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Synthesis, UV-curing Behavior and Surface Properties of New Fluorine-containing Aromatic Oxetane Monomers

Chinese Journal of Polymer Science 2018年第4期36卷 521-527页

作者： Shu-Qing Fang Yu-Lian Pang ying-quan zou College of Chemistry Beijing Normal University

In this paper, we combined high-end cationic UV-curable material with fluorinated chain obtaining a series of new fluorine-containing aromatic oxetane monomers via a mild nucleophilic substitution reaction. The structures and properties of monomers were characterized using ~1 H-NMR, ^（19） F-NMR, dynamic viscosity tests and differential scanning calorimetry（DSC）. It was determined that all of the fluorinated monomers obtained had much lower viscosity and higher thermostability after the introduction of hexafluorobenzene. Then, UV-curable coatings were prepared using four fluorine-containing aromatic oxetane monomers（FOX1-4）; the UV-curing kinetics, with three kinds of initiators, and properties of the cured films were evaluated using real-time Fourier transform infrared（FTIR） spectroscopy, water and diiodomethane contact angle tests, surface energy calculations and scanning electron microscopy（SEM）. The FTIR spectroscopy results showed that the coatings possessed excellent conversion rate（〉 99% with liquid initiator PAG-201 in 150 s）, and as the fluorine content increased, the monomers exhibited decreased mobility with the increasing viscosity and worse solubility with fluorinated monomers, resulting in a lower conversion rate. Moreover, the coatings possessed favorable hydrophobic and oleophobic properties and low surface energies owing to the fluoride chains floating to the membrane-air interface, which was also confirmed by discrete concave structures in SEM images. These new kinds of monomers can replace traditional fluorinated cationic monomers applied to the fingerprint resistant, fouling resistant, scratch resistant and anti-aging coatings, adhesives or printing ink materials.

关键词： Fluorine-containing monomers Oxetane UV-curing kinetics Surface properties

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A novel strategy to prepare LDH networks loaded carbon structure by C-MEMS techniques for glucose detection

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Chinese Chemical Letters 2017年第1期28卷 149-152页

作者： Bo Hai ying-quan zou Gui-Bao Guo Zheng-De Wang Yun-ying Liu Xiao-Xia Wang Hui Yan Li-Tong Ma Yu-Chen Bai School of Chemistry and Chemical Engineering Inner Mongolia University of Science & Technology College of Chemistry Beijing Normal University

NiAl-layered double hydroxide（NiAl-LDH） networks loaded carbon microcylinder（CMC） hybrid was synthesized for the first time using typical carbon based microelectromechanical systems（C-MEMS）techniques combined with in situ growth progress. The incorporation of NiAl-LDH on C-MEMS structures via a simple pyrolysis of modified photoresist was investigated. With proper control of parameters in lithography and hydrothermal processes, the NiAl-LDH/CMC composites with suitable morphology were fabricated. When the composites applied as new catalytic material for glucose detection, this simple sensor showed satisfying electrocatalytic properties towards glucose oxidation owing to its unique structure and excellent electric conductivity. It is also worth pointing out that this novel fabrication process can equip carbon microfeatures with various nanostructures, and have wide potential applications in scaling up carbon based nanocomposites.

关键词： Lithography Incorporation C-MEMS techniques NiAl-layered double hydroxide Sensor

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou ying Liang Zhongyue zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and Automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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Unraveling the mechanism of action of Shangxia Liangji formula for treating insomnia:a metabolomics and network pharmacology approach

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Traditional Medicine Research 2025年第2期10卷 16-29页

作者： Xia-Jie quan Hao Liang Yong-Hong Tang Li Jiang Xiong-ying Ji Feng-ying Zhang Ping Zhang Bo Ouyang Department of Information The Affiliated Nanhua HospitalHengyang Medical SchoolUniversity of South ChinaHengyang 421002China Department of Neurology The Affiliated Nanhua HospitalHengyang Medical SchoolUniversity of South ChinaHengyang 421002China Department of Gastroscopy The Affiliated Nanhua HospitalHengyang Medical SchoolUniversity of South ChinaHengyang 421002China Department of Traditional Chinese Medicine The Affiliated Nanhua HospitalHengyang Medical SchoolUniversity of South ChinaHengyang 421002China

Background:Insomnia is a prevalent clinical condition and Shangxia Liangji formula(SXLJF)is a well-established method of ***,the specific mechanism of action of SXLJF remains ***:The mouse model of insomnia was established by intraperitoneal injection of ***-two mice were randomly divided into a negative control group,model group,SXLJF group(18.72 g/kg/day),and positive control group(diazepam,2 mg/kg)and treated with the corresponding drugs for 7 consecutive *** open field test and pentobarbital-induced sleeping test were ***-MS-based untargeted metabolomics and network pharmacology were applied to explore the potential targets of SXLJF for treating ***,key targets were validated using ***:Behavioral tests demonstrated that SXLJF reduced the total distance,average velocity,central distance,and sleep latency,and prolonged sleep *** and network pharmacology revealed potential targets,signaling pathways,metabolic pathways,and metabolites associated with the anti-insomnia effects of ***,tyrosine hydroxylase(TH)and tyrosine metabolism emerged as crucial metabolic pathways and targets,***-qPCR results supported the role of TH in the mechanism of SXLJF in treating ***:In conclusion,TH and tyrosine metabolism may represent significant targets and pathways for SXLJF in treating insomnia.

