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摩擦:英文版 2023年第11期11卷 2091-2106页

作者： Yongjun zhou yuanya ZHANG Meng LIU Yanling WANG Junya yuan Xuehu MEN School of Materials and Energy Lanzhou UniversityLanzhou 730000China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of SciencesLanzhou 730000China center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

Epoxy resin(EP)composites with satisfactory thermal and tribological performance are highly required for engineering moving ***,the simple addition of fillers leaded to the serious filler agglomeration and limited promotion in tribological *** this work,we constructed a new kind of three-dimensional(3D)reduced graphene oxide(RGO)/Si_(3)N_(4) hybrid aerogel for EP composites,which was prepared by a facile hydrothermal self-assembly method followed by freeze-drying *** a result,the dispersibility of Si_(3)N_(4) whiskers was greatly improved through wrapping of polydopamine–polyethyleneimine copolymer(PDA–pei)copolymer and physical spacing of 3D ***,benefiting from the synergistic effect of RGO and Si_(3)N_(4)@PDA–pei in the thermal network,the thermal conductivity of RGO/Si_(3)N_(4) hybrid aerogel(GSiA)–EP increased by 45.4%compared to that of the neat *** addition,the friction coefficient and wear rate of GSiA–EP decreased by 83.7%and 35.8%,*** work is significant for opening a tribological performance enhancement strategy though constructing 3D hybrid architecture.

关键词： three-dimensional(3D)hybrid structure reduced graphene oxide(RGO)/Si_(3)N_(4)hybrid aerogel thermal properties tribological properties wear mechanism synergistic effect

Effects of slow or controlled release fertilizer types and fertilization modes on yield and quality of rice

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Journal of Integrative Agriculture 2018年第10期17卷 2222-2234页

作者： WEI Hai-yan CHEN Zhi-feng XING Zhi-peng zhou Lei LIU Qiu-yuan ZHANG Zhen-zhen JIANG Yan HU Ya-jie ZHU Jin-yan CUI {2.-yuan DAI Qi-gen ZHANG Hong-cheng Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation center for Modem Production Technology of Grain Crops/Agricultural College Yangzhou University Yangzhou 225009 P.R.China

There is limited information about the influence of slow or controlled release fertilizer（S/CRF） on rice yield and quality. In this study, japonica rice cultivar Nanjing 9108 was used to study the effects of three different S/CRFs（polymer-coated urea（PCU）, sulfur-coated urea（SCU）, and urea formaldehyde（UF）） and two fertilization modes（both S/CRF and common urea（CU） as basal fertilizer, S/CRF as basal and CU as tillering fertilizer） on rice yield and quality. CU only was applied separately as control（CK）. Results showed that, rice grain yield, chalky kernel rate, chalky area, overall chalkiness, and the content of gliadin, glutenin, and protein, all showed the trends of UF〉PCU〉SCU within the same fertilization mode, and showed the trends of S/CRF as basal and CU as tillering fertilizer〉both S/CRF and CU as basal fertilizer within the same type of S/CRF. In contrast, the contents of amylose, amylopectin, and starch, as well as taste value, and peak and hot viscosity showed trends of SCU〉PCU〉UF, and the trends of both S/CRF and CU as basal fertilizer〉S/CRF as basal and CU as tillering fertilizer. Among S/CRF treatments and fertilization modes, taste values of cooked rice were positively correlated with amylose, amylopectin, and starch contents, as well as gel consistency, peak viscosity, hot viscosity, and cool viscosity, while negatively correlated with globulin, gliadin, glutenin, and protein contents. The types of S/CRF and fertilization modes are important for improving rice yield and quality. Compared to CK, higher yield and similar quality of rice was achieved with UF as basal and CU as tillering fertilizer, and similar yield with improved appearance and eating and cooking quality of rice was achieved with either both UF and CU as basal fertilizer, or PCU as basal and CU as tillering fertilizer.

