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Magnitude, spatial patterns and drivers of methane fluxes from coastal salt marshes and mangrove forests in China

Science China Earth Sciences 2025年第3期68卷 850-866页

作者： yixuan yU Derrick y.F.LAI Minjie hu Chuan TONG Key Laboratory of humid Subtropical Eco-geographical Process of Ministry of Education Fujian Normal University School of Geographical Sciences Fujian Normal University Department of Geography and Resource Management The Chinese University of Hong Kong

Methane（CH4） is a highly potent greenhouse gas due to its high radiative forcing and global warming *** the magnitude, spatial patterns, and drivers of CH4emission from wetlands at the regional scale is crucial for formulating suitable strategies to mitigate future climate change. In this study, we synthesized the CH4flux data reported for major plant communities in the coastal salt marshes and mangrove forests in China and produced a spatial map of CH4fluxes from the mangrove sediments and salt marshes in China based on the results of machine learning models. Moreover, we examined the main drivers of CH4flux from these coastal wetlands and estimated the annual CH4emissions from the salt marshes and mangrove forests in different coastal provinces of China using a bottom-up approach. Our results showed that the mean CH4fluxes from the salt marsh and mangrove ecosystems in China were 2.22±0.29 and 2.26±0.55 mg m-2h-1, respectively. Across China, the total annual CH4emission from salt marshes was 31,179.75 Mg CH4a-1, while that from mangroves was 5,533.97 Mg CH4a-1with the sediments contributing to 12.7%(701.64 Mg CH4a-1) of the total efflux. The salt marshes and mangroves in China accounted for 2.75% and 0.14%–0.15% of the global annual CH4emissions from these two ecosystems,respectively. Moreover, we found that CH4emissions from the salt marshes and mangroves could offset the cooling impact arising from net carbon dioxide uptake by 88% and 53%, respectively, over a 100-year time scale. Mean annual temperature, soil moisture, and soil organic carbon content were the main factors controlling CH4emissions from these coastal ***, we found that CH4fluxes from the salt marshes in China decreased with latitude, which implied a possible increase in marsh CH4emission in response to global warming that should be considered carefully when developing nature-based climate solutions to achieve national goals for carbon neutrality.

关键词： Salt marsh Mangrove Methane flux Spatial pattern Driving factors China

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Amplitude analysis of the decays D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)

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Chinese Physics C 2024年第8期48卷 6-33页

