This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
Discussions about the future of energy sources and environmental sustainability are becoming critical on a global scale.The energy sector plays a central role in the economy,as the availability and cost of energy infl...Discussions about the future of energy sources and environmental sustainability are becoming critical on a global scale.The energy sector plays a central role in the economy,as the availability and cost of energy influence the competitiveness of economies,while the level of energy consumption impacts the standard of living for individuals.This paper aims to examine environmental challenges and steps for a sustainable transition towards a hydrogen economy,focusing on its potential as an alternative to fossil fuels and the importance of developing the hydrogen paradigm.The research methodology is based on a combination of qualitative and quantitative methods,including an analysis of global and regional trends in the energy transition,the impact of various forms of hydrogen production(green,blue,gray hydrogen)on greenhouse gas emissions,and a comparison of existing policies and strategies in different countries transitioning to a sustainable hydrogen economy.Research results show that green hydrogen,produced via electrolysis using renewable energy sources,holds the greatest potential for reducing greenhouse gas emissions,while gray and blue hydrogen can serve as transitional options.The development of the hydrogen paradigm,rooted in innovative technologies,renewable energy sources,and international cooperation,is crucial for decarbonization and the creation of a sustainable global economy,despite challenges such as high costs and the need for global coordination.The hydrogen paradigm is becoming a cornerstone of these efforts,laying the foundation for a long-term,sustainable global economy.Currently,over 180 hydrogen transport projects,60 distribution projects,80 storage projects,30 terminal and port projects,and more than 220 hydrogen production projects are under development worldwide.The global momentum of the hydrogen transition helps mitigate climate change and build a sustainable future.展开更多
This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis f...This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.展开更多
To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat...To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-lat...Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers.In this study,we examine the May 10–11,2024,superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model(TIEGCM)with observation-constrained high-latitude forcing.Auroral precipitation parameters(energy flux and mean energy)are assimilated from a Defense Meteorological Satellite Program(DMSP)Special Sensor Ultraviolet Spectrographic Imager(SSUSI)using a multi-resolution Gaussian process(Lattice Kriging)approach,whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network(SuperDARN)observations with the Thomas and Shepherd(2018)model(TS18).For comparison,an additional simulation is performed using empirical models for both convection and auroral forcing.The results show that during the main phase of the May 10 storm,the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity.These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI.Above the ionospheric F2 peak over the SED source region,SuperDARN-constrained potentials generate stronger and more localized E×B drifts that dominate plasma uplift and drive its transport into the polar cap,although neutral winds and downward ambipolar diffusion partially offset these effects.Below the F2 peak,neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI.These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance.展开更多
Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established ba...Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.展开更多
The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoir...The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties.展开更多
The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes...The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.展开更多
Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory v...Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.展开更多
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innova...The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innovative concept of the“AI-Driven Research Ecosystem”,a dynamic and collaborative research environment.Within this ecosystem,we focus on the unification of human-AI collaboration models and the emerging new research thinking paradigms.We analyze the multifaceted roles of AI within the research lifecycle,spanning from a passive tool to an active assistant and autonomous participants,and categorize these interactions into distinct human-AI collaboration models.Furthermore,we examine how the pervasive involvement of AI necessitates an evolution in human research thinking,emphasizing the significant roles of critical,creative,and computational thinking.Through a review of existing literature and illustrative case studies,this paper provides a comprehensive overview of the AI-driven research ecosystem,highlighting its potential for transforming scientific research.Our findings advance the current understanding of AI’s multiple roles in research and underscore its capacity to revolutionize both knowledge discovery and collaborative innovation,paving the way for a more integrated and impactful research paradigm.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