关键词： type of slow or controlled release fertilizer fertilization mode yield quality

Effects of pore size,mesostructure and aluminum modification on FDU-12.supported NiMo catalysts for hydrodesulfurization

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Petroleum Science 2020年第6期17卷 1737-1751页

作者： pei yuan Xue-Qin Lei Hong-Ming Sun Hong-Wei Zhang Chun-Sheng Cui yuan-yuan Yue Hai-Yan Liu Xiao-Jun Bao Ting-Hai Wang National Engineering Research center of Chemical Fertilizer Catalyst School of Chemical EngineeringFuzhou UniversityFuzhou 350002People’s Republic of China State Key Laboratory of Heavy Oil Processing China University of PetroleumBeijing 102249People’s Republic of China

A series of NiMo/FDU-12.catalysts with tunable pore diameters and mesostructures have been controllably synthesized by adjusting the synthetic hydrothermal temperature and applied for the hydrodesulfurization of dibenzothiophene and its *** state-of-the-art electron tomography revealed that the pore sizes of FDU-12.supports were enlarged with the increase in the hydrothermal temperature and the mesostructures were transformed from ordered cage-type pores to locally disordered ***,the MoS2.morphology altered from small straight bar to semibending arc to spherical shape and finally to larger straight bar with the change of support *** them,FDU-12.hydrothermally treated at 150℃possessed appropriate pore diameter and connected pore structure and was favorable for the formation of highly active MoS2.with curved morphology;thus,its corresponding catalyst exhibited the best HDS ***,it was indicated that the isomerization pathway could be significantly improved for HDS of 4,6-dimethyldibenzothiophene after the addition of aluminum,which was expected to be applied to the removal of the macromolecular sulfur *** study sheds lights on the relationship between support effect,active sites morphology and HDS performance,and also provides a guidance for the development of highly active HDS catalysts.

关键词： Mesostructure FDU-12 NiMo catalysts Hydrodesulfurization Al modification

A general and transferable deep learning framework for predicting phase formation in materials

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npj Computational Materials 2021年第1期7卷 89-98页

作者： Shuo Feng Huadong Fu Huiyu zhou yuan Wu Zhaoping Lu Hongbiao Dong School of Engineering University of LeicesterLeicester LE17RHUK beijing Advanced Innovation center of Materials Genome Engineering University of Science and Technology Beijing100083 BeijingChina School of Informatics University of LeicesterLeicester LE17RHUK State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing100083 BeijingChina

Machine learning has been widely exploited in developing new ***,challenges still exist:small dataset is common for most tasks;new datasets,special descriptors and specific models need to be built from scratch when facing a new task;knowledge cannot be readily transferred between independent *** this paper we propose a general and transferable deep learning(GTDL)framework for predicting phase formation in *** proposed GTDL framework maps raw data to pseudoimages with some special 2.D structure,e.g.,periodic table,automatically extracts features and gains knowledge through convolutional neural network,and then transfers knowledge by sharing features extractors between *** of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems;for high-entropy alloys the GTDL framework can discriminate five types phases(BCC,FCC,HCP,amorphous,mixture)with accuracy and recall above 94%in fivefold *** addition,periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small *** method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.

关键词： materials alloys alloy

基于全基因组测序分析1起产气荚膜梭菌导致的腹泻暴发事件

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中国人兽共患病学报 2024年第8期40卷 758-762,773页

作者：闫爱霞潘艳艳康颖李首飞王苗王洛桐王园园刘雨薇李颖黄振洲北京市顺义区疾病预防控制中心北京101300 杭州市疾病预防控制中心杭州310021

目的对1起产气荚膜梭菌导致腹泻暴发事件进行病原学分析。方法采集病例肛拭子样本和环境涂抹样本,肛拭子样本增菌前后分别进行产气荚膜梭菌plc和cpe基因荧光PCR检测和产气荚膜梭菌分离培养,对分离菌落进行全自动生化鉴定和飞行时间质谱... 详细信息