作者： M.Ablikim M.N.Achasov P.Adlarson O.Afedulidis X.C.Ai R.Aliberti A.Amoroso Q.An y.bai O.bakina I.balossino y.ban H.-R.bao V.batozskaya K.begzsuren N.berger M.berlowski M.bertani D.bettoni F.bianchi E.bianco A.bortone I.boyko R.A.briere A.brueggemann H.Cai X.Cai A.Calcaterra G.F.Cao N.Cao S.A.Cetin J.F.Chang W.L.Chang G.R.Che G.Chelkov C.Chen C.H.Chen Chao Chen G.Chen H.S.Chen M.L.Chen S.J.Chen S.L.Chen S.M.Chen T.Chen X.R.Chen X.T.Chen y.b.Chen y.Q.Chen Z.J.Chen Z.y.Chen S.K.Choi X.Chu G.Cibinetto F.Cossio J.J.Cui H.L.Dai J.P.Dai A.Dbeyssi R.E.de boer D.Dedovich C.Q.Deng Z.y.Deng A.Denig I.Denysenko M.Destefanis F.De Mori b.Fang S.S.Fang W.X.Fang y.Fang y.Q.Fang R.Farinelli L.Fava F.Feldbauer G.Felici C.Q.Feng J.H.Feng y.T.Feng K.Fischer M.Fritsch C.D.Fu J.L.Fu y.W.Fu H.Gao y.N.Gao yang Gao S.Garbolino I.Garzia P.T.Ge Z.W.Ge C.Geng E.M.Gersabeck b.Ding X.X.Ding y.Ding y.Ding J.Dong L.y.Dong M.y.Dong X.Dong M.C.Du S.X.Du Z.H.Duan P.Egorov y.H.Fan J.Fang JA.Gilman K.Goetzen L.Gong W.X.Gong W.Gradl S.Gramigna M.Greco M.H.Gu y.T.Gu C.y.Guan Z.L.Guan A.Q.Guo L.b.Guo M.J.Guo R.P.Guo y.P.Guo A.Guskov J.Gutierrez K.L.Han T.T.Han X.Q.Hao F.A.Harris K.K.He K.L.He F.H.Heinsius C.H.Heinz y.K.Heng C.Herold T.Holtmann P.C.Hong G.y.Hou X.T.Hou y.R.Hou Z.L.Hou b.y.hu H.M.hu J.F.hu T.hu y.hu G.S.huang K.X.huang L.Q.huang X.T.huang y.P.huang T.hussain F.H\"olzken N.H\"usken N.in der Wiesche M.Irshad J.Jackson S.Janchiv J.H.Jeong Q.Ji Q.P.Ji W.Ji X.b.Ji X.L.Ji y.y.Ji X.Q.Jia Z.K.Jia D.Jiang H.b.Jiang P.C.Jiang S.S.Jiang T.J.Jiang X.S.Jiang y.Jiang J.b.Jiao J.K.Jiao Z.Jiao S.Jin y.Jin M.Q.Jing X.M.Jing T.Johansson S.Kabana N.Kalantar-Nayestanaki X.L.Kang X.S.Kang M.Kavatsyuk b.C.Ke V.Khachatryan A.Khoukaz R.Kiuchi O.b.Kolcu b.Kopf M.Kuessner X.Kui A.Kupsc W.K\"uhn J.J.Lane P.Larin L.Lavezzi T.T.Lei Z.H.Lei H.Leithoff M.Lellmann T.Lenz C.Li C.Li C.H.Li Cheng Li D.M.Li F.Li G.Li H.Li H.b.Li H.J.Li H.N.Li hui Li J.R.Li J.S.Li K.Li L.J.Li L.K.Li Lei Li M.H.Li P.R.Li Q.M.Li Q.X.Li R.Li S.X.Li T.Li W.D.Li W.G.Li X.Li X.H.Li X.L.Li X.y.Li y Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China beihang University Beijing 100191China bochum Ruhr-University D-44780 BochumGermany budker Institute of Nuclear Physics Sb RAS(bINP) Novosibirsk 630090Russia Carnegie Mellon University PittsburghPennsylvania 15213USA Central China Normal University Wuhan 430079China Central South University Changsha 410083China China Center of Advanced Science and Technology Beijing 100190China China University of Geosciences Wuhan 430074China Chung-Ang University Seoul06974Republic of Korea COMSATS University Islamabad Lahore CampusDefence RoadOff Raiwind Road54000 LahorePakistan Fudan University Shanghai 200433China GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291 DarmstadtGermany Guangxi Normal University Guilin 541004China Guangxi University Nanning 530004China Hangzhou Normal University Hangzhou 310036China Hebei University Baoding 071002China Helmholtz Institute Mainz Staudinger Weg 18D-55099 MainzGermany Henan Normal University Xinxiang 453007China Henan University Kaifeng 475004China Henan University of Science and Technology Luoyang 471003China Henan University of Technology Zhengzhou 450001China huangshan College Huangshan 245000China hunan Normal University Changsha 410081China hunan University Changsha 410082China Indian Institute of Technology Madras Chennai 600036India Indiana University BloomingtonIndiana 47405USA INFN Laboratori Nazionali di Frascati INFN Laboratori Nazionali di FrascatiI-00044FrascatiItaly INFN Laboratori Nazionali di Frascati INFN Sezione di PerugiaI-06100PerugiaItaly INFN Laboratori Nazionali di Frascati University of PerugiaI-06100PerugiaItaly INFN Sezione di Ferrara INFN Sezione di FerraraI-44122FerraraItaly INFN Sezione di Ferrara University of FerraraI-44122FerraraItaly Inner Mongolia University Hohhot 010021China Institute of Modern Physics Lanzhou 730000China Institute of Physics and Technology Peace Avenue 54BUlaanbaatar 13330Mongolia Instituto de A

Using e^(+)e^(−)annihilation data corresponding to an integrated luminosity of 2.93 fb^(−1)taken at the center-of-mass energy√s=3.773 GeV with the bESIII detector,a joint amplitude analysis is performed on the decays D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η).The fit fractions of individual components are obtained,and large interferences among the dominant components of the decays D^(0)→a_(1)(1260)π,D^(0)→π(1300)π,D^(0)→ρ(770)ρ(770),and D^(0)→2(ππ)_(S)are observed in both *** the obtained amplitude model,the CP-even fractions of D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η)are determined to be(75.2±1.1_(stat).±1.5_(syst.))%and(68.9±1.5_(stat).±2.4_(syst.))%,*** branching fractions of D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η)are measured to be(0.688±0.010_(stat.)±0.010_(syst.))%and(0.951±0.025_(stat.)±0.021_(syst.))%,*** amplitude analysis provides an important model for the binning strategy in measuring the strong phase parameters of D^(0)→4πwhen used to determine the CKM angleγ(ϕ_(3))via the b^(−)→DK^(−)decay.

