Magnetic recyclable ZnO/SrFe12O19 photocatalyst for effective photodegradation of rhodamine B under simulated sunlight

This work develops a novel magnetic photocatalysts ZnO/SrFe₁₂O₁₉ (ZS) synthesized with hydrothermal process. The introduction of SrFe₁₂O₁₉ not only enhances the photocatalytic behavior of ZnO towards Rhodamine B (RhB) decomposition, but also reinforces the recycling stability. Especially, ZS-5 composite exhibits the optimal photocatalytic performance, and the RhB decomposition reaches 99.5% after being exposed to simulative sunlight for 70 min, which is obviously superior to that of bare ZnO. Furthermore, the ZS-5 can be recovered from RhB solution by an extra magnet space and reused. After five recycles, the RhB removal efficiency can still be maintained over 90%. Such prominent photocatalytic property and stability of ZS-5 are associated with the greatly improved detachment efficiency of photoexcited carriers in a magnetic field. This study could provide a new-type recyclable photocatalyst that can effectively purify dye wastewater for convenient recovery.     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

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。     

5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。     

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。     

Magnetic properties,phase evolution,hollow structure and biomedical application of hematite (α-Fe2O3) and QUAIPH

We investigate synthesis, phase evolution, hollow and porous structure and magnetic properties of quasi-amorphous intermediate phase (QUAIPH) and hematite (α-Fe₂O₃) nanostructure synthesized by annealing of akaganeite (β-FeOOH) nanorods. It is found that the annealing temperature determines the phase composition of the products, the crystal structure/size dictates the magnetic properties whereas the final nanorod morphology is determined by the starting material. Annealing of β-FeOOH at ～300 °C resulted in the formation of hollow QUAIPH nanorods. The synthesized material shows low-cytotoxicity, superparamagnetism and good transverse relaxivity, which is rarely reported for QUAIPH. The QUAIPH nanorods started to transform to porous hematite nanostructures at ～350 °C and phase transformation was completed at 600 °C. During the annealing, the crystal structure changed from monoclinic (akaganeite) to quasi-amorphous and rhombohedral (hematite). Unusually, the crystallite size first decreased (akaganeite → QUAIPH) and then increased (QUAIPH → hematite) during annealing whereas the nanorods retained particle shape. The magnetic properties of the samples changed from antiferromagnetic (akaganeite) to superparamagnetic with blocking temperature T_B = 84 K (QUAIPH) and finally to weak-ferromagnetic with the Morin transition at T_M = 244 K and high coercivity H_C = 1652 Oe (hematite). The low-cytotoxicity and MRI relaxivity (r₂ = 5.80 mM^?1 s^?1 (akaganeite), r₂ = 4.31 mM^?1 s^?1 (QUAIPH) and r₂ = 5.17 mM^?1 s^?1 (hematite)) reveal potential for biomedical applications.