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STCF conceptual design report (Volume 1): Physics & detector

Frontiers of physics 2024年第1期19卷 1-154页

作者： M.Achasov X.C.Ai L.P.An R.Aliberti Q.An X.z.Bai Y.Bai O.Bakina A.Barnyakov V.Blinov V.Bobrovnikov D.Bodrov A.Bogomyagkov A.Bondar I.Boyko z.h.Bu F.M.Cai h.Cai J.J.Cao Q.h.Cao X.Cao z.Cao Q.Chang K.T.Chao D.Y.Chen h.Chen h.X.Chen J.F.Chen K.Chen L.L.Chen P.Chen S.L.Chen S.M.Chen S.Chen S.P.Chen W.Chen X.Chen X.F.Chen X.R.Chen Y.Chen Y.Q.Chen h.Y.Cheng J.Cheng S.Cheng T.G.Cheng J.P.Dai L.Y.Dai X.C.Dai D.Dedovich A.Denig I.Denisenko J.M.Dias D.z.Ding L.Y.Dong W.h.Dong V.Druzhinin D.S.Du Y.J.Du z.G.Du L.M.Duan D.Epifanov Y.L.Fan S.S.Fang z.J.Fang G.Fedotovich C.Q.Feng X.Feng Y.T.Feng J.L.Fu J.Gao Y.N.Gao P.S.Ge C.Q.Geng L.S.Geng A.Gilman L.Gong T.Gong B.Gou W.Gradl J.L.Gu A.Guevara L.C.Gui A.Q.guo F.K.guo J.C.guo J.guo Y.P.guo z.h.guo A.Guskov K.L.han L.han M.han X.Q.hao J.B.he S.Q.he X.G.he Y.L.he z.B.he z.X.heng B.L.hou T.J.hou Y.R.hou C.Y.hu h.M.hu K.hu R.J.hu W.h.hu X.h.hu Y.C.hu J.hua G.S.huang J.S.huang M.huang Q.Y.huang W.Q.huang X.T.huang X.J.huang Y.B.huang Y.S.huang N.hüsken V.Ivanov Q.P.Ji J.J.Jia S.Jia z.K.Jia h.B.Jiang J.Jiang S.z.Jiang J.B.Jiao z.Jiao h.J.Jing X.L.Kang X.S.Kang B.C.Ke M.Kenzie A.Khoukaz I.Koop E.Kravchenko A.Kuzmin Y.Lei E.Levichev C.h.Li C.Li D.Y.Li F.Li G.Li G.Li h.B.Li h.Li h.N.Li h.J.Li h.L.Li J.M.Li J.Li L.Li L.Li L.Y.Li N.Li P.R.Li R.h.Li S.Li T.Li W.J.Li X.Li X.h.Li X.Q.Li X.h.Li Y.Li Y.Y.Li z.J.Li h.Liang J.h.Liang Y.T.Liang G.R.Liao L.z.Liao Y.Liao C.X.Lin D.X.Lin X.S.Lin B.J.Liu C.W.Liu D.Liu F.Liu G.M.Liu h.B.Liu J.Liu J.J.Liu J.B.Liu K.Liu K.Y.Liu K.Liu L.Liu Q.Liu S.B.Liu T.Liu X.Liu Y.W.Liu Y.Liu Y.L.Liu z.Q.Liu z.Y.Liu z.W.Liu I.Logashenko Y.Long C.G.Lu J.X.Lu N.Lu Q.F.Lü Y.Lu Y.Lu z.Lu P.Lukin F.J.Luo T.Luo X.F.Luo Y.h.Luo h.J.Lyu X.R.Lyu J.P.Ma P.Ma Y.Ma Y.M.Ma F.Maas S.Malde D.Matvienko z.X.Meng R.Mitchell A.Nefediev Y.Nefedov S.L.Olsen Q.Ouyang P.Pakhlov G.Pakhlova X.Pan Y.Pan E.Passemar Y.P.Pei h.P.Peng L.Peng X.Y.Peng X.J.Peng K.Peters S.Pivovarov E.Pyata B.B.Qi Y.Q.Qi W.B.Qian Y.Qian C.F.Qiao J.J.Qin J.J.Qin L.Q.Qin X.S.Qin T.L.Qiu J.Rademacker C.F.Redmer h.Y.Sang Anhui University Hefei 230039China Beihang University Beijing 100191China Budker Institute of Nuclear Physics Novosibirsk 630090Russia CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China Cavendish Laboratory University of CambridgeJJ Thomson AveCambridge CB30HEUnited Kingdom Central China Normal University Wuhan 430079China Central South University Changsha 410083China China University of Geosciences Wuhan 430074China China University of Mining and Technology Xuzhou 221116China École Polytechnique Fédérale de Lausanne(EPFL) LausanneSwitzerland Fudan University Shanghai 200433China Goethe University Frankfurt D-60325 Frankfurt am MainGermany Guangxi Normal University Guilin 541004China Guangxi Uninversity Nanning 530004China hangzhou Institute for Advanced Study UCASHangzhou 310024China hebei Normal University Shijiazhuang 050024China hebei University Baoding 071002China hefei University of Technology Hefei 230601China helmholtz Institute Mainz Staudinger Weg 18D-55099 MainzGermany henan Normal University Xinxiang 453007China henan University Kaifeng 475004China high Energy Physics Center Chung-Ang UniversitySeoul 06974Korea higher School of Economy 11 Pokrovsky Bulvar Moscow 109028Russia huangshan University Huangshan 245000China hubei University of Automotive Technology Shiyan 442002China hunan Normal University Changsha 410081China hunan University of Science and Technology Xiangtan 411201China hunan University Changsha 410082China Indiana University BloomingtonIndiana 47405USA Inner Mongolia University Hohhot 010021China Institute of Advanced Science Facilities Shenzhen 518107China Institute of high Energy Physics Chinese Academy of SciencesBeijing 100049China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China Institute of Physics Academia SinicaTaipeiTaiwan 11529China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China Jilin University Changch

