The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high c...Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.展开更多
This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The partici...This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.展开更多
Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most ...Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most promising directions of development.This paper proposes an optimized schedulingmodel for a hydrogen-coupled electro-heat-gas integrated energy system(HCEHG-IES)using generative adversarial imitation learning(GAIL).The model aims to enhance renewable-energy absorption,reduce carbon emissions,and improve grid-regulation flexibility.First,the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process(MDP).To overcome the limitations of conventional deep reinforcement learning algorithms—including long optimization time,slow convergence,and subjective reward design—this study augments the PPO algorithm by incorporating a discriminator network and expert data.The newly developed algorithm,termed GAIL,enables the agent to perform imitation learning from expert data.Based on this model,dynamic scheduling decisions are made in continuous state and action spaces,generating optimal energy-allocation and management schemes.Simulation results indicate that,compared with traditional reinforcement-learning algorithms,the proposed algorithmoffers better economic performance.Guided by expert data,the agent avoids blind optimization,shortens the offline training time,and improves convergence performance.In the online phase,the algorithm enables flexible energy utilization,thereby promoting renewable-energy absorption and reducing carbon emissions.展开更多
Explorations into new electrolytes have highlighted the critical impact of solvation structure on battery performance,Classical molecular dynamics(CMD)using semi-empirical force fields has become an essential tool for...Explorations into new electrolytes have highlighted the critical impact of solvation structure on battery performance,Classical molecular dynamics(CMD)using semi-empirical force fields has become an essential tool for simulating solvation structures.However,mainstream force fields often lack accuracy in describing strong ion-solvent interactions,causing disparities between CMD simulations and experimental observations.Although some empirical methods have been employed in some of the studies to address this issue,their effectiveness has been limited.Our CMD research,supported by quantum chemical calculations and experimental data,reveals that the solvation structure is influenced not only by the charge model but also by the polarization description.Previous empirical approaches that focused solely on adjusting ion-solvent interaction strengths overlooked the importance of polarization effects.Building on this insight,we propose integrating the Drude polarization model into mainstream force fields and verify its feasibility in carbonate,ether,and nitrile electrolytes.Our experimental results demonstrate that this approach significantly enhances the accuracy of CMD-simulated solvation structures.This work is expected to provide a more reliable CMD method for electrolyte design,shielding researchers from the pitfalls of erroneous simulation outcomes.展开更多
The presence of background classical sources affects quantum field theory significantly in different ways.Neutrino oscillation is a phenomenon that confirms that neutrinos are massive fermions in nature,a celebrated r...The presence of background classical sources affects quantum field theory significantly in different ways.Neutrino oscillation is a phenomenon that confirms that neutrinos are massive fermions in nature,a celebrated result in modern physics.Neutrino oscillation plays an important role in many astrophysical observations.However,the interactions between the background classical sources with neutrinos are not often considered.In the present article,we show the effect of some classical sources,namely matter currents,electromagnetic waves,torsion,and gravitational waves on neutrino oscillation.It is shown explicitly that the above sources can change the helicity state of neutrinos during neutrino oscillation.展开更多
Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of ...Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of this botanical motif necessitates not merely lexical equivalence,but more importantly,the transmission of its profound cultural resonance and aesthetic essence.This study posits that effective rendition of grass imagery should adopt an integrative approach synthesizing the objectives of cultural translation with the intrinsic aesthetic characteristics of classical poetry.Through systematic analysis of the cultural semiotics embedded in grass symbolism,the research investigates practical translation techniques at lexical,syntactic,and stylistic dimensions.The findings aim to contribute to the theoretical framework of cultural image translation in Chinese poetic tradition while providing methodological references for cross-cultural interpretation of classical verse.By bridging cultural semiotics with translation praxis,this investigation seeks to advance the intercultural communication of Chinese poetic heritage through nuanced treatment of its botanical symbolism.展开更多
This paper concerns the Cauchy problem of 3D compressible micropolar fluids in the whole space R^(3). For regular initial data with m0E0 is suitable small, where m0 and E0 represent the upper bound of initial density ...This paper concerns the Cauchy problem of 3D compressible micropolar fluids in the whole space R^(3). For regular initial data with m0E0 is suitable small, where m0 and E0 represent the upper bound of initial density and initial energy, we prove that if ρ0 ∈ Lγ ∩ H3 with γ ∈ (1, 6), then the problem possesses a unique global classical solution on R^(3) × [0, T] with any T ∈ (0, ∞). It’s worth noting that both the vacuum states and possible random largeness of initial energy are allowed.展开更多
