As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to intr...As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to introduce a new rock skeleton parameter which is the dry-rock VP/VS ratio squared(DVRS).In the process of fluid factor calculation or inversion,the existing methods take this parameter as a static constant,which has been estimated in advance,and then apply it to the fluid factor calculation and inversion.The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that,taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy.To solve the above problems,we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time,then apply it to fluid factor calculation and inversion.Firstly,the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers.Next,the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion(GNI)method.The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability,effectively suppress illusion.Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion(BDI)theory can successfully solve the above four parameter simultaneous inversion problem,and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor.All these results completely verified the feasibility and effectiveness of the proposed method.展开更多
Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at...Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.展开更多
Due to traffic and wave actions, cast steel joints are subjected to variable-amplitude fatigue loading, which may cause fatigue problems. The ratio of the minimum strain to the maximum strain(strain ratio)can be emplo...Due to traffic and wave actions, cast steel joints are subjected to variable-amplitude fatigue loading, which may cause fatigue problems. The ratio of the minimum strain to the maximum strain(strain ratio)can be employed to analyze the influence of variable-amplitude fatigue both in the elastic and plastic ranges. To evaluate the effect of the strain ratio on G20Mn5 QT cast steel, the fatigue tests of smooth specimens were carried out at the strain ratio of 0.1. The cyclic deformation and the relationships between the strain amplitude, the stress amplitude, the Smith, Watson and Topper(SWT)parameter and fatigue life were studied and compared with those at the strain ratio of-1. Compared with other methods, Basquin formula and Solonberg formula provide reliable and appropriate ranges of S-N curve and fatigue limit at different strain ratios respectively. The SWT parameter can be used to predict the fatigue life at other strain ratios accurately.展开更多
Lumbar spinal stenosis (LSS) causes ischemia, inflammation, demyelination and results in cauda equina (CE) syndrome, with pain and locomotor functional deficits. We investigated whether exogenous administration of S-n...Lumbar spinal stenosis (LSS) causes ischemia, inflammation, demyelination and results in cauda equina (CE) syndrome, with pain and locomotor functional deficits. We investigated whether exogenous administration of S-nitrosoglutathione (GSNO), an endogenous redox modulating anti-neuroinflammatory agent, hastens functional recovery in a CE compression (CEC) rat model. CEC was induced in adult female rats by the surgical implantation of two silicone blocks within the epidural spaces of L4-L6 vertebrae. GSNO (50 μg/kg body weight) was administered by gavage 1 h after the injury, and the treatment was continued daily thereafter. GSNO induced change in the pain threshold was evaluated for four days after the compression. Tissue analyses and locomotor function evaluation were carried out at two weeks and four weeks after the CEC respectively. GSNO significantly improved motor function in CEC rats as evidenced by an increased latency on rotarod compared with vehicle-treated CEC rats. CEC induced hyperalgesia was decreased by GSNO. GSNO also increased the expression of VEGF, reduced cellular infiltration (H&E staining) and apoptotic cell death (TUNEL assay), and hampered demyelination (LFB staining and g-ratio). These data demonstrate that administration of GSNO after CEC decreased inflammation, hyperalgesia and cell death leading to improved locomotor function of CEC rats. The therapeutic potential of GSNO observed in the present study with CEC rats suggests that GSNO is a candidate drug to test in LSS patients.展开更多
基金the National Natural Science Foundation of China(41904116,41874156,42074167 and 42204135)the Natural Science Foundation of Hunan Province(2020JJ5168)the China Postdoctoral Science Foundation(2021M703629)for their funding of this research.
文摘As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to introduce a new rock skeleton parameter which is the dry-rock VP/VS ratio squared(DVRS).In the process of fluid factor calculation or inversion,the existing methods take this parameter as a static constant,which has been estimated in advance,and then apply it to the fluid factor calculation and inversion.The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that,taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy.To solve the above problems,we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time,then apply it to fluid factor calculation and inversion.Firstly,the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers.Next,the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion(GNI)method.The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability,effectively suppress illusion.Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion(BDI)theory can successfully solve the above four parameter simultaneous inversion problem,and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor.All these results completely verified the feasibility and effectiveness of the proposed method.
文摘Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.
基金Supported by the National Natural Science Foundation of China(No.51178307 and No.51525803)
文摘Due to traffic and wave actions, cast steel joints are subjected to variable-amplitude fatigue loading, which may cause fatigue problems. The ratio of the minimum strain to the maximum strain(strain ratio)can be employed to analyze the influence of variable-amplitude fatigue both in the elastic and plastic ranges. To evaluate the effect of the strain ratio on G20Mn5 QT cast steel, the fatigue tests of smooth specimens were carried out at the strain ratio of 0.1. The cyclic deformation and the relationships between the strain amplitude, the stress amplitude, the Smith, Watson and Topper(SWT)parameter and fatigue life were studied and compared with those at the strain ratio of-1. Compared with other methods, Basquin formula and Solonberg formula provide reliable and appropriate ranges of S-N curve and fatigue limit at different strain ratios respectively. The SWT parameter can be used to predict the fatigue life at other strain ratios accurately.
文摘Lumbar spinal stenosis (LSS) causes ischemia, inflammation, demyelination and results in cauda equina (CE) syndrome, with pain and locomotor functional deficits. We investigated whether exogenous administration of S-nitrosoglutathione (GSNO), an endogenous redox modulating anti-neuroinflammatory agent, hastens functional recovery in a CE compression (CEC) rat model. CEC was induced in adult female rats by the surgical implantation of two silicone blocks within the epidural spaces of L4-L6 vertebrae. GSNO (50 μg/kg body weight) was administered by gavage 1 h after the injury, and the treatment was continued daily thereafter. GSNO induced change in the pain threshold was evaluated for four days after the compression. Tissue analyses and locomotor function evaluation were carried out at two weeks and four weeks after the CEC respectively. GSNO significantly improved motor function in CEC rats as evidenced by an increased latency on rotarod compared with vehicle-treated CEC rats. CEC induced hyperalgesia was decreased by GSNO. GSNO also increased the expression of VEGF, reduced cellular infiltration (H&E staining) and apoptotic cell death (TUNEL assay), and hampered demyelination (LFB staining and g-ratio). These data demonstrate that administration of GSNO after CEC decreased inflammation, hyperalgesia and cell death leading to improved locomotor function of CEC rats. The therapeutic potential of GSNO observed in the present study with CEC rats suggests that GSNO is a candidate drug to test in LSS patients.