The superτ-charm facility(STCF)is an electron–positron collider proposed by the Chinese particle physics *** is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5×10^(35) cm^(–2)·s^(–1) or *** STCF will produce a data sample about a factor of 100 larger than that of the presentτ-charm factory—the BEPCII,providing a unique platform for exploring the asymmetry of matter-antimatter(charge-parity violation),in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions,as well as searching for exotic hadrons and physics beyond the Standard *** STCF project in China is under development with an extensive R&D *** document presents the physics opportunities at the STCF,describes conceptual designs of the STCF detector system,and discusses future plans for detector R&D and physics case studies.

关键词： electron–positron collider tau-charm region high luminosity STCF detector conceptual design

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New breakthrough: Building non-markovian environments for single-qubit systems at any time interval

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Science China(Physics,Mechanics & Astronomy) 2025年第3期Mechanics & Astronomy) .卷 193页

作者： h.z.Shen Center for Quantum Sciences and School of Physics Northeast Normal University

The proposal of quantum simulation provides a highly effective approach for experimental research on open quantum systems. To date, numerous quantum simulation studies pertaining to open quantum systems have been put forward,including Fanchini et al.’s simulation of a dephasing noise channel utilizing an optical system [1], and Xiong et al.’s realization of a quantum simulation for a non-rotating-wave approximation noise channel, also leveraging an optical system [2].

关键词： quantum system breakthrough

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Asking one mechanism in glial cells during neuroinflammation

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Neural Regeneration Research 2025年第4期20卷 1077-1078页

作者： Xiaoli guo Chikako harada Takayuki harada Visual Research Project Tokyo Metropolitan Institute of Medical ScienceTokyoJapan

Multiple sclerosis(MS),which is characterized by inflammatory demyelination in the central nervous system(CNS),is the most common neurological disease in the young adult *** autoimmune encephalomyelitis(EAE),an animal model of MS,is often used in preclinical *** data indicate that in addition to immune cells such as T cells and dendritic cells,CNS resident microglia and astrocytes play important roles in demyelinating neuroinflammation(healy et al.,2022).In particular,microglia are key immune-competent cells that can respond to environmental *** depletion of transforming growth factor-β-activated kinase 1,a mitogen-associated protein kinase kinase kinase,in microglia is reported to reduce CNS inflammation and diminish axonal and myelin damage *** suggests that elucidating the mechanisms of microglia-specific responses during pathologies may help in the development of treatments that reduce EAE/MS disease severity(Goldmann et al.,2013).