关键词： Shangxia Liangji formula insomnia metabolomics network pharmacology tyrosine hydroxylase tyrosine metabolism

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空间异形钢结构拱桥拱脚局部应力分析

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山西建筑 2025年第1期51卷 154-157页

作者：邹颖深圳市市政设计研究院有限公司广东深圳518029

空间异形钢结构拱桥设计计算比较复杂,以锦言大桥为例,对空间异形钢结构拱桥拱脚钢混结合段进行有限元建模分析,采用板单元模拟钢结构,采用实体单元模拟混凝土结构,采用线单元模拟预应力钢束。根据桥梁总体结构模型最不利作用组合计算... 详细信息

空间异形钢结构拱桥设计计算比较复杂,以锦言大桥为例,对空间异形钢结构拱桥拱脚钢混结合段进行有限元建模分析,采用板单元模拟钢结构,采用实体单元模拟混凝土结构,采用线单元模拟预应力钢束。根据桥梁总体结构模型最不利作用组合计算结果获取钢拱肋在拱脚位置结构受力,通过对拱脚相关构件进行分析计算,提出拱脚的设计思路及施工注意事项,为相关桥梁提供一些参考价值。

关键词：空间异形钢结构拱桥拱脚有限元分析预应力钢束

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谱系1和8重组猪繁殖与呼吸综合征病毒的分离鉴定与遗传进化分析

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动物医学进展 2025年第1期46卷 29-37页

作者：邹舟黎颖韦莹莹刘惠僮韦祖樟广西大学动物科学技术学院动物传染病与分子免疫学实验室广西南宁530005 广西壮族自治区兽用生物制品工程研究中心广西南宁530005 广西畜禽繁育与疾病防控重点实验室广西南宁530004

2023年从广西疑似发生猪繁殖与呼吸综合征病毒(PRRSV)感染猪场的病猪肺脏组织中分离到1株PRRSV,将其命名为GXWZ20230831。采用RT-PCR扩增其全基因组序列并测序,分析遗传进化特点。结果显示,GXWZ20230831能够在Marc-145细胞上增殖,且产... 详细信息

2023年从广西疑似发生猪繁殖与呼吸综合征病毒(PRRSV)感染猪场的病猪肺脏组织中分离到1株PRRSV,将其命名为GXWZ20230831。采用RT-PCR扩增其全基因组序列并测序,分析遗传进化特点。结果显示,GXWZ20230831能够在Marc-145细胞上增殖,且产生明显的细胞病变;该毒株全基因组长度为15019个核苷酸(nt),与基因2型PRRSV谱系1 NADC30-like毒株的同源性最高,为94.0%,与基因1型PRRSV代表毒株Lelystad virus同源性最低,仅为60.0%;通过Nsp2氨基酸序列比对,结果发现GXWZ20230831存在NADC30-like典型的131(111+1+19)个氨基酸的不连续缺失模式;与美洲型代表株VR2332相比,GXWZ20230831的GP5蛋白在非中和抗原表位发生了V27A、V185A以及R191K 3处突变,同时在GP5蛋白中存在4个潜在的N-糖基化位点;构建的全基因组序列、nsp2基因、ORF5基因遗传进化树均显示该毒株处于谱系1的分支;重组分析表明,GXWZ20230831是1株以谱系1经典毒株NADC30为骨架,谱系8毒株提供重组片段的重组毒株,重组断点位于基因组的nsp1(458-1821 nt)和Nsp4(5405-8179 nt)中。研究结果可为掌握广西地区的PRRSV遗传变异规律提供参考,为制定防控措施提供科学依据。

关键词：猪繁殖与呼吸综合征病毒分离鉴定重组遗传进化分析

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A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

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Chinese Journal of Electronics 2021年第5期30卷 815-823页

作者： WANG Chao zou quan Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Hainan Key Laboratory for Computational Science and Application Hainan Normal University

Several Coronaviruses(CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM(Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV(HCoV) and Nonhuman-infective CoV(Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

关键词： Spike protein NonHCoVs genetics microorganisms Support vector machine mortality Spike-SVM model Human-infective CoV severe respiratory syndrome machine learning method biology computing predicting Human-infective coronavirus diseases proteins HCoV human infection differentiating Nonhuman-infective CoV infection risk CoVs discriminate HCoVs learning (artificial intelligence) bioinformatics support vector machines informative features significant morbidity molecular biophysics early warning system epidemic pathogens Coronaviruses

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Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level

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Research 2023年第4期2023卷 145-162页

作者： Ren Qi quan zou Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China School of Life Science and Technology University of Electronic Science and Technology of ChinaChengduChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina.

Cancer treatments always face challenging problems,particularly drug resistance due to tumor cell *** existing datasets include the relationship between gene expression and drug sensitivities;however,the majority are based on tissue-level *** drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative ***,machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene *** modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer *** this paper,we review machine learning and deep learning approaches in drug *** analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis,we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell *** anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

关键词： drugs expression overcome

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Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

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Frontiers of Computer Science 2024年第2期18卷 217-229页

作者： Yizheng WANG Xin ZHANG ying JU Qing LIU quan zou Yazhou ZHANG Yijie DING ying ZHANG Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Beidahuang Industry Group General Hospital Harbin 150088China School of Informatics Xiamen UniversityXiamen 361005China Department of Anesthesiology Hospital(T.C.M)Affiliated to Southwest Medical UniversityLuzhou 646000China Software Engineering College Zhengzhou University of Light IndustryZhengzhou 450002China

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been *** developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model *** constructed three kernels in miRNA and disease space and conducted kernel fusion using *** propagation uses matrix factorization and matrix approximation to effectively reduce computation and time *** results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

关键词： human miRNA-disease association multiple kernel learning link propagation miRNA similarity disease similarity

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Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

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Defence Technology（防务技术） 2024年第9期39卷 147-156页

作者： Kai Wei Longmiao Chen quan zou School of Mechanical Engineering NanJing University of Science and TechnologyNanjing210094China

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

关键词： Chain shell magazine Full system dynamics estimation Disturbance estimation Parameter estimation Adaptive extended state observer

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