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MmPiFNN:A multi-mode physics-informed fuzzy neural network for passive recognition of surface ships by underwater equipment using ship radiated noise signals 认领 引用
1
作者 Feng Liu Zipeng Li +2 位作者 Kunde Yang Fuhu Chen Junru Yu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期243-266,共24页
Ship radiated noise(SRN)is a key acoustic cue for underwater platforms such as submarines to detect,identify,and track surface vessels in long-range sonar confrontation scenarios.Accurate classification of SRN signals... Ship radiated noise(SRN)is a key acoustic cue for underwater platforms such as submarines to detect,identify,and track surface vessels in long-range sonar confrontation scenarios.Accurate classification of SRN signals is thus critical for underwater target recognition and maritime situational awareness.However,under complex and dynamic marine environments,SRN recognition remains highly challenging due to strong background noise,sample imbalance,and limited availability of labeled data.To enhance recognition performance under these constraints,this paper proposes a novel multi-mode physics-informed fuzzy neural network(MmPiFNN)that integrates multi-mode features,fuzzy inference,and physics-based constraints.The model applies Wasserstein generative adversarial networkbased data augmentation to address class imbalance and data scarcity.It then extracts time domain,time-frequency domain,and spatial domain features in parallel,followed by a fuzzy inference mechanism that adaptively fuses multi-mode information,improving interpretability.The fused features are input into a physics-informed neural network enhanced with three physics-based constraints:classification loss,multi-mode consistency loss,and physics-informed residual loss,enabling end-to-end physically consistent learning.The experimental results demonstrate that the proposed MmPiFNN achieves a classification precision of 91.22%on the DeepShip Dataset,outperforming existing models.Moreover,it maintains stable and high recognition performance even under small sample conditions,indicating strong practical value and promising application potential. 展开更多
关键词 Ship radiated noise Physical constraint Mode fusion Fuzzy system Passive recognition
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Joint physics and model-guided pre-stack seismic inversion using double dual neural network 认领 引用
2
作者 Jian Zhang Hui Sun +2 位作者 Xing-Guo Huang Li Han Yan-Song Li 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期157-177,共21页
Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enha... Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enhancing the exploration and production process.Deep learning-based seismic inversion does not need a known physical system and can give satisfactory results with sufficient training data.The acquisition of such datasets for seismic inversion poses a significant challenge due to the exorbitant costs associated with drilling activities.Integrating domain knowledge,physical systems,and well log data into a deep learning-based seismic inversion framework is crucial for improving its efficiency and effectiveness.Nevertheless,existing data-driven approaches do not adequately exploit such information,thereby constraining their overall performance and applicability.Therefore,we develop a double dual neural network structure built upon the closed-loop neural network framework,which incorporates both physics and model information to mitigate the dependency on extensive labeled datasets.The information from the different domains is linked through a loss function,where one dual network is responsible for constraining the inversion results using physics information to ensure the physics consistency of the predictions,and the other dual network is responsible for constraining the inversion results using a priori model information to enhance the reliability of the predictions.The method makes full use of well-log data for network training when wells are available,as well as providing unsupervised learning and inversion under well-free conditions.The integration of qualitative and quantitative analyses proves instrumental in demonstrating the effectiveness of the proposed methodology through the use of synthetic and field pre-stack examples. 展开更多
关键词 Pre-stack seismic inversion Double dual neural network Physics guided Model guided
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Decomposing highly multimode fibers using a physics-driven neural network 认领 引用
3
作者 Qian Zhang Yuan Sui +5 位作者 Stefan Rothe Jiali Sun Dennis Pohle Nektarios Koukourakis Guohai Situ Juergen W.Czarske 《Advanced Photonics Nexus》 CSCD 2026年第3期186-195,共10页