目的对1起产气荚膜梭菌导致腹泻暴发事件进行病原学分析。方法采集病例肛拭子样本和环境涂抹样本,肛拭子样本增菌前后分别进行产气荚膜梭菌plc和cpe基因荧光PCR检测和产气荚膜梭菌分离培养,对分离菌落进行全自动生化鉴定和飞行时间质谱鉴定。对鉴定为产气荚膜梭菌的分离株进行全基因组测序,分析菌株携带毒力基因和耐药基因情况,基于全部分离株核心基因组单核苷酸多态性进行遗传聚集性分析。结果未增菌的病例肛拭子cpe和plc基因检出率分别为46.15%(6/13)和53.85%(7/13),基于BHI厌氧增菌后病例肛拭子cpe和plc基因检出率分别为38.46%(5/13)和53.85%(7/13);10名病例肛拭子分离到产气荚膜梭菌,均为F生物型(携带毒力基因分布plc+/cpb-/etx-/iA-/cpe+/cpb2./netB-),10个分离株基于cgSNP构建的聚类树形成2.相互独立且遗传距离较远的分支,Lineage 1仅分布1株,为ST589,携带耐药基因为erm(Q),Lineage 2.布9株,为ST149,携带耐药基因为tetB(P),为一组高度克隆化的菌株。结论本次暴发事件由F型产气荚膜梭菌所导致,且多数病例感染一组cpe+高度克隆化菌株。全基因组测序技术可应用于产气荚膜梭菌暴发事件病原学分析,基于肛拭子样本的产气荚膜梭菌增菌方法和分子筛查方法有待开发和应用。

关键词：产气荚膜梭菌 cpe基因全基因组测序腹泻

医学模拟技术在机动卫勤分队训练中的应用现状、存在问题及建议

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医疗卫生装备 2024年第10期45卷 88-92页

作者：黄俊翔辛培源张永顺刘征宇方科陆洲解放军94647部队医院福州350026 中部战区空军医院麻醉科山西大同038000 空军军医大学基础医学院西安710032 解放军95968部队卫生队北京100043 空军军医大学卫勤训练基地卫生勤务教研室西安710038

介绍了医学模拟技术在外军卫勤领域的应用现状,阐述了医学模拟技术在我军机动卫勤分队训练中的应用现状及存在问题,提出了健全医学模拟管理体系建设、优化人才队伍管理培养模式和推动医学模拟系统标准化模块化建设等建议,为促进我军各... 详细信息

介绍了医学模拟技术在外军卫勤领域的应用现状,阐述了医学模拟技术在我军机动卫勤分队训练中的应用现状及存在问题,提出了健全医学模拟管理体系建设、优化人才队伍管理培养模式和推动医学模拟系统标准化模块化建设等建议,为促进我军各级卫勤机构及机动卫勤分队实战化组训水平和卫勤保障能力的提升提供了参考。

关键词：医学模拟技术医学模拟训练卫勤分队训练卫勤保障

Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm

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World Journal of Gastroenterology 2021年第21期27卷 2910-2920页

作者： Ji-yuan zhou Liu-Wei Song Rong yuan Xiao-Ping Lu Gui-Qiang Wang Department of Gastroenterology The Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhou 510260Guangdong ProvinceChina School of Public Health Xiamen UniversityXiamen 361005Fujian ProvinceChina Intervention and Cell Therapy center Peking University Shenzhen HospitalShenzhen 518036Guangdong ProvinceChina Department of Infectious Disease Peking University First HospitalBeijing 100034China

BACKGROUND Persistent liver inflammatory damage is the main risk factor for developing liver fibrosis,cirrhosis,and even hepatocellular carcinoma in chronic hepatitis B(CHB)***,accurate prediction of the degree of liver inflammation is a high priority and a growing medical *** To build an effective and robust non-invasive model for predicting hepatitis Brelated hepatic *** A total of 650 treatment-naïve CHB(402.HBeAg-positive and 2.8 HBeAgnegative)patients who underwent liver biopsy were enrolled in this *** inflammation grading was assessed by the Ishak scoring *** quantitative hepatitis B core antibody(qAnti-HBc)levels and 2. immunerelated inflammatory factors were measured quantitatively using a chemiluminescent microparticle immunoassay.A backward feature elimination(BFE)algorithm utilizing random forest(RF)was used to select optional features and construct a combined *** diagnostic abilities of the model or variables were evaluated based on the estimated area under the receiver operating characteristics curve(AUROC)and compared using the DeLong *** Four features were selected to predict moderate-to-severe inflammation in CHB patients using the RF-BFE *** predictive features included qAnti-HBc,ALT,AST,and ***’s correlation analysis indicated that serum qAnti-HBc,ALT,AST,and CXCL11 levels were positively correlated with the histology activity index(HAI)*** selected features were incorporated into the model to establish a novel model named I-3A *** AUROC[0.82.;95%confidence interval(CI):0.790-0.851]of the I-3A index was significantly increased compared with qAnti-HBc alone(0.760,95%CI:0.72.-0.792.P<0.0001)in all CHB *** use of an I-3A index cutoff value of 0.41 produced a sensitivity of 69.17%,specificity of 81.44%,and accuracy of 73.8%.Additionally,the I-3A index showed significantly improved diagnostic performance for predicting moderate-to-severe i

