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Fluorescent Nile blue-functionalized poly(N-isopropylacrylamide)microgels responsive to temperature and polyamines

SmartMat 2024年第2期5卷 156-168页

作者： qiming Shen Changhao fang Liang Hu Michael J.Serpe Department of Chemistry University of AlbertaEdmontonAlbertaCanada State Key Laboratory of Radiation Medicine and Protection Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutionsand School for Radiological and Interdisciplinary Sciences(RAD-X)Soochow UniversitySuzhouChina

Fluorescent poly(N-isopropylacrylamide-co-Nile blue)(pNIPAm-co-NB)microgels were synthesized that exhibited fluorescence intensity changes in a water temperature-dependent fashion.NB is well known to exhibit fluorescence intensity that depends on the hydrophobicity of the environment,while pNIPAm-based microgels are well known to transition from swollen(hydrophilic)to collapsed(relatively hydrophobic)at temperatures greater than 32℃;hence,we attribute the above behavior to the hydrophobicity changes of the microgels with increasing temperature.This phenomenon is ultimately due to NB dimers(relatively quenched fluorescence)being broken in the hydrophobic environment of the microgels leading to relatively enhanced fluorescence.We went on to show that the introduction of cucurbit[7]uril(CB[7])into the pNIPAm-co-NB microgels enhanced their fluorescence allowing them to be used for polyamine(e.g.,spermine[SPM])detection.Specifically,CB[7]forms a host–guest interaction with NB in the microgels,which prevents NB dimerization and enhances their fluorescence.When SPM is present,it forms a host–guest complex that is favored over the CB[7]-NB host–guest interaction,which frees the NB for dimerization and leads to fluorescence quenching.As a result,we could generate an SPM sensor capable of SPM detection down to~0.5µmol/L in complicated matrixes such as serum and urine.

关键词： cucurbituril fluorescence pNIPAm-co-NB microgel spermine detection thermoresponsive

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混频数据驱动的电子废弃物生成量时空演化预测

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管理工程学报 2024年第3期38卷 186-201页

作者：王方余乐安何昌华刘启明西安电子科技大学经济与管理学院陕西西安710126 四川大学商学院四川成都610065 滨州魏桥国科高等技术研究院山东滨州256600 哈尔滨工程大学经济管理学院黑龙江哈尔滨150001

本文针对电子废弃物生成量预测易受季节性、突变性和区域性等多重因素影响的问题,基于综合集成方法论的建模思想,构建了混频数据驱动的电子废弃物生成量时空演化预测框架。首先,本文集成GM(1,1)、支持向量回归、Holt-Winters和X12模型等... 详细信息

本文针对电子废弃物生成量预测易受季节性、突变性和区域性等多重因素影响的问题,基于综合集成方法论的建模思想,构建了混频数据驱动的电子废弃物生成量时空演化预测框架。首先,本文集成GM(1,1)、支持向量回归、Holt-Winters和X12模型等,实现了对电器电子产品低频年度销量值和高频季度销量占比值的预测,并以乘法运算得到了产品的季度销量预测值。然后,本文对特定电器电子产品的寿命期进行调研和拟合,利用经典市场供给模型,实现了对电子废弃物全国总体生成量的测算。最后,基于各地平均每百户产品拥有量、总户数和产品结构数据,本文构建区域配额估算模型,实现了电子废弃物全国总量向各地各区域的映射,为电子废弃物的时空间演化规律分析提供了依据。基于我国燃气热水器、太阳能热水器和手机的数据,本文验证了提出的新框架具有可行性和有效性,结果表明:(1)混频预测模型较经典模型(Holt-Winters、灰色波形预测和X12模型)能够获得更高的预测精度;(2)不同品类的电子废弃物(燃气热水器和手机)生成量呈现出了差异化的时空演化特征。

关键词：混频数据电子废弃物季节性预测时空演化

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li qiming fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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纳米钨在高压下的物理力学行为与尺寸效应研究