Multimode fibers(MMFs)play an increasing role in optical communication,ultra-thin fiber endoscopy systems,and fiber lasers.The characterization of light propagation properties through active and passive MMFs attracts ... Multimode fibers(MMFs)play an increasing role in optical communication,ultra-thin fiber endoscopy systems,and fiber lasers.The characterization of light propagation properties through active and passive MMFs attracts high interest as many linear optical phenomena fundamentally depend on the interplay among multiple spatial modes.Access to the exact modal amplitudes and phase weights via mode decomposition(MD)provides a useful means of investigating the physical effects.It facilitates technological advances in telecommunications,endoscopy,sensors,and amplifiers that utilize MMFs.We present an untrained neural network assisted by a linear physical model of the multimode waveguide that carries out computational MD.For the first time,the reconstructed amplitude distribution achieves a high correlation coefficient,either for thousands of modes in a short MMF or for tens of modes in a 1-km-long MMF.We also investigate the limitations of MD based on single-shot intensity images by evaluating the relative modal errors and the effect of image resolution on the decomposition accuracy.We demonstrate our network framework and results on both passive and active multimode photonic systems.Our approach holds great promise for applications in fiber lasers,endoscopic computational imaging,and especially in fiber-based communication,where fiber crosstalk is heavy and reference-free calibration techniques are required. 展开更多
关键词 multimode fiber mode decomposition deep learning physical information phase retrieval fiber laser
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Use of a Neural Network with a Perturbation Forecast Model to Emulate the Effect of Moist Physics Parameterization 认领 引用
4
作者 FENG Ye-rong XUE Ji-shan +1 位作者 LI Meng-jie XU Dao-sheng 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第2期107-118,共12页
To develop a novel moist physics parameterization scheme,this study analyzed Typhoon Mujigae in the South China Sea.The China Meteorological Administration’s Tropical Region Atmospheric Model System,a regional numeri... To develop a novel moist physics parameterization scheme,this study analyzed Typhoon Mujigae in the South China Sea.The China Meteorological Administration’s Tropical Region Atmospheric Model System,a regional numerical weather prediction model,was run using alternately activated and deactivated conventional moist physics parameterization schemes.The difference between the outputs of these runs formed a dataset used to train a fully connected neural network.This network predicts the temporal tendencies of potential temperature and specific humidity,representing the heating and drying effects of moist physical processes.A perturbation forecast approach was employed to isolate these moist physical effects from the influence of large-scale dynamical processes on heat and moisture transport.The results demonstrate that the trained neural network scheme successfully replicates the heating and drying features,primarily latent heat release,around the typhoon center.It exhibited spatial distributions of heat sources and moisture sinks comparable to those of the conventional scheme.The analysis revealed a key characteristic of typhoon convection:heat sources correspond to moisture sinks.Vertically averaged moisture sinks exceed the heat sources,indicating an excess latent heat release that necessitates balancing by radiative cooling.This study confirmed that a deep-learning moist physics scheme can effectively emulate traditional parameterization schemes,particularly for typhoons. 展开更多
关键词 deep neural network moist physics effect perturbation forecast model regional numerical weather prediction model
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Ultrafast Condensed Matter Physics at the Horizon 认领 引用
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作者 Jimin Zhao 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第3期205-206,共2页
Ultrafast optical spectroscopy was successfully introduced decades ago.Its deep relationship with condensed matter physics profoundly enriched the scientific frontier of light–matter interactions.Previously,materials... Ultrafast optical spectroscopy was successfully introduced decades ago.Its deep relationship with condensed matter physics profoundly enriched the scientific frontier of light–matter interactions.Previously,materials such as metals,insulators,semiconductors,and superconductors were investigated,followed by magnetic materials,strongly correlated materials,complex oxides,nano-materials,topological materials,and metamaterials. 展开更多
关键词 metals ultrafast optical spectroscopy magnetic materialsstrongly condensed matter physics insulators semiconductors ultrafast condensed matter physics light matter interactions
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Searches for New Physics Beyond the Standard Model in Hyperon Sector 认领 引用
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作者 Jianyu Zhang Jinlin Fu Hai-Bo Li 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第6期15-21,共7页
