weather Research and Forecasting (WRF) Model is conducted for a heavy rainfall case over South Korea. The results show that the revised SAS CPS outperforms the original SAS. At 3 and 1 km, the precipitation core over South Korea is well reproduced by the experiments with the revised SAS scheme. On the contrary, the simulated precipitation is widespread in the case of the original SAS experiment and there are multiple spurious cores when the CPS is removed at those resolutions. The modified mass flux at the cloud base is found to play a major role in organizing the grid-scale precipitation at the convective core. A 1-month simulation at 3 km confirms that the revised scheme produces slightly better summer monsoonal precipitation results as compared to the typical model setup without CPS.
Improving the capacitance of carbon materials for supercapacitors without sacrificing their rate performance, especially volumetric capacitance at high mass loadings, is a big challenge because of ...the limited assessable surface area and sluggish electrochemical kinetics of the pseudocapacitive reactions. Here, it is demonstrated that “self‐doping” defects in carbon materials can contribute to additional capacitance with an electrical double‐layer behavior, thus promoting a significant increase in the specific capacitance. As an exemplification, a novel defect‐enriched graphene block with a low specific surface area of 29.7 m2 g−1 and high packing density of 0.917 g cm−3 performs high gravimetric, volumetric, and areal capacitances of 235 F g−1, 215 F cm−3, and 3.95 F cm−2 (mass loading of 22 mg cm−2) at 1 A g−1, respectively, as well as outstanding rate performance. The resulting specific areal capacitance reaches an ultrahigh value of 7.91 F m−2 including a “self‐doping” defect contribution of 4.81 F m−2, which is dramatically higher than the theoretical capacitance of graphene (0.21 F m−2) and most of the reported carbon‐based materials. Therefore, the defect engineering route broadens the avenue to further improve the capacitive performance of carbon materials, especially for compact energy storage under limited surface areas.
Owing to the significantly improved double‐layer capacitance originating from the “self‐doping” defects, defective graphene blocks with high defect density (ID/IG = 2.16), high packing density (0.917 g cm–3), and low specific surface area (29.7 m2 g–1) show an integration of high gravimetric, volumetric, and areal capacitances for supercapacitors.
We study a distributed antenna system where L antenna terminals (ATs) are connected to a central processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). ...This model has been widely investigated both for the uplink (UTs to CP) and for the downlink (CP to UTs), which are instances of the general multiple-access relay and broadcast relay networks. We contribute to the subject in the following ways: 1) For the uplink, we consider the recently proposed "compute and forward" (CoF) approach and examine the corresponding system optimization at finite SNR. 2) For the downlink, we propose a novel precoding scheme nicknamed "reverse compute and forward" (RCoF). 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding. 4) We provide extensive numerical results and finite SNR comparison with other "state of the art" information theoretic techniques, in scenarios including fading and shadowing. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. In both cases, for a given set of transmitters, the goal consists of selecting a subset of the receivers such that the corresponding system matrix has full rank and the sum rate is maximized. We present low-complexity ATs and UTs selection schemes and demonstrate through Monte Carlo simulation that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the noninteger penalty affecting CoF/RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, show competitive performance of the proposed approaches with significantly lower complexity.
Two new models for semantic communication systems are proposed. The first model incorporates the convolutional block attention module, which considers attention techniques in both the channel and ...spatial domains. The second model applies the efficient channel attention (ECA) network with reduced complexity. Experimental results demonstrate that the convolutional block attention module‐equipped model improved signal‐to‐distortion ratio performance by 25%$25\%$ at a signal‐to‐noise ratio of 0dB$0 \text{ dB}$ while maintaining a similar number of parameters compared to the existing model using squeeze‐and‐excitation network. Meanwhile, the efficient channel attention‐equipped model reduced parameters by approximately 48%$48\%$ without any degradation in performance compared to the existing model.
