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。     

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.     

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).     

Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.     

农药沉积利用率喷雾指示剂诱惑红的酶标仪测定方法

诱惑红是一种水溶性染料,近年来被用作农药沉积利用率测定的指示剂。诱惑红的检测方法为高效液相色谱法和分光光度法。高效液相色谱法需要专门的分析仪器,且价格昂贵,可见分光光度计法操作繁琐,试剂用量大,耗时长,均不适宜田间快速高效测定。笔者发展了诱惑红的酶标仪测定方法,简单高效,可批量检测多个样品,且准确度和精密度高,线性关系良好,514 nm检测波长下不受其他农药影响,并应用于玉米无人飞机喷雾场景下农药雾滴在玉米冠层的沉积利用率测定。     

磁性硅铁载铜吸附Hg0动力学机制及密度泛函研究

采用粉末冶金法将纳米单质铜(Cu⁰)、硅铁(FeSi)、四氧化三铁硅涂层(Fe₃O₄@SiO₂)混合煅烧并制备出新型磁性硅铁载铜吸附剂MagFeSi-Cu⁰。实验研究不同烟气温度下MagFeSi-Cu⁰的汞吸附能力基础上,结合颗粒内扩散模型、准二阶动力学模型、Elovich模型及Bangham模型分析了MagFeSi-Cu⁰吸附Hg⁰过程的主要控制步骤。在此基础上,依据密度泛函理论(DFT)研究了不同反应温度下FeSi表面Cu⁰原子与Hg⁰原子的汞齐作用机制。研究结果表明,Bangham模型的拟合值与MagFeSi-Cu⁰汞吸附实验值拟合度最高,MagFeSi-Cu⁰表面痕量Hg⁰吸附由汞的外扩散和表面铜汞齐吸附共同控制;通过密度泛函计算,发现Cu⁰颗粒表面Cu-Hg齐吸附能为-0.534 eV,当烟气温度从80℃上升至200℃时,Hg⁰原子与单质Cu原子的吸附自由能从-22.47 kJ/mol下降至-13.96 kJ/mol,这些结果为深入了解Hg⁰在Cu(111)表面的反应机理提供了理论基础。