关键词： bESIII D^(0)meson decays amplitude analysis CP-even fraction

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Improving Multi-task GNNs for Molecular Property Prediction via Missing Label Imputation

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Machine Intelligence Research 2025年第1期22卷 131-144页

作者： Fenyu hu Dingshuo Chen Qiang Liu Shu Wu University of Chinese Academy of Sciences Beijing100190China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of AutomationChinese Academy of SciencesBeijing100190China

The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there is a great need to develop the multi-task learning ability of ***,limited by expensive and time-consuming human annotations,collecting complete labels for each task is *** a result,most existing benchmarks involve many missing labels in training data,and the performance of GNNs is impaired due to the lack of sufficient supervision *** overcome this obstacle,we propose to improve multi-task molecular property prediction by missing label ***,a bipartite graph is first introduced to model the molecule-task co-occurrence ***,the imputation of missing labels is transformed into predicting missing edges on this bipartite *** predict the missing edges,a graph neural network is devised,which can learn the complex molecule-task co-occurrence *** that,we select reliable pseudo labels according to the uncertainty of the prediction *** with enough and reliable supervision information,our approach achieves state-of-the-art performance on a variety of real-world datasets.

关键词： Graph classification imbalance learning prediction bias mixture of experts multiview representations

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Recycling Fe and improving organic pollutant removal via in situ forming magnetic core-shell Fe_(3)O_(4)@CaFe-LDH in Fe(Ⅱ)-catalyzed oxidative wastewater treatment

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Journal of Environmental Sciences 2025年第1期147卷 523-537页

作者： Ting He Jie Sun Liangwei Deng Jialin Ming Changwei hu Key Laboratory of Green Chemistry and Technology Ministry of EducationCollege of ChemistrySichuan UniversityChengdu 610064China biogas Institute of Ministry of Agriculture and Rural Affairs Chengdu 610041China Inner Mongolia Key Laboratory of Fine Organic Synthesis Inner Mongolia UniversityHohhot 010021China

Due to its high efficiency,Fe(Ⅱ)-based catalytic oxidation has been one of the most popular types of technology for treating growing organic pollutants.A lot of chemical Fe sludge alongwith various refractory pollutantswas concomitantly produced,whichmay cause secondary environmental problemswithout proper *** here innovatively proposed an effective method of achieving zero Fe sludge,reusing Fe resources(Fe recovery=100%)and advancing organics removal(final TOC removal>70%)simultaneously,based on the in situ formation of magnetic Ca-Fe layered double hydroxide(Fe_(3)O_(4)@CaFe-LDH)***(Ca^(2+)and Fe^(3+))concentration(≥30 mmol/L)and their molar ratio(Ca:Fe≥1.75)were crucial to the success of the *** nano Fe_(3)O_(4)was designed to be involved in the Fe(Ⅱ)-catalytic wastewater treatment process,and was modified by oxidation intermediates/products(especially those with COO-structure),which promoted the co-precipitation of Ca^(2+)(originated from Ca(OH)_(2)added after oxidation process)and byproduced Fe^(3+)cations on its surface to in situ generate core-shell Fe_(3)O_(4)@*** oxidation products were further removed during Fe_(3)O_(4)@CaFe-LDH material formation via intercalation and *** applicable to many kinds of organicwastewater,such as bisphenol A,methyl orange,humics,and biogas *** prepared magnetic and hierarchical CaFe-LDH nanocomposite material showed comparable application performance to the recently reported *** work provides a new strategy for efficiently enhancing the efficiency and economy of Fe(Ⅱ)-catalyzed oxidative wastewater treatment by producing high value-added LDHs materials.