More than a simple concert pianist,Wilson Chu is an Indonesian musical diplomacy force.As a distinguished performer,composer,and educator,Wilson has left an indelible mark on the international stage by seamlessly blen...More than a simple concert pianist,Wilson Chu is an Indonesian musical diplomacy force.As a distinguished performer,composer,and educator,Wilson has left an indelible mark on the international stage by seamlessly blending Western classical music with Southeast Asian tradition.Since 2019,he has been the youngest Associate Professor of Piano at the College of Chinese and ASEAN Arts(CCAA)at Chengdu University,where he has dedicated himself to shaping the next generation of musicians.展开更多
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources...Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era.展开更多
Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In ...Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In this work,we develop Atif-V2.0,an extended classical density functional theory(cDFT)framework that integrates the interfacial statistical associating fluid theory(iSAFT)to model multicomponent associating fluids composed of water-soluble polymers,alkanes,and water.Building on the original theoretical framework of Atif for modeling nanoconfined inhomogeneous fluids,Atif-V2.0 embeds explicit solvent and captures additional physical interactions-hydrogen bonding,which are critical in associating fluid systems.The other key feature of Atif-V2.0 is its ability to account for polymer topology.We demonstrate its capability by predicting the equilibrium structure and thermodynamic behavior of branched hydrolyzed polyacrylamide solutions near hard walls with various branching topologies,which provides a robust theoretical tool for the rational design of EOR polymers.展开更多
We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and ext...We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.展开更多
Molecular cloning remains a cornerstone technique in genetic engineering and synthetic biology.In this study,we conducted a systematic comparative analysis between the classical cloning method and the Golden Gate asse...Molecular cloning remains a cornerstone technique in genetic engineering and synthetic biology.In this study,we conducted a systematic comparative analysis between the classical cloning method and the Golden Gate assembly technique,utilizing Escherichia coli as the model organism.Through polymerase chain reaction(PCR)amplification,restriction enzyme digestion,ligation,transformation,and Sanger sequencing,we assessed the operational efficiency and cloning fidelity of both strategies.Our results demonstrated that Golden Gate assembly,leveraging type IIS restriction enzymes and simultaneous ligation,significantly enhanced cloning efficiency and precision,particularly for seamless multi-fragment assembly.In contrast,the classical cloning approach maintained certain advantages in simplicity and robustness for specific experimental conditions.Challenges encountered during transformation and sequencing highlighted the critical impact of technical accuracy on experimental outcomes.This study underscores the importance of selecting appropriate cloning methodologies tailored to experimental objectives and laboratory capabilities,providing a foundation for optimized molecular cloning workflows in future synthetic biology and biotechnology applications.展开更多
BACKGROUND Surgically created arterio-venous fistulas(AVFs)are the gold standard for haemodialysis access for patients with end-stage renal disease.Standard practice of AVF creation involves selecting the non-dominant...BACKGROUND Surgically created arterio-venous fistulas(AVFs)are the gold standard for haemodialysis access for patients with end-stage renal disease.Standard practice of AVF creation involves selecting the non-dominant upper limb and starting with most distally with radio-cephalic arterio-venous fistula.The primary patency rate of radio-cephalic arterio-venous fistula varies from 20%-25%.It has been suggested the neointimal hyperplasia at the mobilized venous segment causes stenosis of the anastomosis.Therefore,the radial artery deviation and reimplantation(RADAR)technique,in which the vein is minimally mobilized,should result in a higher success rate.AIM To compare the RADAR technique with classical technique in creation of AVF including:(1)Success rate;(2)Time to maturation;(3)Duration of surgery;and(4)Complication rate.METHODS In our study we recruited 94 patients in two randomized groups and performed the AVF by the classical method or the RADAR method.RESULTS The RADAR group had higher primary success rate(P=0.007),less rate of complications(P=0.04),shorter duration of surgery(P=0.00)and early time to maturation(0.001)when compared with the classical group.The RADAR procedure is a safe and a more efficient alternative to the current classical method of AVF creation.Longer duration of follow-up is required to assess the long-term outcomes in the future.CONCLUSION The RADAR procedure is a safe and more efficient alternative to the current classical method of AVF creation.Longer duration of follow-up is required to assess the long-term outcomes in the future.展开更多
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金Supported by National Natural Science Foundation of China(Grant No.52475280)Shaanxi Provincial Natural Science Basic Research Program(Grant No.2025SYSSYSZD-105).