关键词： inflammation clinical

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Monogenic gene therapy for glaucoma and optic nerve injury

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Neural Regeneration Research 2025年第3期20卷 815-816页

作者： Chikako harada Xiaoli guo Takayuki harada Visual Research Project Tokyo Metropolitan Institute of Medical ScienceTokyoJapan

The prevalence of glaucoma, the second leading cause of global blindness, is increasing due to aging populations. In glaucoma, degeneration of the optic nerve and retinal ganglion cells(RGCs) causes visual field defec... 详细信息

关键词： visual glaucoma degeneration

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Sulfur doping and oxygen vacancy in In2O3 nanotube co-regulate intermediates of CO2 electroreduction for efficient hCOOh production and rechargeable zn-CO2 battery

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Journal of Energy Chemistry 2025年第2期101卷 474-484页

作者： Yu Li zhengrong Xu Quanxin guo Qin Li Rui Liu Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education School of Materials Science and Engineering Tongji University

By manipulating the distribution of surface electrons,defect engineering enables effective control over the adsorption energy between adsorbates and active sites in the CO2reduction reaction（CO2RR）.herein,we report a hollow indium oxide nanotube containing both oxygen vacancy and sulfur doping（V_o-Sx-In2O3） for improved CO2-to-hCOOh electroreduction and *** componential synergy significantly reduces the*OChO formation barrier to expedite protonation process and creates a favorable electronic micro-environment for*hCOOh *** a result,the CO2RR performance of Vo-Sx-In2O3outperforms Pure-In2O3and V_o-In2O3,where V_o-S53-In2O3exhibits a maximal hCOOh Faradaic efficiency of 92.4% at-1,2 V *** hydrogen electrode（RhE） in h-cell and above 92%over a wide window potential with high current density(119.1 mA cm-2at-1.1 V ***) in flow ***,the rechargeable zn-CO2battery utilizing V_o-S53-In2O3as cathode shows a high power density of 2.29 mW cm-2and a long-term stability during charge-discharge *** work provides a valuable perspective to elucidate co-defective catalysts in regulating the intermediates for efficient CO2RR.

关键词： CO2 electroreduction In2O3 Intermediate Oxygen vacancy Sulfur doping zn-CO2 battery

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Regulating local electron transfer environment of covalent triazine frameworks through F,N co-modification towards optimized oxygen reduction reaction

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Chinese Chemical Letters 2025年第1期36卷 344-348页

作者： Quanyou guo Yue Yang Tingting hu hongqi Chu Lijun Liao Xuepeng Wang zhenzi Li Liping guo Wei zhou Shandong Provincial Key Laboratory of Molecular Engineering School of Chemistry and Chemical EngineeringQilu University of Technology(Shandong Academy of Sciences)Ji’nan 250353China

The high conductivity of electrocatalyst can eliminate the Schottky energy barrier at the interface of heterogeneous phases during an electrocatalytic reaction and accelerate the rapid electron transfer to the catalytic active ***,the electronic conductivity is a vital parameter for oxygen reduction reaction(ORR).Covalent triazine frameworks(CTFs)have shown great potential application as electrocatalysts in ORR with a merit of the diverse building ***,the intrinsic low conductivity and high impedance of CTFs could be significant setbacks in electrocatalytic ***,CTFs were constructed by introducing F and N co-modification for efficient 2e^(-)*** with the pristine CTF,the co-presence of F,N could increase the conductivity obviously by *** a result,F-N-CTF exhibits enhanced catalytic performance of h_(2)O_(2)generation and selectivity towards reaction *** work reveals the importance of conductivity optimization for CTFs and provides guidance for designing high conductivity non-metallic organic semiconductor catalysts for 2e^(-)ORR.

关键词： Covalent triazine frameworks Conductivity Co-modification Electrocatalysis Oxygen reduction reaction

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Microstructural evolution during the progressive transformation-induced plasticity effect in a Fe-0.1C-5Mnmedium manganese steel

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 369-379页

作者： Mei zhang Wenhao Li Yangfei Chen Yang Jiang Xiaofei guo han Dong School of Materials Science and Engineering Shanghai UniversityShanghai 200444China State Key Laboratory of Advanced Special Steel Shanghai UniversityShanghai 200444China

The microstructural evolution of a cold-rolled and intercritical annealed medium-Mn steel(Fe-0.10C-5Mn)was investigated during uniaxial tensile ***-situ observations under scanning electron microscopy,transmission electron microscopy,and X-ray diffraction analysis were conducted to characterize the progressive transformation-induced plasticity process and associated fracture initiation *** findings were discussed with the local strain measurements via digital image *** results indicated that Lüders band formation in the steel was limited to 1.5%strain,which was mainly due to the early-stage martensitic phase transformation of a very small amount of the less stable large-sized retained austenite(RA),which led to localized stress concentrations and strain hardening and further retardation of *** small-sized RA exhibited high stability and progressively transformed into martensite and contributed to a stably extended Portevin-Le Chatelier *** volume fraction of RA gradually decreased from 26.8%to 8.2%prior to *** the late deformation stage,fracture initiation primarily occurred at the austenite/martensite and ferrite/martensite interfaces and the ferrite phase.

