High performance asymmetric supercapacitor based on 3D microsphere-like 1T-MoS2 with high 1T phase content

1T phase molybdenum disulfide (1T-MoS₂) has aroused extensive concern in energy storage devices such as supercapacitors due to its large interlayer spacing, high conductivity and good hydrophilicity. However, it is struggle to synthesize 1T-MoS₂ with stable 1T phase with high content. Herein, Ammonium ion intercalation molybdenum disulfide (A-MoS₂) with high 1T content and stable 3D microsphere structure was successfully synthesized using a facile hydrothermal method. We explained the feasibility of ammonium ion (NH₄⁺) intercalation through density functional theory (DFT) calculations and proved the successful intercalation of NH₄⁺ by XRD and XPS. Through XPS fitting, the 1T phase content is calculated as high as 83.1%. The as-prepared A-MoS₂ presents a stable 3D microsphere structure with the interlayer spacing expanded to 0.93 nm, which provides a wide ion diffusion channel that allows ions to pass through quickly. Moreover, the high 1T content increases the hydrophilicity of MoS₂, thereby improving the wettability of the electrode, which contributes to the interaction between the electrolyte and electrode. In 1 M Na₂SO₄, A-MoS₂ electrode material displays high specific capacitance of 228 F g^?1 at 5 mV s^?1 and retains 127 F g^?1 at 80 mV s^?1, which proves the good rate capability. Furthermore, the assembled α-MnO₂//A-MoS₂ asymmetric supercapacitor (ASC) displayed a wide operating voltage of 2.1 V. The assembled ASC displays a high energy density of 35.8 Wh?kg^?1 at a power density of 525.0 W kg^?1, which indicates excellent energy storage performance.     

Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

Fabrication of BSCF-based mixed ionic-electronic conducting membrane by electrophoretic deposition for oxygen separation application

Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3-δ (BSCF), which exhibits a high mixed oxide ionic-electronic conduction, was used for the fabrication of an oxygen separation membrane. An asymmetric structure, which was a thin and dense BSCF membrane layer supported on a porous BSCF substrate, was fabricated by the electrophoretic deposition method (EPD). Porous BSCF supports were prepared by the uniaxial pressing method using a powder mixture with BSCF and starch as the pore-forming agent (0–50 wt.%). The sintering behaviors of the porous support and the thin layer were separately characterized by dilatometry to determine the co-fired temperature at which cracking did not occur. A crack-free and thin dense membrane layer, which had about a 15 μm thickness and >95% relative density, was obtained after optimizing the processes of EPD and sintering. The dense/porous interface was well-bonded and the oxygen permeation flux was 2.5 ml (STP) min⁻¹ cm^-2 at 850 °C.     

GaAs Mach－Zehnder型光波导开关／调制器

采用非对称Ｘ结耦合器代替传统的Ｙ分支器，研制了ＧａＡｓＭａｃｈ－Ｚｅｈｎｄｅｒ型２×２光开光，得到了小于－２２．４ｄＢ的串音比和１２Ｖ左右的开关电压，器件的波导传输损耗小于７ｄＢ／ｃｍ。预计该器件可广泛应用于ＧａＡｓ开关列阵及高速光调制等领域。     

An asymmetric line-axis compound parabolic concentrating single basin solar still

An asymmetric line-axis compound parabolic concentrating single basin solar still of concentration 1.15, exit aperture 0.24 metres and length 0.48 metres, 30° inclination and 60° half acceptance angle has been designed fabricated and tested.     

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model

A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities.The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone,based on which a universal-type parametric model can be established to approximate the entire system.This model can be established without separating the nonlinear part from the linear part.The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm.Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.     

基于混淆矩阵和集成学习的分类方法研究

针对多分类问题,本文提出一种基于混淆矩阵和集成学习的分类方法。从模式间的相似性关系入手,基于混淆矩阵产生层次化分类器结构;以支持向量机(SVM)作为基本的两类分类器,对于分类精度不理想的SVM,通过AdaBoost算法对SVM分类器进行加权投票。以变电站环境监控中的目标识别为例(涉及到人、动物、普通火焰(红黄颜色火焰)、白色火焰、白炽灯),实现了变电站环境监控中的目标分类。实验表明,所提出的方法有效提高了分类精度。     

Nonparametric kernel density estimation near the boundary

Standard fixed symmetric kernel-type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. It is shown that, in such settings, alternatives of asymmetric gamma kernel estimators are superior, but also differ in asymptotic and finite sample performance conditionally on the shape of the density near zero and the exact form of the chosen kernel. Therefore, a refined version of the gamma kernel with an additional tuning parameter adjusted according to the shape of the density close to the boundary is suggested. A data-driven method for the appropriate choice of the modified gamma kernel estimator is also provided. An extensive simulation study compares the performance of this refined estimator to those of standard gamma kernel estimates and standard boundary corrected and adjusted fixed kernels. It is found that the finite sample performance of the proposed new estimator is superior in all settings. Two empirical applications based on high-frequency stock trading volumes and realized volatility forecasts demonstrate the usefulness of the proposed methodology in practice.     

EFFECTS OF ASYMMETRIC TEMPERATURE PROFILES ON THERMAL CONVECTION DURING PHYSICAL VAPOR TRANSPORT OF Hg2Cl2

Asymmetric horizontal thermal profiles can change the flow field structure in the physical vapor transport (PVT)of Hg₂Cl₂. It is found that for the ratios of horizontal to vertical thermal Rayleigh numbers Ra_H/Ra ≥ 1·5, the convetive flow structure changes from multicellular to unicellular for the base parametric state of Ra = 2·79 × 10⁴, Pr = 0·91, Le=l·01. Pe = 4·60, Ar= 0·2 and C_r = 1·01. The unicellular flow structure obtained by increasing ∇*_H(Ra_H) is not likely to support the conjecture of the presence of unicellular convective mode in the laser Doppler velocimetry (LDV) experiments for PVT process of Hg₂Cl₂ (Kim el al., 1996). When the six parameters of Ra, Pr, Le, Pe, Ar and C_r are fixed, the dimensional maximum magnitude of the velocity vector |U|_max is proportional to √∇T*_H.     

Enantio-differentiating hydrogenation of prochiral ketones over modified nickel

This article reviews the enantio-differentiating hydrogenation of prochiral ketones over a asymmetrically modified catalyst, focusing on the hydrogenation of simple prochiral alkanones. The parameters affecting catalytic activity and enantiodifferentiating ability are considerable in number, and each parameter should be optimized in order to attain a highperformance enantio-differentiating catalyst. Optimization of the parameters and the mode of enantio-differentiation are discussed and compared with the enantio-differentiating hydrogenation of β-ketoesters.