Molar heat capacities and volumes of transfer of cytosine,thymine, caffeine and 1,3-diethylthymine to aqueous solutions of glycyl-glycine and L-α-alanyl-L-α-alanine at 25°C

Densities and specific heat capacities of ternary aqueous systems containing dipeptides (glycyl-glycine or L--alanyl-L--alanine) and nucleic acid bases (cytosine or thymine) or their alkyl derivatives (1,3-diethylthymine or caffeine) were determined at 25°C by flow calorimetry and flow densimetry. The partial molar volumes and heat capacities of transfer at infinite dilution of the different nucleic acid bases from water to water+dipeptide solutions were obtained therefrom. Except for the case of the transfer of cytosine to aqueous glycyl-glycine solutions where a small positive dependence of the transfer quantities was observed with the dipeptide concentration, the values of the heat capacities of transfer were in general low, positive or negative, depending on the compensation of hydrophobic-hydrophilic interactions between the dipeptide and the base. The volumes of transfer of most of the bases are very small, within the limit of the experimental error.     

一种基于粒子群算法的DC-NOMA系统遍历容量优化方案

为提升设备到设备（Device-to-Device,D2D）辅助的协作非正交多址接入（Non-orthogonal Multiple Access,NOMA）中继系统的性能,以最大化系统遍历容量为目标建立优化模型,利用粒子群优化（Particle Swarm Optimization,PSO）算法设计最优功率分配策略,求出每个用户的最佳功率分配因子,从而得到系统遍历容量的最优值。仿真结果表明,所提出的基于PSO的功率分配方案不仅能提高系统容量,还可以降低用户的中断概率。     

Performance of orthogonal frequency division multiplexing based M-ary quadrature amplitude modulation in asymmetrical dual-hop mixed RF-FSO transmission system

This paper investigates the performance of a dual-hop mixed relay system with radio frequency (RF) and free-space optics (FSO) communication under the effect of pointing error (PE) and atmospheric turbulence (AT). This paper considers a system where RF and FSO links are cascaded. The RF link is modeled by Nakagami-m fading, and the FSO link is modeled as gamma–gamma (G-G) fading channel. Both the channel models use orthogonal frequency division multiplexing (OFDM) with M-ary quadrature amplitude modulation (QAM). The expressions for probability density function, cumulative distribution function, signal-to-noise ratio, and ergodic capacity are derived. The moment generating function (MGF) of fading and the bit error rate (BER) of the OFDM-based M-ary QAM scheme is derived in terms of Meijer's G-function. It has been observed that, in fixed gain relay systems, the modulation scheme's BER is dominated by the SNR of the RF link. While in a variable gain relay system, the turbulence conditions of the FSO system affect the SNR and the BER of the modulation method. The feasibility of heterodyne detection and intensity modulation direct detection (IM/DD) is analyzed in terms of outage probability and ergodic capacity. The results can be used to choose the optimal modulation order and relay system for QAM-OFDM-based optical wireless systems.     

Global attention retinex network for low light image enhancement

Most low-light image enhancement methods only adjust the brightness, contrast and noise reduction of low-light images, making it difficult to recover the lost information in darker areas of the image, and even cause color distortion and blurring. To solve the above problems, a global attention-based Retinex network (GARN) for low-light image enhancement is proposed in this paper. We propose a novel global attention module which computes multiple dimensional information in the channel attention module to help facilitate inference learning. Then the global attention module is embedded into different layers of the network to extract richer shallow texture features and deep semantic features. This means that the rich features are more conducive to learning the mapping relationship between low-light images to normal-light images, so that the detail recovery of dark regions is enhanced in low-light images. We also collected a low/normal light image dataset with multiple scenes, in which the images paired as training set can succeed to be applied to low-light image enhancement under different lighting conditions. Experimental results on publicly available datasets show that our method has better effectiveness and generality than the state-of-the-art methods in terms of evaluations metrics such as PSNR, SSIM, NIQE, Entropy.     

Unlocking the Capacity of Vanadium Oxide by Atomically Thin Graphene-Analogous V2O5·nH2O in Aqueous Zinc-Ion Batteries