BACKGROUND Research has consistently demonstrated that patients with major depressive disorder(MDD)exhibit attentional switching dysfunction,and the dual-task paradigm has emerged as a valuable tool for probing cognit...BACKGROUND Research has consistently demonstrated that patients with major depressive disorder(MDD)exhibit attentional switching dysfunction,and the dual-task paradigm has emerged as a valuable tool for probing cognitive deficits.However,the neuroelectrophysiological mechanism underlying this deficit has not been clarified.AIM To investigate the event-related potential(ERP)characteristics of attentional switching dysfunction and further explore the neuroelectrophysiological mechanism of the cognitive processing deficits underlying attentional switching dysfunction in MDD.METHODS The participants included 29 MDD patients and 29 healthy controls(HCs).The ERPs of the participants were measured while they performed the dual-task para digm.The behavioral and ERP N100,P200,P300,and late positive potential(LPP)data were analyzed.RESULTS This study revealed greater accuracy in HCs and slower reaction times(RTs)in MDD patients.Angry facial pictures led to lower accuracy.The results also revealed shorter RTs for happy facial pictures and the longest RTs for the 500-ms stimulus onset asynchrony.With respect to ERP characteristics,happy facial pictures and neutral facial pictures evoked higher amplitudes.The N100,P200,P300,and LPP amplitudes at Pz were the highest.MDD patients had lower P200 mean amplitudes and LPP amplitudes than HCs did.CONCLUSION In conclusion,MDD patients exhibited abnormal ERP characteristics evoked by the dual-task paradigm,which could be the neural correlates of the known abnormalities in attentional switching in patients with MDD.These results provide valuable insights into the understanding of the neural mechanisms of attentional switching function and may guide targeted interventions in patients with MDD.展开更多
Digital-intelligent technologies represent the advanced direction of new quality productive forces and are becoming a driving force for the digital transformation and high-quality development of the cultural industry....Digital-intelligent technologies represent the advanced direction of new quality productive forces and are becoming a driving force for the digital transformation and high-quality development of the cultural industry.Empowered by new quality productive forces,the digital cultural industry has demonstrated diverse characteristics,including the innovation of cultural production subjects,the intelligentization of production tools,the digitization of production objects,the systematization of production methods,and the diversification of production factors.Leveraging technologies such as AIGC,virtual-physical integration,and DAOs based on Web 3.0,the digital cultural industry has established an innovative paradigm,fostering a new method of AIGC production in the digital cultural industry,a new business format of virtual-physical integration,and a new collaborative ecosystem characterized by co-creation,co-building,and co-governance.Meanwhile,the innovative paradigm of the digital cultural industry also faces a series of new challenges,such as the adaptability issues with AIGC algorithm models,creative bottlenecks,and content quality control problems.Additionally,there are obstacles like the immaturity of international development channels for new business formats,the lack of cultural connotations in creative products,and the lag of the digital-intelligent governance of the industry ecosystem behind digital practices.In light of this,there is an urgent need to establish an optimization mechanism for the high-quality development of digital cultural industries driven by new quality productive forces.This includes optimizing the content production mechanism for AIGC-led high-quality innovation in the digital cultural industry;improving the leapfrog development mechanism for new digital cultural business formats through global-regional collaboration;and enhancing the accurate,high-quality governance mechanism for the digital cultural industry that is aligned with the goals of Chinese modernization.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
This study examines the advent of agent interaction(AIx)as a transformative paradigm in humancomputer interaction(HCI),signifying a notable evolution beyond traditional graphical interfaces and touchscreen interaction...This study examines the advent of agent interaction(AIx)as a transformative paradigm in humancomputer interaction(HCI),signifying a notable evolution beyond traditional graphical interfaces and touchscreen interactions.Within the context of large models,AIx is characterized by its innovative interaction patterns and a plethora of application scenarios that hold great potential.The paper highlights the pivotal role of AIx in shaping the future landscape of the large model industry,emphasizing its adoption and necessity from a user's perspective.This study underscores the pivotal role of AIx in dictating the future trajectory of a large model industry by emphasizing the importance of its adoption and necessity from a user-centric perspective.The fundamental drivers of AIx include the introduction of novel capabilities,replication of capabilities(both anthropomorphic and superhuman),migration of capabilities,aggregation of intelligence,and multiplication of capabilities.These elements are essential for propelling innovation,expanding the frontiers of capability,and realizing the exponential superposition of capabilities,thereby mitigating labor redundancy and addressing a spectrum of human needs.Furthermore,this study provides an in-depth analysis of the structural components and operational mechanisms of agents supported by large models.Such advancements significantly enhance the capacity of agents to tackle complex problems and provide intelligent services,thereby facilitating a more intuitive,adaptive,and personalized engagement between humans and machines.The study further delineates four principal categories of interaction patterns that encompass eight distinct modalities of interaction,corresponding to twenty-one specific scenarios,including applications in smart home systems,health assistance,and elderly care.This emphasizes the significance of this new paradigm in advancing HCI,fostering technological advancements,and redefining user experiences.However,it also acknowledges the challenges and ethical considerations that accompany this paradigm shift,recognizing the need for a balanced approach to harness the full potential of AIx in modern society.展开更多