文摘Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.
文摘This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.
基金supported by State Grid Corporation Technology Project(No.522437250003).
文摘Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most promising directions of development.This paper proposes an optimized schedulingmodel for a hydrogen-coupled electro-heat-gas integrated energy system(HCEHG-IES)using generative adversarial imitation learning(GAIL).The model aims to enhance renewable-energy absorption,reduce carbon emissions,and improve grid-regulation flexibility.First,the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process(MDP).To overcome the limitations of conventional deep reinforcement learning algorithms—including long optimization time,slow convergence,and subjective reward design—this study augments the PPO algorithm by incorporating a discriminator network and expert data.The newly developed algorithm,termed GAIL,enables the agent to perform imitation learning from expert data.Based on this model,dynamic scheduling decisions are made in continuous state and action spaces,generating optimal energy-allocation and management schemes.Simulation results indicate that,compared with traditional reinforcement-learning algorithms,the proposed algorithmoffers better economic performance.Guided by expert data,the agent avoids blind optimization,shortens the offline training time,and improves convergence performance.In the online phase,the algorithm enables flexible energy utilization,thereby promoting renewable-energy absorption and reducing carbon emissions.
基金supported by the Science and Technology Project of State Grid Corporation of China(5419-202199552A-0-5-ZN).
文摘Explorations into new electrolytes have highlighted the critical impact of solvation structure on battery performance,Classical molecular dynamics(CMD)using semi-empirical force fields has become an essential tool for simulating solvation structures.However,mainstream force fields often lack accuracy in describing strong ion-solvent interactions,causing disparities between CMD simulations and experimental observations.Although some empirical methods have been employed in some of the studies to address this issue,their effectiveness has been limited.Our CMD research,supported by quantum chemical calculations and experimental data,reveals that the solvation structure is influenced not only by the charge model but also by the polarization description.Previous empirical approaches that focused solely on adjusting ion-solvent interaction strengths overlooked the importance of polarization effects.Building on this insight,we propose integrating the Drude polarization model into mainstream force fields and verify its feasibility in carbonate,ether,and nitrile electrolytes.Our experimental results demonstrate that this approach significantly enhances the accuracy of CMD-simulated solvation structures.This work is expected to provide a more reliable CMD method for electrolyte design,shielding researchers from the pitfalls of erroneous simulation outcomes.
基金supported by the SERB-Core Research Grant(Project RD/0122-SERB000-044)。
文摘The presence of background classical sources affects quantum field theory significantly in different ways.Neutrino oscillation is a phenomenon that confirms that neutrinos are massive fermions in nature,a celebrated result in modern physics.Neutrino oscillation plays an important role in many astrophysical observations.However,the interactions between the background classical sources with neutrinos are not often considered.In the present article,we show the effect of some classical sources,namely matter currents,electromagnetic waves,torsion,and gravitational waves on neutrino oscillation.It is shown explicitly that the above sources can change the helicity state of neutrinos during neutrino oscillation.
文摘Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of this botanical motif necessitates not merely lexical equivalence,but more importantly,the transmission of its profound cultural resonance and aesthetic essence.This study posits that effective rendition of grass imagery should adopt an integrative approach synthesizing the objectives of cultural translation with the intrinsic aesthetic characteristics of classical poetry.Through systematic analysis of the cultural semiotics embedded in grass symbolism,the research investigates practical translation techniques at lexical,syntactic,and stylistic dimensions.The findings aim to contribute to the theoretical framework of cultural image translation in Chinese poetic tradition while providing methodological references for cross-cultural interpretation of classical verse.By bridging cultural semiotics with translation praxis,this investigation seeks to advance the intercultural communication of Chinese poetic heritage through nuanced treatment of its botanical symbolism.
基金supported by the Natural Science Foundation of Shandong Province of China(ZR2024MA033ZR2021QA049).