关键词： CaFe layered double hydroxide Fe(Ⅱ)-catalytic oxidation Fe recycling Magnetic nanomaterial in situ formation Fe_(3)O_(4)modification

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Measurement of integrated luminosity of data collected at 3.773 GeV by bESIII from 2021 to 2024

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Chinese Physics C 2024年第12期48卷 1-10页

作者： M.Ablikim M.N.Achasov P.Adlarson O.Afedulidis X.C.Ai R.Aliberti A.Amoroso Q.An y.bai O.bakina I.balossino y.ban H.-R.bao V.batozskaya K.begzsuren N.berger M.berlowski M.bertani D.bettoni F.bianchi E.bianco A.bortone I.boyko R.A.briere A.brueggemann H.Cai X.Cai A.Calcaterra G.F.Cao N.Cao S.A.Cetin J.F.Chang G.R.Che G.Chelkov C.Chen C.H.Chen Chao Chen G.Chen H.S.Chen H.y.Chen M.L.Chen S.J.Chen S.L.Chen S.M.Chen T.Chen X.R.Chen X.T.Chen y.b.Chen y.Q.Chen Z.J.Chen Z.y.Chen S.K.Choi G.Cibinetto F.Cossio J.J.Cui H.L.Dai J.P.Dai A.Dbeyssi R.E.de boer D.Dedovich C.Q.Deng Z.y.Deng A.Denig I.Denysenko M.Destefanis F.De Mori b.Ding X.X.Ding y.Ding y.Ding J.Dong L.y.Dong M.y.Dong X.Dong M.C.Du S.X.Du y.y.Duan Z.H.Duan P.Egorov y.H.Fan J.Fang J.Fang S.S.Fang W.X.Fang y.Fang y.Q.Fang R.Farinelli L.Fava F.Feldbauer G.Felici C.Q.Feng J.H.Feng y.T.Feng M.Fritsch C.D.Fu J.L.Fu y.W.Fu H.Gao X.b.Gao y.N.Gao yang Gao S.Garbolino I.Garzia L.Ge P.T.Ge Z.W.Ge C.Geng E.M.Gersabeck A.Gilman K.Goetzen L.Gong W.X.Gong W.Gradl S.Gramigna M.Greco M.H.Gu y.T.Gu C.y.Guan A.Q.Guo L.b.Guo M.J.Guo R.P.Guo y.P.Guo A.Guskov J.Gutierrez K.L.Han T.T.Han F.Hanisch X.Q.Hao F.A.Harris K.K.He K.L.He F.H.Heinsius C.H.Heinz y.K.Heng C.Herold T.Holtmann P.C.Hong G.y.Hou X.T.Hou y.R.Hou Z.L.Hou b.y.hu H.M.hu J.F.hu S.L.hu T.hu y.hu G.S.huang K.X.huang L.Q.huang X.T.huang y.P.huang y.S.huang T.hussain F.Hölzken N.Hüsken N.in der Wiesche J.Jackson S.Janchiv J.H.Jeong Q.Ji Q.P.Ji W.Ji X.b.Ji X.L.Ji y.y.Ji X.Q.Jia Z.K.Jia D.Jiang H.b.Jiang P.C.Jiang S.S.Jiang T.J.Jiang X.S.Jiang y.Jiang J.b.Jiao J.K.Jiao Z.Jiao S.Jin y.Jin M.Q.Jing X.M.Jing T.Johansson S.Kabana N.Kalantar-Nayestanaki X.L.Kang X.S.Kang M.Kavatsyuk b.C.Ke V.Khachatryan A.Khoukaz R.Kiuchi O.b.Kolcu b.Kopf M.Kuessner X.Kui N.Kumar A.Kupsc W.Kühn J.J.Lane L.Lavezzi T.T.Lei Z.H.Lei M.Lellmann T.Lenz C.Li C.Li C.H.Li Cheng Li D.M.Li F.Li G.Li H.b.Li H.J.Li H.N.Li hui Li J.R.Li J.S.Li K.Li L.J.Li L.K.Li Lei Li M.H.Li P.R.Li Q.M.Li Q.X.Li R.Li S.X.Li T.Li W.D.Li W.G.Li X.Li X.H.Li X.L.Li X.y.Li X.Z.Li y.G Institute of High Energy Physics Beijing 18049China beihang University Beijing 18191China bochum Ruhr-University D-44780 BochumGermany budker Institute of Nuclear Physics Sb RAS(bINP) Novosibirsk 630090Russia Carnegie Mellon University PittsburghPennsylvania 15213USA Central China Normal University Wuhan 430079China Central South University Changsha 41883China China Center of Advanced Science and Technology Beijing 18190China China University of Geosciences Wuhan 430074China Chung-Ang University Seoul06974Republic of Korea COMSATS University Islamabad Lahore CampusDefence RoadOff Raiwind Road54000 LahorePakistan Fudan University Shanghai 200433China GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291 DarmstadtGermany Guangxi Normal University Guilin 54184China Guangxi University Nanning 530004China Hangzhou Normal University Hangzhou 31836China Hebei University Baoding 07182China Helmholtz Institute Mainz Staudinger Weg 18D-55099 MainzGermany Henan Normal University Xinxiang 453007China Henan University Kaifeng 475004China Henan University of Science and Technology Luoyang 47183China Henan University of Technology Zhengzhou 450001China huangshan College Huangshan 245000China hunan Normal University Changsha 41881China hunan University Changsha 41882China Indian Institute of Technology Madras Chennai 600036India Indiana University BloomingtonIndiana 47405USA INFN Laboratori Nazionali di Frascati INFN Laboratori Nazionali di FrascatiI-00044FrascatiItaly INFN Laboratori Nazionali di Frascati INFN Sezione di PerugiaI-0618PerugiaItaly INFN Laboratori Nazionali di Frascati University of PerugiaI-0618PerugiaItaly INFN Sezione di Ferrara INFN Sezione di FerraraI-44122FerraraItaly INFN Sezione di Ferrara University of FerraraI-44122FerraraItaly Inner Mongolia University Hohhot 01821China Institute of Modern Physics Lanzhou 730000China Institute of Physics and Technology Peace Avenue 54BUlaanbaatar 13330Mongolia Instituto de Alta Investigación Universidad de Tarapacá