With the continuous improvement of the medical industry’s requirements for the professional capabilities of nursing talents,traditional nursing teaching models can hardly meet the needs of complex nursing work in neu...With the continuous improvement of the medical industry’s requirements for the professional capabilities of nursing talents,traditional nursing teaching models can hardly meet the needs of complex nursing work in neurology.This paper focuses on nursing education for neurology nursing students and explores the construction of the“one-on-one”teaching model,aiming to achieve a paradigm shift in nursing education.By analyzing the current status of neurology nursing education,this paper identifies the problems in traditional teaching models.Combining the advantages of the“one-on-one”teaching model,it elaborates on the construction path of this model from aspects such as the selection and training of teaching instructors,the design of teaching content,the innovation of teaching methods,and the improvement of the teaching evaluation system.The research shows that the“one-on-one”teaching model can significantly enhance nursing students’mastery of professional knowledge,clinical operation skills,communication skills,and emergency response capabilities,as well as strengthen their professional identity and sense of responsibility.It provides an effective way to cultivate high-quality nursing talents who can meet the needs of neurology nursing work and promotes the innovative development of nursing education.展开更多
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
文摘Discussions about the future of energy sources and environmental sustainability are becoming critical on a global scale.The energy sector plays a central role in the economy,as the availability and cost of energy influence the competitiveness of economies,while the level of energy consumption impacts the standard of living for individuals.This paper aims to examine environmental challenges and steps for a sustainable transition towards a hydrogen economy,focusing on its potential as an alternative to fossil fuels and the importance of developing the hydrogen paradigm.The research methodology is based on a combination of qualitative and quantitative methods,including an analysis of global and regional trends in the energy transition,the impact of various forms of hydrogen production(green,blue,gray hydrogen)on greenhouse gas emissions,and a comparison of existing policies and strategies in different countries transitioning to a sustainable hydrogen economy.Research results show that green hydrogen,produced via electrolysis using renewable energy sources,holds the greatest potential for reducing greenhouse gas emissions,while gray and blue hydrogen can serve as transitional options.The development of the hydrogen paradigm,rooted in innovative technologies,renewable energy sources,and international cooperation,is crucial for decarbonization and the creation of a sustainable global economy,despite challenges such as high costs and the need for global coordination.The hydrogen paradigm is becoming a cornerstone of these efforts,laying the foundation for a long-term,sustainable global economy.Currently,over 180 hydrogen transport projects,60 distribution projects,80 storage projects,30 terminal and port projects,and more than 220 hydrogen production projects are under development worldwide.The global momentum of the hydrogen transition helps mitigate climate change and build a sustainable future.
基金Chengdu City Philosophy and Social Sciences Research Center“artificial intelligence+urban communication”theory and Application Research Center Project“Chengdu real estate vertical market public opinion data visualization research”(Project No.RZCC2025017).
文摘This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.
基金supported in part by the National Natural Science Foundation of China,Grant/Award Number:62003267the Key Research and Development Program of Shaanxi Province,Grant/Award Number:2023-GHZD-33Open Project of the State Key Laboratory of Intelligent Game,Grant/Award Number:ZBKF-23-05。
文摘To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金The Shandong Provincial Natural Science Foundation(Grant No.ZR2022JQ18)supported this worksupported by the National Natural Science Foundation of China(NNFSC)Youth Program(Grant No.42304168)+1 种基金supported by the National Key R&D Program of China(Grant No.2022YFF0504400)the NNSFC(Grant Nos.42188101 and 42174210)。
文摘Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers.In this study,we examine the May 10–11,2024,superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model(TIEGCM)with observation-constrained high-latitude forcing.Auroral precipitation parameters(energy flux and mean energy)are assimilated from a Defense Meteorological Satellite Program(DMSP)Special Sensor Ultraviolet Spectrographic Imager(SSUSI)using a multi-resolution Gaussian process(Lattice Kriging)approach,whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network(SuperDARN)observations with the Thomas and Shepherd(2018)model(TS18).For comparison,an additional simulation is performed using empirical models for both convection and auroral forcing.The results show that during the main phase of the May 10 storm,the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity.These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI.Above the ionospheric F2 peak over the SED source region,SuperDARN-constrained potentials generate stronger and more localized E×B drifts that dominate plasma uplift and drive its transport into the polar cap,although neutral winds and downward ambipolar diffusion partially offset these effects.Below the F2 peak,neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI.These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance.