Two models to improve semantic communication systems are introduced. The first utilizes CBAM, considering attention in both channel and spatial domains, while the second uses the ECA network for complexity reduction. Experimental results demonstrate that the CBAM‐enhanced model notably enhances SDR performance in comparison to the existing SENet model, while the ECA‐equipped model achieves significant parameter reduction without sacrificing performance.
Selective hydrogenolysis of biomass-derived glycerol to propanediol is an important reaction to produce high value-added chemicals but remains a big challenge. Herein we report a PtCu single atom ...alloy (SAA) catalyst with single Pt atom dispersed on Cu nanoclusters, which exhibits dramatically boosted catalytic performance (yield: 98.8%) towards glycerol hydrogenolysis to 1,2-propanediol. Remarkably, the turnover frequency reaches up to 2.6 × 10
mol
·mol
·h
, which is to our knowledge the largest value among reported heterogeneous metal catalysts. Both in situ experimental studies and theoretical calculations verify interface sites of PtCu-SAA serve as intrinsic active sites, in which the single Pt atom facilitates the breakage of central C-H bond whilst the terminal C-O bond undergoes dissociation adsorption on adjacent Cu atom. This interfacial synergistic catalysis based on PtCu-SAA changes the reaction pathway with a decreased activation energy, which can be extended to other noble metal alloy systems.
This paper considers an uplink multiuser multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs), in which K users equipped with a single-antenna ...communicate with one base station (BS) with N r antennas. In this system, we present a novel multiuser MIMO detection framework inspired by coding theory. The key idea of the proposed framework is to create a code C of length 2N r over a spatial domain. This code is constructed by an autoencoding function that is completely described by a channel transformation followed by a quantization function of the ADCs for a fixed input constellation set. Using the proposed framework, we present a novel weighted minimum distance decoding (wMDD) that achieves the optimal detection performance by appropriately exploiting unequal channel reliabilities. In addition, we show that bit error rate exponentially decreases with the minimum distance of the code C, which plays a similar role with a condition number in conventional MIMO systems. Furthermore, we develop the communication method that uses the wMDD when the explicit channel state information is not available at the BS. Finally, numerical results are provided to verify the superiority of the proposed method.
Biomimetic assembly of high-quality nanosheets into nacre-like structures can produce macroscopic films with favorable mechanical and optical performances due to the intrinsic properties and high ...level of ordering of the nanoscale building blocks. Natural ground mica is abundant and exhibits great application potential. However, large size and low aspect ratio greatly limit its biomimetic assembly. Moreover, exfoliation of ground mica into high-quality nanosheets remains a significant challenge. Here, we report that large-scale exfoliation of ground mica into mono- or few-layered mica nanosheets with a production rate of ~1.0 g h
can be successfully achieved. The mica nanosheets are then assembled into strong biomimetic polymeric mica film that inherits the high electric insulation, excellent visible transmittance, and unique ultraviolet-shielding properties of natural mica. Its overall performance is superior to that of natural sheet mica and other biomimetic films, making the polymeric mica film a suitable substrate for flexible and transparent devices.
This paper considers a multiple-input multiple-output system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel communication framework that is inspired by ...supervised learning. The key idea of the proposed framework is to learn the nonlinear input-output system, formed by the concatenation of a wireless channel and a quantization function used at the ADCs for data detection. In this framework, a conventional channel estimation process is replaced by a system learning process, in which the conditional probability mass functions (PMFs) of the nonlinear system are empirically learned by sending the repetitions of all possible data signals as pilot signals. Then, the subsequent data detection process is performed based on the empirical conditional PMFs obtained during the system learning. To reduce both the training overhead and the detection complexity, we also develop a supervised-learning-aided successive-interference-cancellation method. In this method, a data signal vector is divided into two subvectors with reduced dimensions. Then, these two subvectors are successively detected based on the conditional PMFs that are learned using artificial noise signals and an estimated channel. For the case of 1-bit ADCs, we derive an analytical expression for the vector error rate of the proposed framework under perfect channel knowledge at the receiver. Simulations demonstrate the detection error reduction of the proposed framework compared to conventional detection techniques that are based on channel estimation.
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