Microstructure,mechanical, tribological,and oxidizing properties of AlCrSiN/AlCrVN/AlCrNbN multilayer coatings with different modulated thicknesses

Multilayer structure design is one of the most promising methods for improving the comprehensive performance of AlCrN-based hard coatings applied to cutting tools. In this study, four types of AlCrSiN/AlCrVN/AlCrNbN multilayer coatings, with different modulated thicknesses, were deposited to investigate their microstructure, mechanical, tribological, and oxidizing properties. All multilayer coatings exhibited grain growth along the crystallographic plane of (200) with a NaCl-type face-centered cubic (FCC) structure. The results show that, as the modulation thickness decreases from ～35 nm to ～10 nm, (1) the grain refinement effect is increasingly evident; (2) all multilayer coatings show a hardness of >30 GPa and an elastic modulus of >300 GPa. Both the ability to resist elastic strain to failure and the plastic deformation of multilayer coatings increase. In addition, their resistance to cracking reduces; (3) the wear rates of these multilayer coatings reduce successively from 1.78 × 10^?16 m³ N^?1 m^?1 to 7.7 × 10^?17 m³ N^?1 m^?1. This is attributed to an increase in self-lubricating VO_x and a decrease in adhesives from the counterparts; (4) the best high-temperature oxidation resistance was obtained for the multilayer coating with a modulated thickness of ～15 nm.     

Microstructures and properties of nickel-titanium carbide composites fabricated by laser cladding

In this work, Ni/TiC composites were synthesized by the laser cladding technique (LCT). A scanning electron microscope (SEM), X-ray diffractometer (XRD), microhardness meter, electrochemical workstation, and friction and wear tester examined the microstructure, surface morphology, phase structure, microhardness, wear, and corrosion resistances of the Ni/TiC composites. These results indicated the Ni/40TiC composite contained finer equiaxed crystals than the Ni and Ni/20TiC composites. In addition, numerous TiC particles in the Ni/40TiC composite impeded growth of the nickel crystals, which resulted in the fine microstructure of the Ni/40TiC composite. The Ni, Ni/20TiC, and Ni/40TiC composites exhibited face-centered cubic (f c c) lattices. The average microhardness values of the Ni/20TiC and Ni/40TiC composites were approximately 748 HV and 851 HV, respectively. The Ni/40TiC composite had the lowest friction coefficient (0.43) among all three coatings, and only some shallow scratches appeared on the surface of the Ni/40TiC composite. The corrosion potential (E) of Ni/40TiC exceeded the Ni/20TiC composite, and both were larger than the Ni composite, which indicated the Ni/40TiC composite had outstanding corrosion resistance and the Ni composite had poor corrosion resistance. The corrosion current densities (i) of Ni, Ni/20TiC, and Ni/40TiC composites were 5.912, 4.405, and 3.248 μA/cm², respectively.     

Catalytic combustion synthesis of CNTs/MgO composite powders and the influences on the thermal shock resistance of low-carbon Al2O3–C refractories

Using MgC₂O₄, Mg powders as raw materials and Ni(NO₃)₂?6H₂O as a catalyst, CNTs/MgO composite powders were prepared by a catalytic combustion synthesis method. The CNTs/MgO composite powders were characterized by XRD, Raman spectroscopy, FESEM/EDS and HRTEM. The effects of catalyst content on the degree of graphitization and aspect ratio of the CNTs in composite powders were investigated. Moreover, the thermal shock resistance of low-carbon Al₂O₃–C refractories after adding the composite powder was investigated. The results indicated that the CNTs prepared with 1 wt% Ni(NO₃)₂?6H₂O addition had a higher degree of graphitization and aspect ratio. In particular, the aspect ratio could reach approximately 200. The growth mechanism of hollow bamboo-like CNTs in the composite powders was proven to be a V-L-S mechanism. The thermal shock resistance of Al₂O₃–C samples could be improved significantly after adding CNTs/MgO composite powders. In particular, compared with CM0, the residual strength ratio of Al₂O₃–C samples with added 2.5 wt% composite powders could be increased 63.9%.     

Experimental research on combined effect of obstacle and local spraying water fog on hydrogen/air premixed explosion

In order to reveal the mechanism of water fog explosion suppression and research the combined effect of water fog and obstacle on hydrogen/air deflagration, multiple sets of experiments were set up. The results show that the instability of thermal diffusion under lean combustion conditions is the main influencing factor of hydrogen/air flame surface instability, and the existence of water fog will aggravate the hydrogen/air flame surface instability. When obstacle is not considered, 8 μm, 15 μm, 30 μm water fog can significantly reduce the flame velocity and explosion overpressure of hydrogen/air, 45 μm fine water fog plays the opposite role. When considering the relative position of the water fog release position and the obstacle, the 8 μm, 15 μm, 30 μm water fog has almost no suppression effect when released near the obstacle, but a significant suppression effect occur, when using the 45 μm water fog. In the field of theoretical research, the research results not only provide an experimental basis for the fine water fog to reduce the consequences of hydrogen explosion accidents, and the optimal diameter range used by the water fog, but also provide experimental reference for the numerical simulation of hydrogen/air explosion suppression in semi-open space, and promote the development of hydrogen explosion suppression theory. In terms of engineering applications, this study can provide a theoretical basis for the layout of fire fighting equipment in the engine room of nuclear power plants or hydrogen-powered ships.     

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.     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Double emulsion (W/O/W) gel stabilised by polyglycerol polyricinoleate and calcium caseinate as mangiferin carrier: insights on formulation and stability properties

Mangiferin (MGF) is a phenolic compound isolated from mango, but its poor solubility significantly limits its use. In this study, MGF was embedded into the inner aqueous phase of W₁/O/W₂ emulsions. Firstly, the dissolution method of MGF was determined. MGF remained stable in solution with pH 13 at 30 min, and its solubility reached 10 mg mL⁻¹. When the pH of MGF solutions was adjusted from pH 13 to pH 6, MGF did not immediately crystallise, providing sufficient time to construct the MGF-loaded W₁/O/W₂ emulsions. Subsequently, the MGF-loaded W₁/O/W₂ emulsions were constructed using polyglycerol polyricinoleate (PGPR) and calcium caseinate (CAS). The formation and stability of the W₁/O/W₂ emulsions were investigated. The MGF-loaded W₁/O/W₂ emulsions stabilised with 1% PGPR and 1% – 3% CAS exhibited a low viscosity, limited loading capacity, and poor stability. Conversely, the MGF-loaded W₁/O/W₂ emulsions stabilised by 3%PGPR–3%CAS exhibited optimal loading capacity (encapsulation efficiency = 95.31% and loading efficiency = 0.91%) and stability, which was attributed to the fact that high viscosity and gel state retarded the migration of inner aqueous phase. These results indicated that the W₁/O/W₂ emulsions stabilised by PGPR and CAS may be a potential alternative for encapsulating mangiferin.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.