基金Supported by the School-level Project of Sichuan Minzu College(XYZB2017ZB).
文摘Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.
基金funded by the Technical Development(Entrusted)Project of Science and Department of SINOPEC(Grant No.P23240-4)the National Natural Science Foundation of China(Grant Nos.42172165,42272143 and 2025ZD1403901-05)。
文摘The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties.
基金Key Research and Development Program of Hubei Province,China(Grant No.2022BAA096)Zhejiang Provincial Natural Science Foundation of China(This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No.LR25A020002)support of the Center for Materials Analysis and Characterization,Material Characterization Lab,and Nanofabrication Lab at Hubei University。
文摘The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.
文摘Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金funded by the General Program of the National Natural Science Foundation of China grant number 62277022.
文摘The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innovative concept of the“AI-Driven Research Ecosystem”,a dynamic and collaborative research environment.Within this ecosystem,we focus on the unification of human-AI collaboration models and the emerging new research thinking paradigms.We analyze the multifaceted roles of AI within the research lifecycle,spanning from a passive tool to an active assistant and autonomous participants,and categorize these interactions into distinct human-AI collaboration models.Furthermore,we examine how the pervasive involvement of AI necessitates an evolution in human research thinking,emphasizing the significant roles of critical,creative,and computational thinking.Through a review of existing literature and illustrative case studies,this paper provides a comprehensive overview of the AI-driven research ecosystem,highlighting its potential for transforming scientific research.Our findings advance the current understanding of AI’s multiple roles in research and underscore its capacity to revolutionize both knowledge discovery and collaborative innovation,paving the way for a more integrated and impactful research paradigm.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金Supported by Wuxi Taihu Talent Project,No.WXTTP 2021the General Scientific Research Program of Wuxi Municipal Health Commission,No.M202447.
文摘BACKGROUND Research has consistently demonstrated that patients with major depressive disorder(MDD)exhibit attentional switching dysfunction,and the dual-task paradigm has emerged as a valuable tool for probing cognitive deficits.However,the neuroelectrophysiological mechanism underlying this deficit has not been clarified.AIM To investigate the event-related potential(ERP)characteristics of attentional switching dysfunction and further explore the neuroelectrophysiological mechanism of the cognitive processing deficits underlying attentional switching dysfunction in MDD.METHODS The participants included 29 MDD patients and 29 healthy controls(HCs).The ERPs of the participants were measured while they performed the dual-task para digm.The behavioral and ERP N100,P200,P300,and late positive potential(LPP)data were analyzed.RESULTS This study revealed greater accuracy in HCs and slower reaction times(RTs)in MDD patients.Angry facial pictures led to lower accuracy.The results also revealed shorter RTs for happy facial pictures and the longest RTs for the 500-ms stimulus onset asynchrony.With respect to ERP characteristics,happy facial pictures and neutral facial pictures evoked higher amplitudes.The N100,P200,P300,and LPP amplitudes at Pz were the highest.MDD patients had lower P200 mean amplitudes and LPP amplitudes than HCs did.CONCLUSION In conclusion,MDD patients exhibited abnormal ERP characteristics evoked by the dual-task paradigm,which could be the neural correlates of the known abnormalities in attentional switching in patients with MDD.These results provide valuable insights into the understanding of the neural mechanisms of attentional switching function and may guide targeted interventions in patients with MDD.
基金funded by Research on Policy Design and Implementation Path for High-Quality Development of Digital Cultural Industry(23&ZD087),a major project of the National Social Science Foundation of China.