Hyperon physics offers a distinctive laboratory for probing the intensity frontier and searching for physics beyond the Standard Model.This review summarizes recent results from the BESIII experiment,including pioneer... Hyperon physics offers a distinctive laboratory for probing the intensity frontier and searching for physics beyond the Standard Model.This review summarizes recent results from the BESIII experiment,including pioneering studies of dark baryons,massless BSM particles,and invisible decay modes,together with investigations of baryon-and lepton-number violation.A central highlight is the determination of theΛelectric dipole moment using quantum-entangled hyperon-antihyperon pairs,achieving a sensitivity three orders of magnitude beyond previous limits.These measurements provide world-leading constraints on new physics scenarios and establish a robust foundation for next-generation precision studies.By integrating experimental progress with theoretical developments and future facility prospects,this review emphasizes the critical role of hyperon probes in testing the fundamental laws of nature. 展开更多
关键词 besiii experimentincluding standard modelthis dark baryonsmassless determination electric dipole moment invisible decay modestogether probing intensity frontier hyperon physics dark baryons
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Multisensory Neuromorphic Devices:From Physics to Integration 认领 引用 被引量：1
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作者 An Gui Haoran Mu +2 位作者 Rong Yang Guangyu Zhang Shenghuang Lin 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第4期263-316,共54页
The increasing complexity of intelligent sensing environments,driven by the growth of Internet of Things technologies,has created a strong demand for neuromorphic systems capable of real-time,low-power multisensory pe... The increasing complexity of intelligent sensing environments,driven by the growth of Internet of Things technologies,has created a strong demand for neuromorphic systems capable of real-time,low-power multisensory perception.Traditional sensory architectures,constrained by single-modal processing and centralized computing,struggle to meet the requirements of diverse and dynamic input conditions.Multisensory neuromorphic devices offer a promising solution by mimicking the distributed,event-driven processing of biological systems.Recent efforts have explored synaptic devices and material systems that respond to various input modalities,including visual,tactile,thermal,and chemical stimuli.However,challenges remain in signal conversion,encoding compatibility,and the fusion of heterogeneous inputs without loss of unisensory information.This review provides a comprehensive overview of the physical mechanisms,device behaviors,and integration strategies that underpin signal processing in neuromorphic hardware.We highlight synaptic mechanisms conducive to cross-modal interaction,analyze representative signal fusion approaches at the device level,and discuss future directions for constructing efficient,scalable,and biologically inspired multisensory neuromorphic systems. 展开更多
关键词 Neuromorphic computing Multisensory signals Physical mechanism Multisensory fusion Synapse
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Remaining useful life prediction for hydraulic spool valve based on physics-informed Gamma process 认领 引用 被引量：1
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作者 Shaoyang DU Chao ZHANG +3 位作者 Shaoping WANG Rentong CHEN Yuwei ZHANG Rui MU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期106-118,共13页
Internal leakage caused by wear in hydraulic spool valves represents a critical failure mode that threatens the performance of aircraft hydraulic systems and compromises flight safety.Due to complex operational loads ... Internal leakage caused by wear in hydraulic spool valves represents a critical failure mode that threatens the performance of aircraft hydraulic systems and compromises flight safety.Due to complex operational loads and time-varying material properties,the relationship between wear state and Remaining Useful Life(RUL)is nonlinear.Consequently,accurately modeling this wear remains a significant challenge,as existing research often neglects the coupled effects of material properties,stress conditions,and dynamic lubrication parameters.To address this issue,this study proposes a novel framework integrating physical mechanisms with stochastic processes to enhance wear degradation modelling and RUL prediction.First,a Physics-of-Failure(PoF)model is developed based on Archard's wear theory,which characterizes tribological behavior at the contact interface and accounts for the effects of lubrication and load conditions.Next,a Gamma process is introduced to model the degradation trajectory,with physical parameters guiding the specification of the time-scale function.A Bayesian expectation–maximization algorithm is employed to estimate and update the model parameters.Finally,a numerical simulation and case study on spool valves are conducted to demonstrate the effectiveness of the proposed model.The cross-validation results confirmed that the introduction of random effects effectively reduces the impact of uncertainty on physics-informed modeling.This study offers a systematic solution to RUL prediction for hydraulic systems. 展开更多