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高压物理学报 2024年第3期38卷 29-36页

作者：赵康林王齐明张友君蒋刚彭放李延春四川大学原子与分子物理研究所四川成都610065 中国科学院高能物理研究所北京100049

探究尺寸效应对材料高压物性的影响规律有助于开发具有新颖或者改良特性的新材料。采用金刚石对顶砧结合同步辐射X射线衍射技术,研究了平均晶粒尺寸分别为30和65 nm的多晶钨粉在高压下的静态压缩行为。通过分析每个压力点下X射线衍射图... 详细信息

探究尺寸效应对材料高压物性的影响规律有助于开发具有新颖或者改良特性的新材料。采用金刚石对顶砧结合同步辐射X射线衍射技术,研究了平均晶粒尺寸分别为30和65 nm的多晶钨粉在高压下的静态压缩行为。通过分析每个压力点下X射线衍射图谱的峰位和半高宽等,得到了纳米金属钨在高压下的晶胞体积、晶粒尺寸和微观应变等。通过拟合三阶Birch-Murnaghan方程,得到了30和65 nm钨的体弹模量,分别为257(7)GPa和343(8)GPa。结合前人的研究结果发现,当晶粒尺寸从微米降低至10 nm,钨的屈服强度逐渐增大,10 nm钨的屈服强度较微米晶样品的屈服强度提高了3.5倍;体弹模量呈现先增大后减小的趋势,30 nm钨的体弹模量较65 nm钨减小了25%。

关键词：状态方程屈服强度体弹模量高压纳米晶体

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Forecasting traffic flows in irregular regions with multi-graph

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1179-1193页

作者： Dewen SENG Fanshun LV Ziyi LIANG Xiaoying SHI qiming fang School of Computer Science and Technology Hangzhou DianziUniversityHangzhou 310018China

The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.

关键词： Traffic flow prediction Multi-graph convolutional network Gated recurrent unit Irregular regions

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MAML^(2):meta reinforcement learning via meta-learning for task categories

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Frontiers of Computer Science 2023年第4期17卷 57-67页

作者： qiming FU Zhechao WANG Nengwei fang Bin XING Xiao ZHANG Jianping CHEN School of Electronics and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Chongqing Industrial Big Data Innovation Center Co.Ltd. Chongqing 404100China School of Medical Informatics Xuzhou Medical UniversityXuzhou 221004China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou 215009China

Meta-learning has been widely applied to solving few-shot reinforcement learning problems,where we hope to obtain an agent that can learn quickly in a new task.However,these algorithms often ignore some isolated tasks in pursuit of the average performance,which may result in negative adaptation in these isolated tasks,and they usually need sufficient learning in a stationary task distribution.In this paper,our algorithm presents a hierarchical framework of double meta-learning,and the whole framework includes classification,meta-learning,and re-adaptation.Firstly,in the classification process,we classify tasks into several task subsets,considered as some categories of tasks,by learned parameters of each task,which can separate out some isolated tasks thereafter.Secondly,in the meta-learning process,we learn category parameters in all subsets via meta-learning.Simultaneously,based on the gradient of each category parameter in each subset,we use meta-learning again to learn a new metaparameter related to the whole task set,which can be used as an initial parameter for the new task.Finally,in the re-adaption process,we adapt the parameter of the new task with two steps,by the meta-parameter and the appropriate category parameter successively.Experimentally,we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set.Additionally,our algorithm presents a more rapid adaptation process within readaptation.Moreover,we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.