关键词： medium-Mn steel retained austenite progressive transformation-induced plasticity effect local strain fracture initiation

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New Insights on Gamma-Ray Burst Radiation Mechanisms from Multiwavelength Observations

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Research in Astronomy and Astrophysics 2025年第2期25卷 3-12页

作者： Yu-hua Yao Fang-Sheng Min Shi Chen Yi-Qing guo College of Physics Chongqing University Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Wisconsin Ice Cube Particle Astrophysics Center University of Wisconsin–Madison University of Chinese Academy of Sciences School of Physics and Astronomy Yunnan University Tianfu Cosmic Ray Research Center

The study of high-energy gamma-ray emission from gamma-ray bursts(GRBs) involves complex synchrotron radiation and synchrotron self-Compton(SSC) scattering mechanisms with multiple parameters exhibiting a wide distribution. Recent advancements in GRB research, particularly the observation of very high energy(VhE,>100 Ge V) radiation, have ushered in a new era of multiwavelength exploration, offering fresh perspectives and limitations for understanding GRB radiation mechanisms. This study aimed to leverage VhE observations to refine constraints on synchrotron + SSC radiation from electrons accelerated by forward shocks. By analyzing two external environments—the uniform interstellar medium and stratified stellar wind medium, we conducted spectral and variability fitting for five specific bursts(GRB 180720B, GRB 190114C, GRB 190829A, GRB 201216C, and GRB 221009A) to identify the optimal parameters characterizing these events. A comparative analysis of model parameter distributions with and without VhE radiation observations reveals that the magnetic energy equipartition factor εBis more concentrated with VhE emissions. This suggests that VhE emissions may offer greater constraints on this microphysical parameter. Additionally, we found that the energy budget between VhE and ke V–Me V γ-ray emissions under the SSC radiation exhibits an almost linear relationship, which may serve as a tool to differentiate radiation mechanisms. We anticipate future statistical analyses of additional VhE bursts to validate our findings.

关键词： (transients:) gamma-ray bursts gamma-rays: ISM radiation mechanisms: non-thermal

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Counterfactual Learning on Graphs:A Survey

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Machine Intelligence Research 2025年第1期22卷 17-59页

作者： zhimeng guo zongyu Wu Teng Xiao Charu Aggarwal hui Liu Suhang Wang College of Information Sciences and Technology Pennsylvania State UniversityUniversity Park 16802USA International Business Machines Corporation T.J.Watson Research Center New York 10598USA College of Engineering Michigan State UniversityEast Lansing 48824USA

Graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction *** neural networks(GNNs)have achieved great success in representation learning on graphs,facilitating various downstream ***,GNNs have several drawbacks such as lacking interpretability,can easily inherit the bias of data and cannot model casual ***,counterfactual learning on graphs has shown promising results in alleviating these *** approaches have been proposed for counterfactual fairness,explainability,link prediction and other applications on *** facilitate the develop-ment of this promising direction,in this survey,we categorize and comprehensively review papers on graph counterfactual *** divide existing methods into four categories based on problems *** each category,we provide background and motivating ex-amples,a general framework summarizing existing works and a detailed review of these *** point out promising future research directions at the intersection of graph-structured data,counterfactual learning,and real-world *** offer a comprehensive view of resources for future studies,we compile a collection of open-source implementations,public datasets,and commonly-used evalu-ation *** survey aims to serve as a“one-stop-shop”for building a unified understanding of graph counterfactual learning cat-egories and current resources.

关键词： Counterfactual learning graph-structured data graph neural networks fairness explainability

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Prediction of velocity and pressure of gas-liquid flow using spectrum-based physics-informed neural networks

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Applied Mathematics and Mechanics(English Edition) 2025年第2期46卷 341-356页

作者： Nanxi DING hengzhen FENG h.z. LOU Shenghua FU Chenglong LI zihao zhANG Wenlong MA zhengqian zhANG School of Mechanical Engineering Beijing Institute of Technology University of Southern California Xi'an Institute of Electromechanical Information Technology

This research introduces a spectrum-based physics-informed neural network (SP-PINN) model to significantly improve the accuracy of calculation of two-phase flow parameters, surpassing existing methods that have limitations in global and continuous data sampling. SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes (N-S) equations, enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks. The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors, achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network (PINN) approaches. Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s. Such advancements signify a 100-fold improvement over traditional DNS calculations, underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.

关键词： physics-informed neural network (PINN) spectral method two-phase flow parameter prediction O359

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