关键词 Hydraulic equipment Physics-informed Gamma process Physics of Failure Remaining Useful Life prediction Stochastic models Wear of materials
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A rock physics model-based approach to delineate deep and tight sand reservoir heterogeneity in the Bohai Bay Basin,China 认领 引用
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作者 Donghong Zhou Qifeng Dou +5 位作者 Jiaqi Jiang Yihao Deng Xiaodong Wei Xingrui Dou Yuhui Zhao Fengyang Dong 《Energy Geoscience》 EI CAS CSCD 2026年第1期72-80,共9页
Delineating sweet spots is critical for the exploration and production of oil and gas in deep and tight sand reservoirs.The lack of advanced and reliable methods makes this a challenge for geologists and geophysicists... Delineating sweet spots is critical for the exploration and production of oil and gas in deep and tight sand reservoirs.The lack of advanced and reliable methods makes this a challenge for geologists and geophysicists.This study introduces,for the first time,an integrated workflow that combines pre-stack seismic inversion with rock physics modeling to predict reservoir porosity and shale volume(V-shale)for sweet spot identification in tight sand reservoirs.A new elastic parameter,the density calculation index(DCI),is introduced which links acoustic and shear impedance for seismic density inversion,thereby addressing the long-standing problem of poor density inversion accuracy.A novel combined Sun–Walsh rock physics model,developed as part of this study,significantly improves V-shale evaluation from seismic data.The proposed three-step seismic inversion approach includes:(1)deriving acoustic and shear impedance from angle-stack seismic data using model-based inversion;(2)calculating density using shear impedance constrained by DCI,followed by porosity estimation from the density–porosity relation;and(3)evaluating V-shale using theα-parameter derived from the Sun–Walsh model and pre-stack inversion results.This integrated workflow provides an effective tool for building accurate 3D reservoir models,and is especially applicable to deep,low-porosity,tight sand reservoirs worldwide. 展开更多
关键词 Rock physics model Seismic inversion Sand reservoir Porosity V-shale
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Artificial intelligence in reactor physics:current status and future prospects 认领 引用
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作者 Rui-Zhi Zhang Sheng-Feng Zhu +5 位作者 Kan Wang Ding She Jean-Philippe Argaud Bertrand Bouriquet Qing Li He-Lin Gong 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第6期196-231,共36页
Reactor physics is the study of neutron properties,focusing on the use of models to examine the interactions between neutrons and materials in nuclear reactors.Artificial intelligence(AI)has made significant contribut... Reactor physics is the study of neutron properties,focusing on the use of models to examine the interactions between neutrons and materials in nuclear reactors.Artificial intelligence(AI)has made significant contributions to reactor physics,such as in operational simulations,safety design,real-time monitoring,core management,and maintenance.This paper presents a comprehensive review of AI approaches in reactor physics,especially considering the category of Machine Learning(ML,which we also refer to as AI/ML to recall the AI name we found in articles),with the aim of describing the application scenarios,frontier topics,unsolved challenges,and future research directions.From equation solving and state parameter prediction to nuclear industry applications,this study provides a step-by-step overview of ML methods applied to steadystate,transient,and burnup problems.Most studies have achieved industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods,which leads to successful applications.However,research on ML methods in reactor physics is somewhat fragmented,and the ability to generalize models must be strengthened.Progress is still possible,especially in addressing theoretical challenges and enhancing industrial applications,such as building surrogate models and digital twins. 展开更多
关键词 Reactor physics Artificial intelligence Machine learning Neutron governing equations Fuel burnup Simulation Core design Monitoring
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Typhoon Kompasu(2118)simulation with planetary boundary layer and cloud physics parameterization improvements 认领 引用
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作者 Xiaowei Tan Zhiqiu Gao Yubin Li 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第1期41-46,共6页
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred... This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure. 展开更多
关键词 Tropical cyclone Numerical simulation Planetary boundary layer parameterization scheme Cloud physics scheme
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Permeability estimation using rock physics modeling and variational Bayes inversion 认领 引用