关键词： meta-learning reinforcement learning few-shot learning negative adaptation

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Zn/Co-ZIF衍生多孔碳钯基催化剂的制备及在甲酸制氢中的应用

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无机盐工业 2024年第6期56卷 147-152页

作者：刘青翠李云青庞瑞琪田亚萍陈一莹李芳李其明辽宁石油化工大学石油化工学院辽宁抚顺113001

通过浸渍还原法将Pd、Ag等锚定在经过HNO_(3)酸化和(NH_(4))_(2)HPO_(4)磷化处理得到的Zn/Co-ZIF衍生HPNC多孔碳上,制备出高活性的Ag_(1)Pd_(7)@HPNC催化剂。采用XRD、TEM、SEM和EDS等系列表征手段对Zn/CoZIF、多孔碳载体和制备的催化... 详细信息

通过浸渍还原法将Pd、Ag等锚定在经过HNO_(3)酸化和(NH_(4))_(2)HPO_(4)磷化处理得到的Zn/Co-ZIF衍生HPNC多孔碳上,制备出高活性的Ag_(1)Pd_(7)@HPNC催化剂。采用XRD、TEM、SEM和EDS等系列表征手段对Zn/CoZIF、多孔碳载体和制备的催化剂进行了表征分析和系统测试。结果表明,多孔碳中的Zn和大部分的Co物种可通过高温焙烧和酸化处理方式被有效去除,小尺寸的Pd和Ag纳米颗粒可高效负载于HPNC多孔碳载体的表面。通过探究不同载体及银钯不同的质量比对催化剂催化活性的影响,证明了Ag_(1)Pd_(7)@HPNC催化剂具有优异的甲酸催化性能。甲酸分解实验表明,经(NH_(4))_(2)HPO_(4)处理后的Pd@HPNC催化剂活性优于未处理的Pd@HNC催化剂活性。掺杂Ag会进一步提高Ag_(x)Pd_(y)@HPNC(x>0)双金属催化剂的催化活性,其中当m_(Ag)∶m_(Pd)=1∶7时,Ag_(1)Pd_(7)@HPNC催化甲酸分解活性最佳,在318 K条件下甲酸分解的转化频率(TOF)可达1025 h^(-1);并且该催化剂循环使用5次后TOF仍保持为920 h^(-1),表明其具有良好的循环稳定性。Ag_(1)Pd_(7)@HPNC在甲酸催化脱氢方面展示了较好的应用前景。

关键词：甲酸制氢 ZIF 钯基化剂多孔碳

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煤中氟氯自动分析仪原理及在实验中的优劣分析

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化工管理 2024年第12期 38-41页

作者：康菲李起明高凯房立冬李芳张佳伟中国检验认证集团河北有限公司河北石家庄050000 沧州市政阳工程项目管理有限公司河北沧州061000

精确的仪器检测被称为实验室检测的发展方向,尤其是在有毒有害成分检测中,由设备代替人工是必然的发展趋势。三德FCl3000测定仪将高温水解和电位滴定结合在一起实现全自动化,在实验室中代替人工按照国家标准进行检测。文章通过介绍实验... 详细信息

精确的仪器检测被称为实验室检测的发展方向,尤其是在有毒有害成分检测中,由设备代替人工是必然的发展趋势。三德FCl3000测定仪将高温水解和电位滴定结合在一起实现全自动化,在实验室中代替人工按照国家标准进行检测。文章通过介绍实验设备原理以及将设备在工作中的表现和国家标准中规定方法的人工操作进行比对,包括适用范围、实验环境、实验时间、操作安全、结果准确度等方面,得到在操作时间和结果准确性方面设备逊于人工操作,在实验环境和安全性以及连贯性方面设备优于人工操作。通过以上研究使操作人员可以更多了解设备以得到准确可信的实验结果。

关键词：氟氯自动分析仪高温水解电位滴定

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang qiming Fu Nengwei fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were optimized.Moreover,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in TRNSYS.The results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2759-2785页

作者： Zhengkai Ding qiming Fu Jianping Chen You Lu Hongjie Wu Nengwei fang Bin Xing School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep reinforcement learning(DRL)methods have recently been proposed to address the HVAC control problem.However,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow convergence.In this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal comfort.MAQMC is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort violation.In addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

关键词： Deep reinforcement learning multi-zone residential HVAC multi-agent energy conservation comfort

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