关键词： Hepatic inflammation Machine learning Quantitative hepatitis B core antibody CXCL11 Diagnostic efficiency

Intrinsic emission and tunable phosphorescence of perfluorosulfonate ionomers with evolved ionic clusters

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Science china Chemistry 2020年第6期63卷 833-840页

作者： Qing zhou Jia Cui Tianjia Yang Changlu Hu Zihao Zhong Zhenhua Sun Yongyang Gong Supeng pei Yongming Zhang School of Chemistry and Chemical Engineering Shanghai Key Lab of Electrical Insulation and Thermal AgingShanghai Electrochemical Energy Devices Research CenterCenter of Hydrogen ScienceShanghai Jiao Tong UniversityShanghai 200240China School of Chemical and Environmental Engineering Shanghai Institute of TechnologyShanghai 201418China Petrochina Petrochemical Research Institute Beijing 102299China College of Materials Science and Engineering Guilin University of TechnologyGuilin 541004China

Intrinsic emission from unorthodox luminogens without traditional conjugated building blocks is drawing increasing ***,the emission mechanism is still ***,we demonstrate the intriguing emission from perfluorosulfonate ionomers(PFSIs),which can be explained by the clustering triggered emission(CTE)*** being free of any conventional chromophores,PFSIs exhibit bright emission and multi-color phosphorescence(77 K)in concentrated solutions,powders and membranes with obvious aggregation-induced emission(AIE)*** sulfonic acids are responsible for the light emission,and their connection and evolution are deeply explored via X-ray diffraction(XRD)and small angel X-ray scattering(SAXS),in which the electron overlap determined by the clustered status results in the extended conjugation and simultaneously rigidified *** results demonstrate that it is feasible to use fluorescence analysis to explore the ionic cluster structure and evolution of PFSI,and it can be applied in the pure organic luminescent field as well.

关键词： nonconventional luminogens clustering-triggered emission tunable phosphorescence perfluorosulfonate ionomers ion clusters

Integration, Quality Assurance, and Usage of Global Aircraft Observations in CRA

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Journal of Meteorological Research 2021年第1期35卷 1-16页

作者： Jie LIAO Huiying WANG Zijiang zhou Zhiquan LIU Lipeng JIANG Fang yuan National Meteorological Information center China Meteorological AdministrationBeijing 100081China National center for Atmospheric Research CO 80301USA

This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for china's first generation global atmospheric reanalysis(CRA) product(1979–2.18). An integration method named "classified integration" is developed. Aircraft observations from nine different sources are integrated into the Integrated Global Meteorological Observation Archive from Aircraft(IGMOAA), a new dataset from the National Meteorological Information center(NMIC) of the china Meteorological Administration(CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 h Pa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis(CFSR) of NCEP,the observation number of IGMOAA increased by 12.9% between 2.10 and 2.14, mainly as a result of adding more Chinese Aircraft Meteorological Data Relay(AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2.18 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2.18 and are real-time updated at the CMA Data-as-a-Service(CMADaa S) platform. For CRA, the fits to observations improve over time. From 1994 to 2.18, root-meansquare error(RMSE) of observations relative to CRA background decreases from 1.8 to 1.0℃ for temperature above 300 h Pa, and from 4.5 to 3 m s^(-1) for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.

关键词： aircraft observation integration quality assurance blacklist china's global atmospheric reanalysis(CRA)product

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

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Engineering 2019年第1期5卷 156-163页

作者： Yufei Liu yuan zhou Xin Liu Fang Dong Chang Wang Zihong Wang College of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Public Policy and Management Tsinghua University Beijing 100084 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China center for Strategic Studies Chinese Academy of Engineering Beijing 100088 China

It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.

关键词： Artificial intelligence Generative adversarial network Deep neural network Small sample size Cancer