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作者 Mohammadfarid Ghasemi Abdorrazagh Javid 《Petroleum Science》 SCIE EI CAS CSCD 2026年第2期656-679,共24页
Permeability estimation is pivotal in reservoir characterization;however,prevailing methods lack a standardized approach.Traditionally reliant on core samples,permeability assessment encounters limitations across dive... Permeability estimation is pivotal in reservoir characterization;however,prevailing methods lack a standardized approach.Traditionally reliant on core samples,permeability assessment encounters limitations across diverse thicknesses and wells.An innovative core-independent two-step rock physics template(RPT)can be designed to estimate elastic and conductive properties.The suggested RPT employs the T-matrix method to leverage well-log data encompassing porosity,fluid saturation,and various textural parameters.The estimation process for textural parameters involves addressing uncertainties through the fixed form variational inference(FFVB)with the trust region reflective optimization algorithm.These uncertainties span estimated textural parameters,seismic wave propagation velocity,electrical resistivity,and hydraulic permeability.Micro and macro voids,micro-spherical pores porosity,and their semi-axis are modeled using Beta distributions for both prior and variational families.The noise in the model assumes an inverse gamma distribution for sonic travel time and true formation resistivity.Validation of the proposed method is achieved by comparing the FFVB results with Metropolis Hasting's sampling method in three depths and also through geological observations and experimental analyses on available core samples.The inverse problem,involving the determination of textural parameters through sonic travel time and resistivity,is solved.Subsequently,the forward problem is addressed to estimate permeability.The robustness of the inverse problem is underscored by minimal discrepancies between measured sonic travel times,true formation resistivity values,and the results of the forward problem.The method demonstrates its effectiveness in permeability estimation,even in regions lacking core data,thereby emphasizing its reliability and applicability in diverse geological settings. 展开更多
关键词 Variational Bayes Permeability Well log data Rock physics modelling Carbonates
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Comparison of Physical,Gaussian Process,and Physics-Informed Gaussian Process Models for Wind Turbine Power Curve Estimation 认领 引用
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作者 Samuel Martínez-Gutiérrez Carlos Gutiérrez +2 位作者 Alejandro Merino Diego García-Álvarez Daniel Sarabia 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期695-717,共23页
Accuratemodelling of power production in wind power systems is essential for optimizing their real-time operation and meeting technical or economic objectives.However,the precisemodelling of wind turbine power output ... Accuratemodelling of power production in wind power systems is essential for optimizing their real-time operation and meeting technical or economic objectives.However,the precisemodelling of wind turbine power output remains challenging,particularly when relying on conventional parametric models,which often struggle to capture complex or non-linear behaviors.This paper compares three modelling approaches to estimate the power produced by a real wind turbine(a Senvion MM82/2050 located in France):one parametric,based on analytical expressions of the power coefficient CP(λ,β);another nonparametric,which uses Gaussian processes(GP)to probabilistically model the relationship between operating variables and the power generated;and a third semiparametric approach,which uses a physics-informed GP that explicitly incorporates the wind conversion model based on the power coefficient CP(λ,β)within the Gaussian process as a mean function.Parametric models are efficient,interpretable,and useful when the underlying system model is known;however,they exhibit less predictive power in the face of complex behavior.In contrast,GPs offer greater flexibility,quantify uncertainty,and adapt to complex patterns in the data;however,their extrapolation outside the training range is limited and can lead to erroneous or even physically impossible predictions.The physics-informed GP integrates physical knowledge about the conversion of wind speed to power,improving the estimations outside the training range.Four model estimation procedures were performed using real data obtained from the SCADA system:the first one,retrieves the parameters of the power coefficient Cp from the physical model;the second estimates the hyperparameters of the GP;the third simultaneously estimates both the Gaussian process hyperparameters and the power coefficient parameters of the physics-informed GP;and the fourth computes only the hyperparameters of the physics-informed GP,keeping the optimal power coefficient parameters obtained in the first procedure.The fitting