Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

旋转叶片电加热防冰系统设计与应用

在开展结冰试验时，结冰风洞风扇叶片前缘有结冰风险，影响风洞安全运行。针对大型旋转叶片结冰问题，提出了旋转叶片电加热防冰系统设计方法，研制了大型结冰风洞旋转叶片电加热防冰系统。首先，研制了内置电加热单元和温度反馈的防冰叶片。针对叶片复杂的工作环境，提出一种新的旋转叶片电加热防冰功率计算方法，通过精确测量和方案优化，设计了基于特殊环境的分半式、大尺寸、高线速度、碳刷自动移开/压紧的导电滑环。最后，采用变结构分级温度闭环和试验参数连锁防冰控制策略解决了旋转叶片结冰问题。该系统已应用于大型结冰风洞，运行中加热电流和叶片温度反馈信号传输连续，所有旋转叶片前缘快速加热且温度分布均匀，防冰效果好。     

软模板法制备多级孔ZSM-5分子筛的研究进展

相比传统ZSM-5分子筛,多级孔ZSM-5分子筛具有空间位阻小、传质效率高、焦炭少等特点,近年来在分子筛领域应用广泛。多级孔分子筛通常可用后处理法和模板剂法制备,相比后处理法,模板剂法能更好地控制介孔的结构和孔道尺寸。概述了采用传统表面活性剂、两亲性的有机硅烷、双功能多季铵盐表面活性剂及高分子聚合物等软模板法合成多级孔ZSM-5分子筛的研究进展。分析不同软模板剂的特点和作用机理,阐述了合成后分子筛的结构特点及催化性能等。指出在今后的研究中,可以设计价格较低的新型功能化模板剂,优化合成过程,致力于在理解合成机理的前提下,寻找操作简单、绿色环保的合成路线,并将其推行到实际工业生产中。     

<i>In Silico</i> analysis and linking of metabolism-related genes with the immune landscape in head and neck squamous cell carcinoma

Metabolic reprogramming and immunologic suppression are two critical characteristics promoting the progression of head and neck squamous cell carcinoma (HNSCC). The integrative analysis of all the metabolism-related genes (MRGs) in HNSCC is lacking and the interaction between the metabolism and the immune characteristics also requires more exploration to uncover the potential mechanisms. Therefore, this study was designed to establish a prognostic signature based on all the MRGs in HNSCC. Genes of HNSCC samples were available from the TCGA and GEO databases while the MRGs were retrieved from a previous study. Ultimately 4 prognostic MRGs were selected to construct a model possessing robust prognostic value and accuracy in TCGA cohorts. The favorable reproducibility of this model was confirmed in validation cohorts from GEO databases. The risk score calculated by this model was an independent prognostic factor that further classified these HNSCC patients into high-/low-risk groups. GSEA analyses and somatic mutations indicated the low-risk group could activate several anti-tumor pathways and possessed lower TP53 mutation. The results of ESTIMATE, single-sample GSEA, CIBERSORT, and some immune-related molecules analyses suggested the low-risk group exhibited lower metabolic activities and higher immune characteristics. The Spearman correlation test implied most metabolic pathways with tumor-promoting function were negatively correlated with the immune activity, indicating a plausible approach of combining the anti-metabolism and the immunotherapy drugs in the high-risk group to enhance therapeutic effects than applied separately. In conclusion, this prognostic signature linking MRGs with the immune landscape could promote the individualized treatment for HNSCC patients.     

Methodology for robust multi-parametric control in linear continuous-time systems

This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-off based on a worst-case propagation of the uncertainty using either interval analysis or ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and a chemical reactor cascade.     

基于NB分类器重访概率预测的Web缓存替换策略

Web缓存是用来解决网络访问延迟和网络拥塞问题，缓存替换策略直接影响缓存的命中率。为此，提出一种朴素贝叶斯（NB）分类器重访概率预测的Web缓存替换策略；根据用户之前访问日志，通过分区操作提取多项特征来表示每次访问的对象，并构建特征数据集；训练NB分类器，用来确定缓存中对象被再次访问的概率，为对象分配权重；结合LRU策略来合理删除一些对象。仿真结果表明，提出的策略在保证较高命中率的同时有效降低了执行时间。     

Mechanically Robust,Ultraelastic Hierarchical Foam with Tunable Properties via 3D Printing

A mechanically robust, ultraelastic foam with controlled multiscale architectures and tunable mechanical/conductive performance is fabricated via 3D printing. Hierarchical porosity, including both macro‐ and microscaled pores, are produced by the combination of direct ink writing (DIW), acid etching, and phase inversion. The thixotropic inks in DIW are formulated by a simple one‐pot process to disperse duo nanoparticles (nanoclay and silica nanoparticles) in a polyurethane suspension. The resulting lightweight foam exhibits tailorable mechanical strength, unprecedented elasticity (standing over 1000 compression cycles), and remarkable robustness (rapidly and fully recover after a load more than 20 000 times of its own weight). Surface coating of carbon nanotubes yields a conductive elastic foam that can be used as piezoresistivity sensor with high sensitivity. For the first time, this strategy achieves 3D printing of elastic foam with controlled multilevel 3D structures and mechanical/conductive properties. Moreover, the facile ink preparation method can be utilized to fabricate foams of various materials with desirable performance via 3D printing.     

The rapid detection for methane of ZnO porous nanoflakes with the decoration of Ag nanoparticles

Realizing the real-time detection of CH₄ is important for the safety of human life. A facile hydrothermal method was used to synthesize Ag nanoparticles-decorated ZnO porous nanoflakes (PNFs) in this study. The characterization results confirmed that Ag nanoparticles had been decorated in ZnO nanoflakes with the thickness of ~10 nm. The gas-sensing properties of Ag-decorated ZnO nanoflakes were also investigated. While the gas-sensing performances of ZnO were remarkably improved by decorating Ag nanoparticles on the surface of ZnO nanoflakes, the response of the Ag-decorated ZnO sensor to 3000 ppm CH₄ is almost 1.3 times as high as that of pristine ZnO sensor. The obtained Ag/ZnO sensor exhibits better long-term stability and shorter response recovery time (5/38 s) in the comparison with pristine ZnO, demonstrating the possibility for the actual detection of CH₄. The enhanced CH₄ sensing performance can be attributed to the synergism between the unique hierarchical porous structure and the sensitizing actions utilized by the Ag nanoparticles.