Fabrication of high-efficiency Yb:Y2O3 laser ceramics without photodarkening

High-efficiency Yb:Y₂O₃ laser ceramics were fabricated using the vacuum-sintering plus hot isostatic pressing (HIP) without sintering additives. High-purity well-dispersed nanocrystalline Yb:Y₂O₃ powder was synthesized using a modified co-precipitation method in-house. The green bodies were first vacuum sintered at a temperature as low as 1430°C and then HIPed at 1450°C. Finally, the samples were air annealed at 800°C for 10 h. Although no sintering aids were used, full density of the samples with excellent optical homogeneity and an inline transmission of 80% at 400 nm could be obtained. Moreover, photodarkening phenomenon was not detected in the ceramics. Preliminary laser experiment with the fabricated ceramics in a two-mirror cavity has demonstrated 32 W continuous-wave (CW) output at ∼1077 nm with an optical-to-optical conversion efficiency of 58.2%. To the best of our knowledge, this is so far the highest CW output power and optical-to-optical conversion efficiency achieved with the Yb³⁺-doped sesquioxide ceramics in a simple two-mirror cavity.     

建筑物掏土纠倾法单孔与多孔塑性区特性研究及工程应用

在建筑物水平掏土纠倾工程中，掏土孔间距是影响纠倾工程安全与工期的重要因素。为了快速准确地确定纠倾工程中的水平掏土孔间距，研究了单个掏土孔和多个掏土孔情况下孔周边土体塑性区发展特性。利用土体塑性力学分析计算得到了单孔下的孔周土塑性区半径，而后通过有限元模拟得到孔周土体塑性区半径的数值解，将孔周塑性区半径解析解与数值解进行了对比。并通过有限元数值模型研究了多个掏土孔相互影响情况下的塑性区发展规律，以孔间土体塑性区贯通时的距离作为掏土孔间距。考虑土体参数随机特性的影响，研究不同上部荷载作用下掏土孔间距的取值变化规律，上部面荷载与地基承载力特征值比值用p表示，孔间距与掏土孔直径比值用n表示。研究发现：多孔塑性区半径（孔间塑性区贯通时）是单孔塑性区半径的1.3倍左右;标准化荷载p与孔间距比值n二者呈线性关系;通过不同土体参数及上部荷载的不同情况下的p-n曲线，给出了掏土孔间距建议值。同时，将研究结果与三个实际工程进行对比，发现p-n曲线法与实际结果更为接近。     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

风险矩阵法在A水泥工厂风险分级的应用初探

本文介绍“风险矩阵法”进行风险分级工作的基本思路，结合水泥厂的生产特点通过危险有害因素辨识，获得危险源分布情况，采用风险矩阵法对风险进行评估，按风险值将风险等级划分为重大风险、较大风险、一般风险和低风险，为水泥生产企业的安全风险分级工作提供参考。     

Omega-6支撑身体，Omega-3支撑大脑：均衡摄入对儿童大脑发育的影响（网络首发、推荐阅读）

人体大脑和身体的发育，需要从食物中摄取均衡的营养物质。人类大脑是区分人类和其他动物的特征。食物中的必需脂肪酸是机体组织结构和功能的必要组成部分。Omega-6（O6）亚油酸（LA6）是皮肤组织的组成成分，且是炎症、血栓形成、免疫和其他信号分子的前体；Omega-3（O3）α-亚麻酸（ALA3），特别是其长链代谢产物——二十二碳六烯酸（DHA3），是大脑、视网膜和部分神经组织中的关键组分。从富含LA6脂肪酸（缺乏O3脂肪酸）的植物籽中提取出的廉价而优质油脂，是20世纪的西方国家食品工业生产的主要脂肪来源。在代谢通路中，高浓度的LA6脂肪酸可拮抗O3脂肪酸代谢，造成O3脂肪酸不足，因此，在给怀孕动物的饲料中，只提供富含LA6但缺乏O3脂肪酸的油脂作为唯一的脂肪来源，会导致幼崽大脑发育不良。过去20~30年的研究表明，低含量LA6且含DHA3的油脂可改善大脑的功能。近年来的研究较多集中在营养因素对大脑发育的影响，最新研究数据表明，脂肪酸平衡对营养不良儿童的大脑发育尤为重要。世界卫生组织（WHO）越来越重视大脑的营养健康，通过其下属的食品法典委员会，建议用于治疗严重急性营养不良儿童的即食治疗食品中，使用含有均衡脂肪酸组成/构成的脂肪。同样，脂肪酸均衡对老年人可能也很重要。目前，业界已经有了调整油脂成分的方法，以确保脂肪酸均衡，从而维持人体整个生命周期的大脑健康。     

火花放电原子发射光谱类型标准化校正方式的探讨

在国内,火花放电原子发射光谱分析广泛使用类型标准化进行方法校正。在国外,标准和文献中鲜见使用此方法的相关论述。类型标准化主要采用平移校正和转动校正两种方式,哪种方式更加合理也鲜见报道。国外设备类型标准化的默认设置优先采用转动校正方式,相关国内标准对最优校正方式的规定尚不明确,此默认设置的合理性有待探讨。实验选用低合金钢20CrNi2Mo、R407标准样品和不锈钢317L、0Cr18Ni9标准样品,引用国内相关标准,以正确度临界差为评判依据,模拟类型标准化样品和待测样品“十分接近”和“接近”两种情况下平移校正和转动校正的数据正确度。经数据统计分析,平移校正分析结果均满足要求,转动校正结果在“接近”情况下部分元素不满足要求。结合相关国家标准中元素含量范围和精密度数据进行分析,通过计算允许最大偏倚量并制作曲线图方式展开分析,得出如下结论:在满足文中类型标准化控制要点前提下,分析设置更适合于采用平移校正方式。     

Behavior of phase transformation of Baotou mixed rare earth concentrate during oxidation roasting

Based on the new process named “Combination Method” for metallurgy and separation of Baotou mixed rare earth concentrate (BMREC), the aim of this paper is to clearly elucidate the phase change behavior of BMREC without additives during oxidative roasting at 450–800 °C. The results indicate that the bastnaesite in BMREC is decomposed at 450–550 °C, the weight loss is about 10.3 wt%, and the activation energy (E) is 144 kJ/mol. The bastnaesite in BMREC is decomposed into rare earth fluoride, rare earth oxides (La₂O₃, Ce₇O₁₂, Pr₆O₁₁ and Nd₂O₃), and CO₂, particularly, with the increase of roasting temperature, bastnaesite in BMREC is more completely decomposed into LaF₃, which causes a decrease in leaching rate of La during the HCl leaching process. Additionally, the maximum cerium oxidation efficiency reaches about 60 wt% when the roasting temperature is equal to or above 500 °C, and the oxidation reaction rate of cerium increases with the increasing roasting temperature.     

基于CiteSpace的资源型产业研究知识图谱分析

资源型产业发展为推进国家经济增长和工业化进程提供了重要保障。为深入了解资源型产业的研究情况，以CNKI数据库为数据源，搜集2000—2020年关于资源型产业的核心及以上期刊论文，利用CiteSpace软件从发文作者与研究机构分布、关键词共现网络和时区图谱等方面，绘制知识图谱，进行可视化分析。研究发现：资源型产业领域的研究成果愈加丰富，但研究群体间联系合作较少，且现有的合作研究主要集中在所处地域资源富集和具有学科优势的研究机构及学者；资源型产业领域的研究热点可概括为产业发展、资源型城市、产业集群、产业结构、产业链和产业集聚等方面；针对资源型产业领域未来可从资源型产业相关理论研究、创新发展模式和可持续发展等方面深入展开。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).