We present a measurement of the integrated luminosity of e^(+)e^(-)collision data collected by the bESIII detector at the bEPCII collider at a center-of-mass energy of Ecm=3.773 *** integrated luminosities of the datasets taken from December 2021 to June 2022,from November 2022 to June 2023,and from October 2023 to February 2024 were determined to be 4.995±0.019 fb^(-1),8.157±0.031 fb^(-1),and 4.191±0.016 fb^(-1),respectively,by analyzing large angle bhabha scattering *** uncertainties are dominated by systematic effects,and the statistical uncertainties are *** results provide essential input for future analyses and precision measurements.

关键词： bhabha scattering events integrated luminosity cross-section

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Optimizing the ORR performance of PtMnM ternary intermetallics by tuning the surface strain

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SCIENCE CHINA-CHEMISTRy 2025年第2期68卷 536-542页

作者： Song, Min Luo, Guanyu Zhang, Qian hu, Hanyu Wang, Deli huazhong Univ Sci & Technol huST Sch Chem & Chem Engn Key Lab Mat Chem Energy Convers & Storage Minist Educ Wuhan 430074 Peoples R China

Identifying the descriptors that control catalytic performance is key to design catalysts with high activity and stability. As indirect descriptors related to catalytic activity, modulating the Pt-Pt interatomic distance and d-band center can effectively enhance the ORR activity. It is well recognized that the Pt-Pt interatomic distance is strongly correlated with the surface strain. Herein, PtMnM/C ternary intermetallics are constructed through partially replacing Mn in PtMn/C binary intermetallics with the other transition metal (M = Fe, Co, Ni, and Cu). The 3d-transition metal doping induces surface strain, and the ORR performance of PtMnM/C exhibits volcano relationship relative to both the Pt-Pt interatomic distance and d-band center. The PtMnCo/C with optimum strain exhibits the highest mass activity (1.06 A mgPt-1) at 0.9 V, which is 2.6 and 4.6 times higher than that of PtMn/C and commercial Pt/C catalysts, respectively. In addition, PtMnCo/C shows good durability with only 10 mV half-wave potential decay after 50,000 potential cycles.

