Piezoelectric properties and microstructure of ceramicrete-based piezoelectric composites

Inorganic piezoelectric ceramic composite is the potential sensing element for long-term structural health monitoring due to its excellent durability and compatibility. In this study, a Ceramicrete-based piezoelectric composite is proposed preliminarily, in which the magnesium potassium phosphate cement is used as the matrix and the lead zirconate titanate particle is utilized as the functional phase. Piezoelectric properties test and microstructure analysis are performed to evaluate the testing samples. Results show that the piezoelectric performance of the composite increase with the increase of piezoelectric ceramic particle size. The value of the piezoelectric strain factor (d₃₃) can reach 83.8 pC/N, while the corresponding piezoelectric voltage factor (g₃₃) is 50.1 × 10^-3 V•m/N at the 50th day after polarization. Microstructure analysis illustrates that the interfacial transition zone (ITZ) between the matrix and the particles is dense. Moreover, the influence of aging on the composite is attributed to the continuous hydration after polarization. It indicates that the composites have a higher piezoelectric performance, which can be regarded as a promising sensing element material.     

Upregulated Expression of Activation-Induced Cytidine Deaminase in Ocular Adnexal Marginal Zone Lymphoma with IgG4-Positive Cells

Immunoglobulin G4-related disease (IgG4-RD) is a systemic disorder characterized by tissue fibrosis and intense lymphoplasmacytic infiltration, causing progressive organ dysfunction. Activation-induced cytidine deaminase (AID), a deaminase normally expressed in activated B-cells in germinal centers, edits ribonucleotides to induce somatic hypermutation and class switching of immunoglobulin. While AID expression is strictly controlled under physiological conditions, chronic inflammation has been noted to induce its upregulation to propel oncogenesis. We examined AID expression in IgG4-related ophthalmic disease (IgG4-ROD; n = 16), marginal zone lymphoma with IgG4-positive cells (IgG4+ MZL; n = 11), and marginal zone lymphoma without IgG4-positive cells (IgG4- MZL; n = 12) of ocular adnexa using immunohistochemical staining. Immunohistochemistry revealed significantly higher AID-intensity index in IgG4-ROD and IgG4+ MZL than IgG4- MZL (p < 0.001 and = 0.001, respectively). The present results suggest that IgG4-RD has several specific causes of AID up-regulation in addition to inflammation, and AID may be a driver of oncogenesis in IgG4-ROD to IgG4+ MZL.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

FPGA互连测试中的反馈桥接故障覆盖问题

现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题；然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。     

基于FLAC3D的破碎围岩巷道支护效果分析

为了解破碎围岩分别采用锚杆支护、锚喷支护以及锚喷+锚索耦合三种支护方式下的支护效果,进而为破碎围岩巷道选择合理的支护方式提供参考。通过借助FLAC^3D软件建立数值模型,分析不同支护条件下的破碎围岩巷道位移量、应力分布以及塑性区的时空演化特征。结果表明,采用锚喷+锚索耦合支护时,可以较好的控制巷道围岩的位移量、减小应力集中效应、缩小塑性区的影响范围。     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

用于电弧检测的电力专用SoC设计

针对传统的电弧电路故障检测结果不准确的问题,设计用于电弧检测的SoC系统,并且在55nm工艺下进行流片验证。采用包含两种结构的模数转换器的片上电压源,设计了锁相环以及复位电路,精度最高可达8.67 bit。验证结果表明,本设计可提高电弧检测的准确性。     

Process parameters influence on zone refining and thermodynamics analysis of 1,2-diphenylethane

Effective distribution coefficients of 9 impurities in 1,2-diphenylethane have been calculated by directional crystallization under different ambient frozen temperature. The effect of varied zone size, temperature difference between the melt and ambient frozen environment, number of zone on purity of 1,2-diphenylethane have been also investigated during the process of zone refining. The results indicate that the product purity in the intermediate purified region with varied zone size is higher 0.04%-0.2% than that with constant zone size. The product purity increases with temperature difference between the melt and ambient frozen environment. The appropriate temperature difference is adopted 50℃. The product purity in the intermediate region of sample bar with 2 molten zones is higher 0.05%-0.43% than that with 1 molten zone. In addition, the change of enthalpy and entropy between impurities and 1,2-diphenylethane have been determined.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.