Prostate cancer(PCa)is one of the most common malignant tumors in the male genitourinary system,ranking second in incidence worldwide.Traditional Chinese medicine(TCM),as an important component of complementary and al...Prostate cancer(PCa)is one of the most common malignant tumors in the male genitourinary system,ranking second in incidence worldwide.Traditional Chinese medicine(TCM),as an important component of complementary and alternative medicine,shows unique advantages in cancer treatment.Chinese herbal medicine is usually composed of multiple ingredients and involves multiple signaling pathways,which showed function of inducing apoptosis of cancer cells,arresting the cell cycle,inhibiting invasion and metastasis,reducing drug resistance,and regulating immune function.Physical therapy is also an important treatment of TCM.Currently,Physical therapy such as acupuncture or Tai Chi and Qigong are gaining increased recognition in the management of PCa,particularly in addressing issues like urinary incontinence and bone metastasis-related pain.This article reviews the TCM treatment and therapy of PCa,in order to provide new research avenues and treatment options for the treatment of PCa with TCM and improve the quality of life of patients.展开更多
Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended time...Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.展开更多
Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of...Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of turbulences in the very early universe in sustaining intense and persistent fluctuations in energy or mass density,which could provide a natural mechanism for PBH formation in the primordial universe. We analyze the mass range and abundance of PBHs produced in the magnetohydrodynamic turbulence induced by the electroweak phase transition. Remarkably, we find that the mass range of the produced PBHs falls within the most viable“asteroid mass” window from the present-day observations, and within natural parameter regions their abundance can be sufficiently large. These findings suggest that PBHs produced during magnetohydrodynamic turbulence in the very early universe may comprise a dominant part of dark matter.展开更多
Drilling operations in carbonate rock heavy oil blocks(e.g.,in the Tahe Oilfield)are challenged by the intrusion of high-viscosity,temperature-sensitive formation heavy oil into the drilling fluid.This phenomenon ofte...Drilling operations in carbonate rock heavy oil blocks(e.g.,in the Tahe Oilfield)are challenged by the intrusion of high-viscosity,temperature-sensitive formation heavy oil into the drilling fluid.This phenomenon often results in wellbore blockage,reduced penetration rates,and compromised well control,thereby significantly limiting drilling efficiency and operational safety.To address this issue,this study conducts a comprehensive investigation into the mechanisms governing heavy oil invasion using a combination of laboratory experiments and field data analysis.Findings indicate that the reservoir exhibits strong heterogeneity and that the heavy oil possesses distinctive physical properties.The intrusion process is governed by multiple interrelated factors,including pressure differentials,pore structure,and the rheological behavior of the heavy oil.Experimental results reveal that the invasion of heavy oil occurs in distinct phases,with temperature playing a critical role in altering its viscosity.Specifically,as temperature increases,the apparent viscosity of the drilling fluid decreases;however,elevated pressures induce a nonlinear increase in viscosity.Furthermore,the compatibility between the drilling fluid and the intruding heavy oil declines markedly with increasing oil concentration,substantially raising the risk of wellbore obstruction.Simulation experiments further confirm that at temperatures exceeding 40℃and injection rates of L/min,the likelihood of wellbore blockage significantly≥0.4increases due to heavy oil infiltration.Based on these insights,a suite of targeted mitigation strategies is proposed.These include the formulation of specialized chemical additives,such as viscosity reducers,dispersants,and plugging removal agents,the real-time adjustment of drilling fluid density,and the implementation of advanced monitoring and early-warning systems.展开更多
基金supported by China Postdoctoral Science Foundation(2022M722674)Peixian Science and Technology Plan Project(P202410)Xuzhou Medical Reserve Talents Project(XWRCHT20220009).