文摘This paper concerns the Cauchy problem of 3D compressible micropolar fluids in the whole space R^(3). For regular initial data with m0E0 is suitable small, where m0 and E0 represent the upper bound of initial density and initial energy, we prove that if ρ0 ∈ Lγ ∩ H3 with γ ∈ (1, 6), then the problem possesses a unique global classical solution on R^(3) × [0, T] with any T ∈ (0, ∞). It’s worth noting that both the vacuum states and possible random largeness of initial energy are allowed.
文摘More than a simple concert pianist,Wilson Chu is an Indonesian musical diplomacy force.As a distinguished performer,composer,and educator,Wilson has left an indelible mark on the international stage by seamlessly blending Western classical music with Southeast Asian tradition.Since 2019,he has been the youngest Associate Professor of Piano at the College of Chinese and ASEAN Arts(CCAA)at Chengdu University,where he has dedicated himself to shaping the next generation of musicians.
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
基金supported by the National Natural Science Foundation of China(Grant Nos.62473371 and 61673389)。
文摘Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era.
基金financially supported by the Key Technologies R&D Program of China National Offshore Oil Corporation(No.KJGG2021-0504)。
文摘Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In this work,we develop Atif-V2.0,an extended classical density functional theory(cDFT)framework that integrates the interfacial statistical associating fluid theory(iSAFT)to model multicomponent associating fluids composed of water-soluble polymers,alkanes,and water.Building on the original theoretical framework of Atif for modeling nanoconfined inhomogeneous fluids,Atif-V2.0 embeds explicit solvent and captures additional physical interactions-hydrogen bonding,which are critical in associating fluid systems.The other key feature of Atif-V2.0 is its ability to account for polymer topology.We demonstrate its capability by predicting the equilibrium structure and thermodynamic behavior of branched hydrolyzed polyacrylamide solutions near hard walls with various branching topologies,which provides a robust theoretical tool for the rational design of EOR polymers.
基金supported by the National Natural Science Foundation of China(Grant Nos.92365202,12475011,and 11921005)the National Key R&D Program of China(Grant No.2024YFA1409002)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.
文摘Molecular cloning remains a cornerstone technique in genetic engineering and synthetic biology.In this study,we conducted a systematic comparative analysis between the classical cloning method and the Golden Gate assembly technique,utilizing Escherichia coli as the model organism.Through polymerase chain reaction(PCR)amplification,restriction enzyme digestion,ligation,transformation,and Sanger sequencing,we assessed the operational efficiency and cloning fidelity of both strategies.Our results demonstrated that Golden Gate assembly,leveraging type IIS restriction enzymes and simultaneous ligation,significantly enhanced cloning efficiency and precision,particularly for seamless multi-fragment assembly.In contrast,the classical cloning approach maintained certain advantages in simplicity and robustness for specific experimental conditions.Challenges encountered during transformation and sequencing highlighted the critical impact of technical accuracy on experimental outcomes.This study underscores the importance of selecting appropriate cloning methodologies tailored to experimental objectives and laboratory capabilities,providing a foundation for optimized molecular cloning workflows in future synthetic biology and biotechnology applications.
文摘BACKGROUND Surgically created arterio-venous fistulas(AVFs)are the gold standard for haemodialysis access for patients with end-stage renal disease.Standard practice of AVF creation involves selecting the non-dominant upper limb and starting with most distally with radio-cephalic arterio-venous fistula.The primary patency rate of radio-cephalic arterio-venous fistula varies from 20%-25%.It has been suggested the neointimal hyperplasia at the mobilized venous segment causes stenosis of the anastomosis.Therefore,the radial artery deviation and reimplantation(RADAR)technique,in which the vein is minimally mobilized,should result in a higher success rate.AIM To compare the RADAR technique with classical technique in creation of AVF including:(1)Success rate;(2)Time to maturation;(3)Duration of surgery;and(4)Complication rate.METHODS In our study we recruited 94 patients in two randomized groups and performed the AVF by the classical method or the RADAR method.RESULTS The RADAR group had higher primary success rate(P=0.007),less rate of complications(P=0.04),shorter duration of surgery(P=0.00)and early time to maturation(0.001)when compared with the classical group.The RADAR procedure is a safe and a more efficient alternative to the current classical method of AVF creation.Longer duration of follow-up is required to assess the long-term outcomes in the future.CONCLUSION The RADAR procedure is a safe and more efficient alternative to the current classical method of AVF creation.Longer duration of follow-up is required to assess the long-term outcomes in the future.