文摘Digital-intelligent technologies represent the advanced direction of new quality productive forces and are becoming a driving force for the digital transformation and high-quality development of the cultural industry.Empowered by new quality productive forces,the digital cultural industry has demonstrated diverse characteristics,including the innovation of cultural production subjects,the intelligentization of production tools,the digitization of production objects,the systematization of production methods,and the diversification of production factors.Leveraging technologies such as AIGC,virtual-physical integration,and DAOs based on Web 3.0,the digital cultural industry has established an innovative paradigm,fostering a new method of AIGC production in the digital cultural industry,a new business format of virtual-physical integration,and a new collaborative ecosystem characterized by co-creation,co-building,and co-governance.Meanwhile,the innovative paradigm of the digital cultural industry also faces a series of new challenges,such as the adaptability issues with AIGC algorithm models,creative bottlenecks,and content quality control problems.Additionally,there are obstacles like the immaturity of international development channels for new business formats,the lack of cultural connotations in creative products,and the lag of the digital-intelligent governance of the industry ecosystem behind digital practices.In light of this,there is an urgent need to establish an optimization mechanism for the high-quality development of digital cultural industries driven by new quality productive forces.This includes optimizing the content production mechanism for AIGC-led high-quality innovation in the digital cultural industry;improving the leapfrog development mechanism for new digital cultural business formats through global-regional collaboration;and enhancing the accurate,high-quality governance mechanism for the digital cultural industry that is aligned with the goals of Chinese modernization.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘This study examines the advent of agent interaction(AIx)as a transformative paradigm in humancomputer interaction(HCI),signifying a notable evolution beyond traditional graphical interfaces and touchscreen interactions.Within the context of large models,AIx is characterized by its innovative interaction patterns and a plethora of application scenarios that hold great potential.The paper highlights the pivotal role of AIx in shaping the future landscape of the large model industry,emphasizing its adoption and necessity from a user's perspective.This study underscores the pivotal role of AIx in dictating the future trajectory of a large model industry by emphasizing the importance of its adoption and necessity from a user-centric perspective.The fundamental drivers of AIx include the introduction of novel capabilities,replication of capabilities(both anthropomorphic and superhuman),migration of capabilities,aggregation of intelligence,and multiplication of capabilities.These elements are essential for propelling innovation,expanding the frontiers of capability,and realizing the exponential superposition of capabilities,thereby mitigating labor redundancy and addressing a spectrum of human needs.Furthermore,this study provides an in-depth analysis of the structural components and operational mechanisms of agents supported by large models.Such advancements significantly enhance the capacity of agents to tackle complex problems and provide intelligent services,thereby facilitating a more intuitive,adaptive,and personalized engagement between humans and machines.The study further delineates four principal categories of interaction patterns that encompass eight distinct modalities of interaction,corresponding to twenty-one specific scenarios,including applications in smart home systems,health assistance,and elderly care.This emphasizes the significance of this new paradigm in advancing HCI,fostering technological advancements,and redefining user experiences.However,it also acknowledges the challenges and ethical considerations that accompany this paradigm shift,recognizing the need for a balanced approach to harness the full potential of AIx in modern society.
文摘With the continuous improvement of the medical industry’s requirements for the professional capabilities of nursing talents,traditional nursing teaching models can hardly meet the needs of complex nursing work in neurology.This paper focuses on nursing education for neurology nursing students and explores the construction of the“one-on-one”teaching model,aiming to achieve a paradigm shift in nursing education.By analyzing the current status of neurology nursing education,this paper identifies the problems in traditional teaching models.Combining the advantages of the“one-on-one”teaching model,it elaborates on the construction path of this model from aspects such as the selection and training of teaching instructors,the design of teaching content,the innovation of teaching methods,and the improvement of the teaching evaluation system.The research shows that the“one-on-one”teaching model can significantly enhance nursing students’mastery of professional knowledge,clinical operation skills,communication skills,and emergency response capabilities,as well as strengthen their professional identity and sense of responsibility.It provides an effective way to cultivate high-quality nursing talents who can meet the needs of neurology nursing work and promotes the innovative development of nursing education.