results were analyzed using the Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)as metrics,as well as the time required for fittingraining.The results show that the parametric approach has a lower predictive capacity than the GP and physics-informed GP.The latter has an RMSE that is slightly lower than that of the standard GP and makes more accurate predictions in regions with limited or no data availability.The results also show a trade-off between accuracy and computational efficiency,the physics-informed GP has a training time considerably longer than that of the other twomodels,nevertheless,it is a valuable tool when prediction robustness is a priority.Finally,the results highlight the need to include additional explanatory variables to better capture the observed dispersion and the effect of the high short-term variability of the 1-min SCADA measurements on model fitting. 展开更多
关键词 Wind turbine power curve wind turbine power coefficient physical model Gaussian processes physicsinformed machine learning
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Chinese Journal of Chemical Physics Instructions to Authors 认领 引用
14
《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第4期I0117-I0117,共1页
ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics(CJCP)ABOUT THIS JOURNAL is devoted to reporting new and original experimental and theoret-ical research on interdisciplinary areas,with chemi... ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics(CJCP)ABOUT THIS JOURNAL is devoted to reporting new and original experimental and theoret-ical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biologi-cal sciences and their interdisciplinary areas.As one of the 24 peer reviewed journals under the Chinese Physical Society(CPS),CJCP has been covered in ISI products(SCIE)as well as other major indexes.CJCP is currently a bimonthly journal,and it publishes in English with Chinese abstract as of 2006. 展开更多
关键词 Chemistry Chemical Physics Interdisciplinary Areas Physics chemical physics cjcp Experimental Research peer reviewed journals Theoretical Research
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Chinese Journal of Chemical Physics Instructions to Authors 认领 引用
15
《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第1期I0044-I0044,共1页
ABOUT THIS JOURNALLaunched in 1988,the Chinese Journal of Chemical Physics(CJCP)is devoted to reporting new and original experimental and theoret-ical research on interdisciplinary areas,with chemistry and physics gro... ABOUT THIS JOURNALLaunched in 1988,the Chinese Journal of Chemical Physics(CJCP)is devoted to reporting new and original experimental and theoret-ical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biologi-cal sciences and their interdisciplinary areas.As one of the 24 peer-reviewed journals under the Chinese Physical Society(CPS). 展开更多
关键词 chemistry physics materials science biological sciences Chinese Physical Society peer reviewed journals interdisciplinary research
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Complex τ Electric Dipole Moment from GeV-Scale New Physics 认领 引用
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作者 Zhong-Lv Huang Xin-Yu Du +2 位作者 Xiao-Gang He Chia-Wei Liu Zi-Yue Zou 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第3期28-33,共6页
Among the charged leptons,the τ electric dipole moment dτis the least constrained.We show that the Im[dτ]imposes strong constraints on new physics that have yet to be discussed.Motivated in particular by the Sup... Among the charged leptons,the τ electric dipole moment dτis the least constrained.We show that the Im[dτ]imposes strong constraints on new physics that have yet to be discussed.Motivated in particular by the Super Tau-Charm Facility(STCF),which will provide a uniquely clean environment for precisionτ-physics,we study the momentum-transfer dependence of dτ(q2)and compare the projected sensitivities of STCF and BelleⅡ.Our analysis shows that an axion-like coupling of the τ lepton can induce sizable real and imaginary components of the EDM.The predicted EDM values may approach the present experimental sensitivities,making them accessible to future measurements at Belle II and the STCF. 展开更多
关键词 momentum transfer dependence axion coupling new physics experimental sensitivities super tau charm facility lepton charged leptonsthe electric dipole moment gev scale new physics
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美国《保健物理》(Health Physics)杂志英文摘要(2026年130卷第1期) 认领 引用
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《辐射防护》 CAS CSCD 北大核心 2026年第2期174-181,共8页