文摘Prostate cancer(PCa)is one of the most common malignant tumors in the male genitourinary system,ranking second in incidence worldwide.Traditional Chinese medicine(TCM),as an important component of complementary and alternative medicine,shows unique advantages in cancer treatment.Chinese herbal medicine is usually composed of multiple ingredients and involves multiple signaling pathways,which showed function of inducing apoptosis of cancer cells,arresting the cell cycle,inhibiting invasion and metastasis,reducing drug resistance,and regulating immune function.Physical therapy is also an important treatment of TCM.Currently,Physical therapy such as acupuncture or Tai Chi and Qigong are gaining increased recognition in the management of PCa,particularly in addressing issues like urinary incontinence and bone metastasis-related pain.This article reviews the TCM treatment and therapy of PCa,in order to provide new research avenues and treatment options for the treatment of PCa with TCM and improve the quality of life of patients.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC2201901,2021YFC2203004,2020YFC2200100 and 2021YFC2201903)International Partnership Program of the Chinese Academy of Sciences(Grant No.025GJHZ2023106GC)+4 种基金the financial support from Brazilian agencies Funda??o de AmparoàPesquisa do Estado de S?o Paulo(FAPESP)Funda??o de Amparoà Pesquisa do Estado do Rio Grande do Sul(FAPERGS)Fundacao de Amparoà Pesquisa do Estado do Rio de Janeiro(FAPERJ)Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nível Superior(CAPES)。
文摘Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.
基金supported by the International Partnership Program of the Chinese Academy of Sciences (Grant No.025GJHZ2023106GC)。
文摘Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of turbulences in the very early universe in sustaining intense and persistent fluctuations in energy or mass density,which could provide a natural mechanism for PBH formation in the primordial universe. We analyze the mass range and abundance of PBHs produced in the magnetohydrodynamic turbulence induced by the electroweak phase transition. Remarkably, we find that the mass range of the produced PBHs falls within the most viable“asteroid mass” window from the present-day observations, and within natural parameter regions their abundance can be sufficiently large. These findings suggest that PBHs produced during magnetohydrodynamic turbulence in the very early universe may comprise a dominant part of dark matter.
基金Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering(Yangtze University),China(Grant No.YQZC202415)Hubei Province Science and Technology Plan Project(Key R&D Special Project),China(Grant No.2023BCB070)+2 种基金Key R&D Program Project in Xinjiang,China,Grant No.2022B01042Guiding Project of Scientific Research Program of Education Department of Hubei Province,China(Grant No.B2023024)Open Fund of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University),Grant No.PLN2023-03.
文摘Drilling operations in carbonate rock heavy oil blocks(e.g.,in the Tahe Oilfield)are challenged by the intrusion of high-viscosity,temperature-sensitive formation heavy oil into the drilling fluid.This phenomenon often results in wellbore blockage,reduced penetration rates,and compromised well control,thereby significantly limiting drilling efficiency and operational safety.To address this issue,this study conducts a comprehensive investigation into the mechanisms governing heavy oil invasion using a combination of laboratory experiments and field data analysis.Findings indicate that the reservoir exhibits strong heterogeneity and that the heavy oil possesses distinctive physical properties.The intrusion process is governed by multiple interrelated factors,including pressure differentials,pore structure,and the rheological behavior of the heavy oil.Experimental results reveal that the invasion of heavy oil occurs in distinct phases,with temperature playing a critical role in altering its viscosity.Specifically,as temperature increases,the apparent viscosity of the drilling fluid decreases;however,elevated pressures induce a nonlinear increase in viscosity.Furthermore,the compatibility between the drilling fluid and the intruding heavy oil declines markedly with increasing oil concentration,substantially raising the risk of wellbore obstruction.Simulation experiments further confirm that at temperatures exceeding 40℃and injection rates of L/min,the likelihood of wellbore blockage significantly≥0.4increases due to heavy oil infiltration.Based on these insights,a suite of targeted mitigation strategies is proposed.These include the formulation of specialized chemical additives,such as viscosity reducers,dispersants,and plugging removal agents,the real-time adjustment of drilling fluid density,and the implementation of advanced monitoring and early-warning systems.