关键词： oxygen reduction reaction electrocatalysts ternary intermetallics surface strain electronic structure

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Domain generalization with semi-supervised learning for people-centric activity recognition

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Science China(Information Sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei Zhu Di LI Xing hu Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Determination of the number ofψ(3686)events taken at bESⅢ

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Chinese Physics C 2024年第9期48卷 8-20页

作者： M.Ablikim M.N.Achasov P.Adlarson O.Afedulidis X.C.Ai R.Aliberti A.Amoroso Q.An y.bai O.bakina I.balossino y.ban H.-R.bao V.batozskaya K.begzsuren N.berger M.berlowski M.bertani D.bettoni F.bianchi E.bianco A.bortone I.boyko R.A.briere A.brueggemann H.Cai X.Cai A.Calcaterra G.F.Cao N.Cao S.A.Cetin J.F.Chang G.R.Che G.Chelkov C.Chen C.H.Chen Chao Chen G.Chen H.S.Chen H.y.Chen M.L.Chen S.J.Chen S.L.Chen S.M.Chen T.Chen X.R.Chen X.T.Chen y.b.Chen y.Q.Chen Z.J.Chen Z.y.Chen S.K.Choi G.Cibinetto F.Cossio J.J.Cui H.L.Dai J.P.Dai A.Dbeyssi R.E.de boer D.Dedovich C.Q.Deng Z.y.Deng A.Denig I.Denysenko M.Destefanis F.De Mori b.Ding X.X.Ding y.Ding y.Ding J.Dong L.y.Dong M.y.Dong X.Dong M.C.Du S.X.Du y.y.Duan Z.H.Duan P.Egorov y.H.Fan J.Fang J.Fang S.S.Fang W.X.Fang y.Fang y.Q.Fang R.Farinelli L.Fava F.Feldbauer G.Felici C.Q.Feng J.H.Feng y.T.Feng M.Fritsch C.D.Fu J.L.Fu y.W.Fu H.Gao X.b.Gao y.N.Gao yang Gao S.Garbolino I.Garzia L.Ge P.T.Ge Z.W.Ge C.Geng E.M.Gersabeck A.Gilman K.Goetzen L.Gong W.X.Gong W.Gradl S.Gramigna M.Greco M.H.Gu y.T.Gu C.y.Guan Z.L.Guan A.Q.Guo L.b.Guo M.J.Guo R.P.Guo y.P.Guo A.Guskov J.Gutierrez K.L.Han T.T.Han F.Hanisch X.Q.Hao F.A.Harris K.K.He K.L.He F.H.Heinsius C.H.Heinz y.K.Heng C.Herold T.Holtmann P.C.Hong G.y.Hou X.T.Hou y.R.Hou Z.L.Hou b.y.hu H.M.hu J.F.hu S.L.hu T.hu y.hu G.S.huang K.X.huang L.Q.huang X.T.huang y.P.huang T.hussain F.Hölzken N.Hüsken N.in der Wiesche J.Jackson S.Janchiv J.H.Jeong Q.Ji Q.P.Ji W.Ji X.b.Ji X.L.Ji y.y.Ji X.Q.Jia Z.K.Jia D.Jiang H.b.Jiang P.C.Jiang S.S.Jiang T.J.Jiang X.S.Jiang y.Jiang J.b.Jiao J.K.Jiao Z.Jiao S.Jin y.Jin M.Q.Jing X.M.Jing T.Johansson S.Kabana N.Kalantar-Nayestanaki X.L.Kang X.S.Kang M.Kavatsyuk b.C.Ke V.Khachatryan A.Khoukaz R.Kiuchi O.b.Kolcu b.Kopf M.Kuessner X.Kui N.Kumar A.Kupsc W.Kühn J.J.Lane P.Larin L.Lavezzi T.T.Lei Z.H.Lei M.Lellmann T.Lenz C.Li C.Li C.H.Li Cheng Li D.M.Li F.Li G.Li H.b.Li H.J.Li H.N.Li hui Li J.R.Li J.S.Li Ke Li L.J.Li L.K.Li Lei Li M.H.Li P.R.Li Q.M.Li Q.X.Li R.Li S.X.Li T.Li W.D.Li W.G.Li X.Li X.H.Li X.L.Li X.Z.Li Xi Institute of High Energy Physics Beijing 100049China beihang University Beijing 100191China bochum Ruhr-University D-44780BochumGermany budker Institute of Nuclear Physics Sb RAS(bINP) Novosibirsk 630090Russia Carnegie Mellon University PittsburghPennsylvania 15213USA Central China Normal University Wuhan 430079China Central South University Changsha 410083China China Center of Advanced Science and Technology Beijing 100190China China University of Geosciences Wuhan 430074China Chung-Ang University Seoul06974Republic of Korea COMSATS University Islamabad Lahore CampusDefence RoadOff Raiwind Road54000LahorePakistan Fudan University Shanghai 200433China GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291DarmstadtGermany Guangxi Normal University Guilin 541004China Guangxi University Nanning 530004China Hangzhou Normal University Hangzhou 310036China Hebei University Baoding 071002China Helmholtz Institute Mainz Staudinger Weg 18D-55099MainzGermany Henan Normal University Xinxiang 453007China Henan University Kaifeng 475004China Henan University of Science and Technology Luoyang 471003China Henan University of Technology Zhengzhou 450001China huangshan College Huangshan 245000China hunan Normal University Changsha 410081China hunan University Changsha 410082China Indian Institute of Technology Madras Chennai 600036India Indiana University BloomingtonIndiana 47405USA INFN Laboratori Nazionali di Frascati (A)INFN Laboratori Nazionali di FrascatiI-00044FrascatiItaly(B)INFN Sezione di PerugiaI-06100PerugiaItaly(C)University of PerugiaI-06100PerugiaItaly INFN Sezione di Ferrara (A)INFN Sezione di FerraraI-44122FerraraItaly(B)University of FerraraI-44122FerraraItaly Inner Mongolia University Hohhot 010021China Institute of Modern Physics Lanzhou 730000China Institute of Physics and Technology Peace Avenue 54BUlaanbaatar 13330Mongolia Instituto de Alta Investigación Universidad de TarapacáCasilla 7DArica 1000000Chile Jilin University Changchun 130012China Joha

