In this paper,we investigate data-driven bright soliton solutions of the nonlocal reverse-time nonlinear Schrodinger(NLS)equation and the parameter identification using the physically informed neural networks(PINNs)al...In this paper,we investigate data-driven bright soliton solutions of the nonlocal reverse-time nonlinear Schrodinger(NLS)equation and the parameter identification using the physically informed neural networks(PINNs)algorithm.Accurate simulations and comparative analyses of relative and absolute errors are performed for two-soliton and four-soliton solutions including linear solitary waves and periodic waves.In the training process,the standard PINNs scheme is employed for linear solitary wave solutions,while the prior information is added at local sharp regions for periodic wave solutions due to the complicated collision behaviors.For the parameter identification,we accurately recognize the nonlinear coefficients of the nonlocal NLS equation from known solutions with different noises.These results reinforce the application of deep learning with the PINNs framework to successfully study nonlocal integrable systems.展开更多
Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.F...Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
This study aimed to investigate the mediating role of knowledge,attitude,and practice(KAP)in the relationship between fatigue and kinesiophobia in 200 coronary heart disease patients from three Chinese tertiary hospit...This study aimed to investigate the mediating role of knowledge,attitude,and practice(KAP)in the relationship between fatigue and kinesiophobia in 200 coronary heart disease patients from three Chinese tertiary hospitals.Using validated scales(MFSI-SF,TSK-SV Heart,and the Rehabilitation Exercise Knowledge-Attitude-Practice Scale for Patients),data analysis via SPSS 25.0 and AMOS 24.0 structural equation modeling identified a significant KAP-mediated pathway(total indirect effect=0.377).KAP of rehabilitation was significantly negatively correlated with fatigue(r=-0.51,p<0.01)and kinesiophobia(r=-0.60,p<0.01),whereas fatigue was significantly positively correlated with kinesiophobia(r=0.678,p<0.01).Results indicate that fatigue amplifies kinesiophobia by compromising KAP of rehabilitation.A significant mediating effect of KAP of rehabilitation on the relationship between fatigue and kinesiophobia was found.Improving patient fatigue and the knowledge,attitudes,and practices of rehabilitation can help reduce kinesiophobia.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12171217 and 12375003)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LMS 25A010013)。
文摘In this paper,we investigate data-driven bright soliton solutions of the nonlocal reverse-time nonlinear Schrodinger(NLS)equation and the parameter identification using the physically informed neural networks(PINNs)algorithm.Accurate simulations and comparative analyses of relative and absolute errors are performed for two-soliton and four-soliton solutions including linear solitary waves and periodic waves.In the training process,the standard PINNs scheme is employed for linear solitary wave solutions,while the prior information is added at local sharp regions for periodic wave solutions due to the complicated collision behaviors.For the parameter identification,we accurately recognize the nonlinear coefficients of the nonlocal NLS equation from known solutions with different noises.These results reinforce the application of deep learning with the PINNs framework to successfully study nonlocal integrable systems.
基金financially supported by the National Key R&D Program of China(No.2024YFC3012701)the National Natural Science Foundation of China(No.42307269)+1 种基金the Growth of Young Scientific and Technological Talents of Guizhou Educational Commission[Qianjiaoji,Grant No.[2024]348]the Foundation Research Project of Kaili University(No.YTH-TD20253I)。
文摘Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
基金Guided Scientific Research Projects of Shiyan City(Project No.:24Y006)。
文摘This study aimed to investigate the mediating role of knowledge,attitude,and practice(KAP)in the relationship between fatigue and kinesiophobia in 200 coronary heart disease patients from three Chinese tertiary hospitals.Using validated scales(MFSI-SF,TSK-SV Heart,and the Rehabilitation Exercise Knowledge-Attitude-Practice Scale for Patients),data analysis via SPSS 25.0 and AMOS 24.0 structural equation modeling identified a significant KAP-mediated pathway(total indirect effect=0.377).KAP of rehabilitation was significantly negatively correlated with fatigue(r=-0.51,p<0.01)and kinesiophobia(r=-0.60,p<0.01),whereas fatigue was significantly positively correlated with kinesiophobia(r=0.678,p<0.01).Results indicate that fatigue amplifies kinesiophobia by compromising KAP of rehabilitation.A significant mediating effect of KAP of rehabilitation on the relationship between fatigue and kinesiophobia was found.Improving patient fatigue and the knowledge,attitudes,and practices of rehabilitation can help reduce kinesiophobia.
