Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.     

Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.     

玻璃中CdSeS纳米晶体的生长及其性能

对掺有镉、硒、硫的玻璃在500—800℃退火2—24h,生长了不同尺寸的CdSxSe1x纳米晶体.用分光光度计和光致发光光谱(PL)分析了纳米晶体的性能.退火温度低于550℃,纳米晶体处于成核阶段,600—625℃处于正常扩散生长阶段,700—800℃处于竞争生长阶段;而650℃处于两种生长阶段之间.虽然650℃下生长的纳米晶体的尺寸分布比较窄,但纳米晶体的尺寸随退火时间的延长几乎不变,在该温度改变退火时间很难改变纳米晶体的平均尺寸.在所有样品中出现了深能级缺陷,在650℃退火时间小于4h或大于16h有利 关键词： 纳米晶体 生长机理 深能级缺陷     

4~20 K 温区固体界面导热填料接触热阻实验研究

超导量子干涉仪、 超导光子探测器等深空探测器需要液氦温区制冷技术提供极低温温度, 固体界面接触热阻的存在会增大耦合界面温度差, 进而增加制冷机系统冷损. 为定量探究4~20 K 深低温区固体接触热阻, 采用GM 作为冷源, 设计了一台可同时调节压力和低温温度的固体界面接触热阻测试实验台. 利用感压纸进行接触界面压力校核, 并对温度重复性进行验证. 实验测试了不同导热介质填充情况下, 温度和压力变化时固体接触热阻的变化规律. 基于最小二乘法对实验数据进行半经验公式拟合, 获得4 ~20 K 温区不同压力加载条件下的接触热阻的定量参考.     

Deep level transient spectroscopy investigation of deep levels in CdS/CdTe thin film solar cells with Te:Cu back contact

Deep levels in Cds/CdTe thin film solar cells have a potent influence on the electrical property of these devices. As an essential layer in the solar cell device structure, back contact is believed to induce some deep defects in the CdTe thin film. With the help of deep level transient spectroscopy (DLTS), we study the deep levels in CdS/CdTe thin film solar cells with Te:Cu back contact. One hole trap and one electron trap are observed. The hole trap H1, localized at E_v+0.128~eV, originates from the vacancy of Cd (V_Cd. The electron trap E1, found at E_c-0.178~eV, is considered to be correlated with the interstitial Cu_i⁼ in CdTe.     

Harmonic operation of high gain harmonic generation free electron laser

In high gain harmonic generation (HGHG) free electron laser (FEL), with the right choice of parameters of the modulator undulator, the dispersive section and the seed laser, one may make the spatial bunching of the electron beam density distribution correspond to one of the harmonic frequencies of the radiator radiation, instead of the fundamental frequency of the radiator radiation in conventional HGHG, thus the radiator undulator is in harmonic operation (HO) mode. In this paper, we investigate HO of HGHG FEL.Theoretical analyses with universal method are derived and numerical simulations in ultraviolet and deep ultraviolet spectral regions are given. It shows that the power of the 3rd harmonic radiation in the HO of HGHG may be as high as 18.5% of the fundamental power level. Thus HO of HGHG FEL may obtain short wavelength by using lower beam energy.     

分子束外延生长赝配高电子迁移率超高速微结构功能材料里深中心识别

应用深能级瞬态谱(DLTS)技术研究分子束外延(MBE)生长的highelectronmobilitytransistors(HEMT)和Pseudomorphichighelectronmobilitytransistors(PHEMT)结构深中心行为.样品的DLTS谱表明,在HEMT和PHEMT结构的nAlGaAs层里存在着较大浓度(1015-1017cm-3)和俘获截面(10-16cm2)的近禁带中部电子陷阱.它们可能与AlGaAs层的氧含量有关.同时还观察到PHEMT结构晶格不匹配的AlGaAsInGaAsGaAs系统在AlGaAs里产生的应力引起DX中心(与硅有关)能级位置的有序移动.其移动量可作为应力大小的一个判据,表明DLTS技术是定性识别此应力的可靠和简便的工具. 关键词： 分子束外延生长 高电子迁移率超高速微结构功能材料 深中心     

基于拓扑优化和深度学习的新型结构生成方法

计算机辅助设计已广泛应用于结构计算和分析,但如何利用计算机智能生成最佳的新型结构还面临巨大挑战。针对这一问题,提出了一种基于拓扑优化和深度学习的新型结构智能生成方法。该方法首先通过结构拓扑优化分析获得不同参数下的优化结果制作训练集图片,并将训练集标签定义为相应的工况类型,然后应用最小二乘生成对抗网络(LSGAN)深度学习算法进行训练并生成大量的新型结构,最后建立评价指标和评估体系对生成的模型进行评价比较,根据需求选择最佳结构设计方案。结合一个铸钢支座节点底板设计的工程案例,详细阐述了上述方法的应用过程,并借助三维重构技术和增材制造技术实现结构模型的一体化制造。研究结果表明,基于拓扑优化和深度学习的新型结构智能生成方法不仅可以自动生成新的结构,而且可以进一步优化结构的材料用量和力学性能。     

2019年微纳米力学热点回眸

微纳米尺度下材料的力学性能与样品的尺寸和表界面等物理量密切相关,因此纳米材料的力学性能可以在很大范围内进行高效的调节,使得纳米材料能够拥有超轻、超硬、以及高断裂强度和高韧性等卓越的力学性能。从尺度缺陷效应和生长机理、屈曲机理分析和应用、界面力学性能和应用、生物和软物质力学性能、软体机器人、机器学习等方面盘点了微纳米力学2019年取得的研究成果。     

南海深海塑料垃圾污染

渔业捕捞及商业航运活动的塑料排放是南海深海塑料污染的主要来源。南海深海微塑料污染始于20世纪80年代,具有明显陆源输入的特征,陆架近岸区域微塑料污染严重。陆坡深海峡谷是塑料/微塑料向深海盆地输运的主要通道,近底浊流在输运中发挥了重要作用。综述了南海海底塑料垃圾深潜研究的最新进展,首次提出了深海塑料垃圾生态系统的概念。