Evaluating Adherence to Safety Standards for Physical Space Design, Equipment, and Patient and Staff Protection in Magnetic Resonance Imaging Centers:A Descriptive Cross-sectional Study Amirreza Sadeghinasab1, Jafar F... Evaluating Adherence to Safety Standards for Physical Space Design, Equipment, and Patient and Staff Protection in Magnetic Resonance Imaging Centers:A Descriptive Cross-sectional Study Amirreza Sadeghinasab1, Jafar Fatahiasl2, Mahmoud Mohammadi-Sadr1, Masoud Heydari Kahkesh3, and Marziyeh Tahmasbi2(1.Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran;2.Department of Radiologic Technology, School of Allied Medical Sciences, Ahvaz, Jundishapur University of Medical Sciences, Ahvaz, Iran;3.Department of Radiology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran)Abstract:Magnetic resonance imaging(MRI) has revolutionized disease diagnosis and treatment.However, the technology poses safety risks, such as exposure to magnetic fields, RF pulses, and cryogens, necessitating strict adherence to safety protocols to protect patients and healthcare workers. 展开更多
关键词 adherence safety standards magnetic resonance imaging physical space design patient staff protection allied medical sciences safety standards medical physics radiologic technology
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Chinese Journal of Chemical Physics Instructions to Authors 认领 引用
18
《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第2期F0003-F0003,共1页
ABOUT THIS JOURNAL Launched in 1988, the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas, with chemistry and physics ... ABOUT THIS JOURNAL Launched in 1988, the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas, with chemistry and physics groundwork of interest to researchers, faculty and students domestic and abroad in the fields of chemistry, physics, material and biological sciences and their interdisciplinary areas. As one of the 24 peerreviewed journals under the Chinese Physical Society (CPS), CJCP has been covered in ISI products (SCIE) as well as other major indexes. 展开更多
关键词 material sciences physics material biological sciences isi pr interdisciplinary areas experimental research chemistry theoretical research
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Innovation and Practice of College Physics Teaching from the Perspective of Multi-Physics Coupling 认领 引用
19
作者 Wenting Lu 《Journal of Contemporary Educational Research》 2026年第5期278-283,共6页
Traditional college physics teaching mostly deals with single physical fields,which is convenient for basic knowledge instruction but tends to fragment the inherent connections among physical phenomena,making it incon... Traditional college physics teaching mostly deals with single physical fields,which is convenient for basic knowledge instruction but tends to fragment the inherent connections among physical phenomena,making it inconsistent with the real physical picture of complex systems in modern engineering practice.Multi-physics coupling studied in this paper focuses on the mutual influence and synergistic interaction of multiple physical fields-including thermal,stress,electromagnetic,and fluid fields-in space and time,representing a frontier and focus of current and future scientific research and engineering design.From the perspective of multi-physics coupling,this paper combines the current situation of college physics teaching and proposes innovative teaching strategies.It aims to effectively break disciplinary barriers,cultivate students’systematic physical thinking,and genuinely integrate the concept of multi-physics coupling into all links of college physics teaching through comprehensive reform and practice.These efforts will greatly enhance students’ability to analyze and solve complex practical problems,and ultimately cultivate more engineering and technological talents with interdisciplinary literacy and innovative competence. 展开更多
关键词 Multi-physics coupling College physics Teaching innovation
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The Construction Strategies for the Project-Based Learning Model in Physics Teaching 认领 引用
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作者 Youqiang Deng 《Journal of Contemporary Educational Research》 2026年第5期272-277,共6页
With the continuous deepening of education reform guided by core competencies,physics teaching is transforming from“knowledge imparting”to“ability cultivation.”As a student-centered,inquiry-based teaching model dr... With the continuous deepening of education reform guided by core competencies,physics teaching is transforming from“knowledge imparting”to“ability cultivation.”As a student-centered,inquiry-based teaching model driven by real-world problems,project-based learning(PBL)is highly consistent with the essence of physics-“derived from life and applied to life.”It can effectively solve the pain points of traditional physics teaching and facilitate the cultivation of students’core competencies.Combined with physics teaching practice,this paper starts from the application significance of PBL in physics teaching and the existing problems in its current implementation,systematically discusses its construction strategies,provides theoretical references and practical paths for physics teaching reform,and promotes the improvement of physics teaching quality and the all-round development of students’comprehensive abilities. 展开更多
关键词 Physics teaching Project-based learning Core competencies Construction strategies
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