The number ofψ(3686)events collected by the bESⅢdetector during the 2021 run period is determined to be(2259.3±11.1)×10~6 by counting inclusiveψ(3686)hadronic *** uncertainty is systematic and the statistical uncertainty is ***,the numbers ofψ(3686)events collected during the 2009 and 2012run periods are updated to be(107.7±0.6)×10~6 and(345.4±2.6)×10~6,*** numbers are consistent with the previous measurements within one standard *** total number ofψ(3686)events in the three data samples is(2712.4±14.3)×10~6.

关键词： ψ(3686) inclusive process Hadronic events bESⅢdetector

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RECONSTRUCTION PRObLEMS OF CONVEX bODIES FROM EVEN L_(p)SURFACE AREA MEASURES

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Acta Mathematica Scientia 2025年第1期45卷 126-142页

作者： Juewei hu Gangsong LENG Department of Mathematics Shanghai UniversityShanghai200444China

We build a computer program to reconstruct convex bodies using even L_(p)surface area measures for p≥***,we transform the minimization problem Pi,which is equivalent to solving the even L_(p)Minkowski problem,into a convex optimization problem P4 with a finite number of *** transformation makes it suitable for computational ***,we prove that the approximate solutions obtained by solving the problem P4 converge to the theoretical solution when N and k are sufficiently ***,based on the convex optimization problem P_(4),we provide an algorithm for reconstructing convex bodies from even L_(p)surface area measures,and present several examples implemented using MATLAb.

关键词： reconstruction problem even L_(p)surface area measures spherical harmonic

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High-precision tip positioning composite feedforward control of linear colocated motion systems with bi-Loop iterative feedforward tuning

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Science China(Technological Sciences) 2025年第2期68卷 153-166页

作者： Junyu hu yourui TAO Shanhu LI Xu HAN College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130China

To enhance the tip positioning accuracy and robustness against disturbances of linear colocated motion systems,a novel composite feedforward(COMFF)control with bi-Loop iterative feedforward tuning(IFFT)is *** tip positioning error consists of the coupled tip residual vibration and linear motor tracking *** the influence of nonlinear friction and load flexibility,the topology of COMFF is designed as decoupled structures so that COMFF can suppress the tip residual vibration and compensate for the linear motor tracking error ***,bi-Loop IFFT is built to improve the robustness of COMFF in high-precision motor tracking *** the state space of the linear motor tracking error based on the projection theorem,two loops are performed in parallel along the iteration *** is the P-type iteration loop that drives linear motor tracking errors to converge to the zero *** other is the observation loop constructed by the recursive least square with forgetting factors to accelerate the P-type iteration *** by the P-type iteration loop with the aid of the observation loop,the proposed COMFF is integrated with fast convergence and high robustness when confronted with model *** effectiveness of the proposed COMFF combined with bi-Loop IFFT is validated by experiments.

关键词： composite feedforward(COMFF)control bi-Loop iterative feedforward tuning(bi-Loop IFFT) colocated motion systems nonlinear friction flexible load

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