Aqueous Zn-ion batteries (AZIBs) are promising due to their high theoretical energy density and intrinsic safety, and the natural abundance of Zn. Since low voltage is an intrinsic shortage of AZIBs, achieving super-high capacity of cathode materials is a vital way to realize high practical energy density, which however remains a huge challenge. Herein, the capacity increase of classical vanadium oxide cathode is predicted via designing atomic thickness of 2D structure to introduce abundant Zn²⁺ storage sites based on density functional theory (DFT) calculation; then graphene-analogous V₂O₅·nH₂O (GAVOH) with only few atomic layers is fabricated, realizing a record capacity of 714 mAh g⁻¹. Pseudocapacitive effect is unveiled to mainly contribute to the super-high capacity due to the highly exposed GAVOH external surface. In situ Raman and synchrotron X-ray techniques unambiguously uncover the Zn²⁺ storage mechanism. Carbon nanotubes (CNTs) are further introduced to design GAVOH-CNTs gel ink for large-scale cathode fabrication. The hybrid cathode demonstrates ultra-stable cycling and excellent rate capability and delivers a high energy density of 476 Wh kg⁻¹ at 76 W kg⁻¹; 228 Wh kg⁻¹ is still retained at high mass loading of 10.2 mg cm⁻². This work provides inspiration for breaking the capacity limit of cathode in AZIBs.     

CuI Encapsulated within Single-Walled Carbon Nanotube Networks with High Current Carrying Capacity and Excellent Conductivity

High current carrying capacity and high conductivity are two important indicators for materials used in microscale electronics and inverters. However, it is challenging to obtain high conductivity and high current carrying capacity at the same time since high conductivity requires a weakly bonded system to provide free electrons, while high current carrying capacity requires a strongly bonded system. In this paper, CuI@SWCNT networks by filling the single-walled carbon nanotubes (SWCNTs) with CuI is ingeniously prepared. CuI@SWCNT shows good stability due to the confinement protection of SWCNTs. Through the host-guest hybridization, CuI@SWCNT networks exhibit a current carrying capacity of 2.04 × 10⁷ A cm⁻² and a conductivity of 31.67 kS m⁻¹. Their current carrying capacity and conductivity are significantly improved compared with SWCNT. The Kelvin probe force microscopy measurements show a drop of surface potential energy after SWCNT filled with CuI, indicating that the CuI guest molecules regulate the position of the Fermi level of SWCNTs, increasing carrier concentration, achieving high conductivity and high current carrying capacity. This study offers ideas and solutions for the regulation of high-performance carbon tube networks, which hold great promise for future applications in carbon-based electronic devices.     

高速移动通信系统中OTFS分数多普勒信道估计加窗研究

针对正交时频空间(OTFS)调制系统中分数多普勒信道对应的物理路径信道状态信息估计困难及计算复杂度较高等问题,该文提出一种节省导频资源的脉冲匹配滤波(PRS-PMF)信道估计算法。该算法首先使用数据与导频联合成帧的嵌入式辅助导频方法获得等效信道的估计,然后通过互相关匹配滤波估计出各路径信道状态信息,相比于传统的脉冲导频互相关匹配滤波信道估计算法,能够在降低计算复杂度的同时减少导频资源的占用。在此基础上,对OTFS系统加窗,减少窗口响应主瓣的整数样点数量并降低旁瓣电平,有效改善了等效信道多普勒响应函数的自相关特性,从而降低了其他符号及噪声对估计符号的干扰。     

大规模MIMO系统中收发联合阈值天线选择算法

在大规模多输入多输出(massive MIMO)系统中使用天线选择算法可提高能效和系统吞吐量,然而适用于传统MIMO系统的天线选择算法具有高复杂度,很难用于massive MIMO系统。为优化天线选择算法,以算法复杂度和系统容量为优化目标,提出了收发联合阈值天线选择算法。该算法在发射端使用最大范数双向天线选择算法进行天线选择,在接收端使用分组maxvol算法并通过仿真实验结果的预设阈值进行天线选择。仿真实验表明,收发联合阈值天线选择算法在降低复杂度的同时可以提高系统容量,与递增天线选择算法相比,系统容量最多可提高52.2 bit/s/Hz。提出的天线选择算法可以满足不同天线相关度和信噪比的传输环境。     

CCNet: CNN model with channel attention and convolutional pooling mechanism for spatial image steganalysis

Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application.     

On the multi-level embedding of crypto-image reversible data hiding

Crypto-space reversible image steganography has attracted increasing attention, given its ability to embed authentication information without revealing the image content. This paper presents an efficient reversible data hiding scheme for crypto-images: a block predictor is applied to compute prediction errors, then an adaptive block mapping algorithm is utilized to compress them whose amplitudes are within a small threshold, finally, this strategy can be applied in a multi-level manner to achieve a higher embedding capacity. Due to the correlations among adjacent pixels in the block, images can be sufficiently compressed to reserve abundant space for additional data embedding. Different from the prior arts, the compression code of the image is fully encrypted. Experimental results verify that the embedded data and original image can be perfectly recovered, the security is higher compared with the state-of-the-arts, and a significant improvement in the average embedding rate is achieved on two large-scale image datasets.