Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results ca...Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.展开更多
Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriousl...Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriously hinder its development.Herein,we report a robust route to a liquid metal(LM)/polyvinyl alcohol(PVA)hydrogel-based TENG(LP-TENG).Owing to the intrinsically liquid feature of conductive LM within the flexible PVA hydrogel,the as-prepared LP-TENG exhibited comprehensiye advantages of adaptability,biocompatibility,outstanding electrical performance,superior stability,recyclability and diverse applications,which were unattainable by traditional systems.Concretely,the LP-TENG delivered appealing open circuit voltage of 250 V,short circuit current of 4μA and transferred charge of 120 nC with high stability,outperforming most advanced TENG systems.The LP-TENG was successfully employed for versatile applications with multifunctionality,including human motion detection,handwriting recognition,energy collection,message transmission and human-machine interaction.This work presents significant prospects for crafting advanced materials and devices in the fields of wearable electronics,flexible skin and smart robots.展开更多
Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetec...Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.展开更多
Fertilization or atmospheric deposition of nitrogen(N)and phosphorus(P)to terrestrial ecosystems can alter soil N(P)availability and the nature of nutrient limitation for plant growth.Changing the allocation of leaf P...Fertilization or atmospheric deposition of nitrogen(N)and phosphorus(P)to terrestrial ecosystems can alter soil N(P)availability and the nature of nutrient limitation for plant growth.Changing the allocation of leaf P fractions is potentially an adaptive strategy for plants to cope with soil N(P)availability and nutrient-limiting conditions.However,the impact of the interactions between imbalanced anthropogenic N and P inputs on the concentrations and allocation proportions of leaf P fractions in forest woody plants remains elusive.We conducted a metaanalysis of data about the concentrations and allocation proportions of leaf P fractions,specifically associated with individual and combined additions of N and P in evergreen forests,the dominant vegetation type in southern China where the primary productivity is usually considered limited by P.This assessment allowed us to quantitatively evaluate the effects of N and P additions alone and interactively on leaf P allocation and use strategies.Nitrogen addition(exacerbating P limitation)reduced the concentrations of leaf total P and different leaf P fractions.Nitrogen addition reduced the allocation to leaf metabolic P but increased the allocation to other fractions,while P addition showed opposite trends.The simultaneous additions of N and P showed an antagonistic(mutual suppression)effect on the concentrations of leaf P fractions,but an additive(summary)effect on the allocation proportions of leaf P fractions.These results highlight the importance of strategies of leaf P fraction allocation in forest plants under changes in environmental nutrient availability.Importantly,our study identified critical interactions associated with combined N and P inputs that affect leaf P fractions,thus aiding in predicting plant acclimation strategies in the context of intensifying and imbalanced anthropogenic nutrient inputs.展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themse...The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.展开更多
As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functi...As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functionality or disabilities remain underexplored.Here,we devised a wearable one-handed keyboard with gesture recognition,employing machine learning algorithms and hydrogel-based mechanical sensors to boost productivity.PCG(PAM/CMC/rGO)hydrogels are composed of polyacrylamide(PAM),sodium carboxymethyl cellulose(CMC),and reduced graphene oxide(rGO),which function as a strain,pressure sensor,and electrode material.The PAM chains offer the gel’s elasticity by covalent cross-linking,while the biocompatible CMC improves the dispersion of rGO and promotes electromechanical properties.Integrating rGO sheets into the polymer matrix facilitates cross-linking and generates supple-mentary conductive pathways,thereby augmenting the gel system’s elasticity,sensitivity,and durability.Our hydrogel sensors include high sensitivity(gage factor(GF)=8.18,395.6%-551.96%)and superior pressure sensing capabilities(Sensitivity(S)=0.3116 kPa^(-1),0-9.82 kPa).Furthermore,we developed a wearable keyboard with up to 98.13%accuracy using convolutional neural networks and a custom data acquisition system.This study establishes the groundwork for creating multifunctional gel sensors for intelligent machines,wearable devices,and brain-computer interfaces.展开更多
Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as...Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as they provide interaction platforms between users and virtual environments.However,traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional(3D)gestures,which results in limited freedom of VR interactions.Here,we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor(ETS),which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane(PLA/TPU)electret nanofiber film.The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability.Integrated with a deep learning-based multilayer perceptron neural network,the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%.An intelligent noncontact VR interactive system based on the ETS is further developed,which is used to manipulate game characters for performing different actions by 3D gestures.Compared with traditional VR HMIs,the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom.This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs,and could make the interaction between human and virtual environments become more efficient and fascinating.展开更多
Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interactio...Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.展开更多
Objective To assess the independent and combined effects of air pollutants,meteorological factors,and greenspace exposure on new tuberculosis(TB)cases.Methods TB case data from Shanghai(2013–2018)were obtained from t...Objective To assess the independent and combined effects of air pollutants,meteorological factors,and greenspace exposure on new tuberculosis(TB)cases.Methods TB case data from Shanghai(2013–2018)were obtained from the Shanghai Center for Disease Control and Prevention.Environmental data on air pollutants,meteorological variables,and greenspace exposure were obtained from the National Tibetan Plateau Data Center.We employed a distributed-lag nonlinear model to assess the effects of these environmental factors on TB cases.Results Increased TB risk was linked to PM_(2.5),PM_(10),and rainfall,whereas NO_(2),SO_(2),and air pressure were associated with a reduced risk.Specifically,the strongest cumulative effects occurred at various lags:PM_(2.5)(RR=1.166,95%CI:1.026–1.325)at 0–19 weeks;PM_(10)(RR=1.167,95%CI:1.028–1.324)at 0–18 weeks;NO_(2)(RR=0.968,95%CI:0.938–0.999)at 0–1 weeks;SO_(2)(RR=0.945,95%CI:0.894–0.999)at 0–2 weeks;air pressure(RR=0.604,95%CI:0.447–0.816)at 0–8 weeks;and rainfall(RR=1.404,95%CI:1.076–1.833)at 0–22 weeks.Green space exposure did not significantly impact TB cases.Additionally,low temperatures amplified the effect of PM_(2.5)on TB.Conclusion Exposure to PM_(2.5),PM_(10),and rainfall increased the risk of TB,highlighting the need to address air pollutants for the prevention of TB in Shanghai.展开更多
Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form ...Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.展开更多
Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-g...Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.展开更多
With the development of globalization,intercultural communicative competence has become one of the core qualities of modern college students.As an important platform to cultivate students’language skills and cultural...With the development of globalization,intercultural communicative competence has become one of the core qualities of modern college students.As an important platform to cultivate students’language skills and cultural literacy,the innovation of college English teaching mode is essential.Based on this,this paper mainly discusses methods to effectively cultivate students’intercultural communicative competence in college English teaching from the perspective of multimodal interactive teaching mode,hoping to provide references for improving the quality of college English teaching and students’comprehensive quality.展开更多
In an aging society,the interface design of smart home products is crucial to the quality of life of the elderly.This paper combines Quality Function Deployment(QFD)and Human-Computer Interaction(HCI)theories,taking s...In an aging society,the interface design of smart home products is crucial to the quality of life of the elderly.This paper combines Quality Function Deployment(QFD)and Human-Computer Interaction(HCI)theories,taking smart washing machines as an example,to explore new paths for the interface design of smart home products for the elderly.An interdisciplinary approach is adopted to construct a design process centered on elderly users,introduce the Kano model to classify requirements,realize the mapping and sorting of requirements to design parameters,and adopt the PUGH model for comprehensive evaluation.This study provides practical and theoretical support for the interface design of smart home products for the elderly.展开更多
In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,inclu...Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,including limited anti-interference capabilities and the insufficient generalization of decision-making experience.To address the intricate problem of directional well trajectory control,an intelligent algorithm design framework grounded in the high-level interaction mechanism between geology and engineering is put forward.This framework aims to facilitate the rapid batch migration and update of drilling strategies.The proposed directional well trajectory control method comprehensively considers the multi-source heterogeneous attributes of drilling experience data,leverages the generative simulation of the geological drilling environment,and promptly constructs a directional well trajectory control model with self-adaptive capabilities to environmental variations.This construction is carried out based on three hierarchical levels:“offline pre-drilling learning,online during-drilling interaction,and post-drilling model transfer”.Simulation results indicate that the guidance model derived from this method demonstrates remarkable generalization performance and accuracy.It can significantly boost the adaptability of the control algorithm to diverse environments and enhance the penetration rate of the target reservoir during drilling operations.展开更多
Objective:To analyze the nursing effect of implementing continuous nursing combined with knowledge,attitude,and practice(KAP)intervention under the guidance of interactive goal-setting theory for patients with gestati...Objective:To analyze the nursing effect of implementing continuous nursing combined with knowledge,attitude,and practice(KAP)intervention under the guidance of interactive goal-setting theory for patients with gestational diabetes mellitus(GDM).Methods:A total of 68 patients with GDM who were admitted to the hospital between December 2021 and December 2023 were selected and randomly divided into two groups using a random number table.The combined group(34 cases)received continuous nursing combined with KAP intervention under the premise of interactive goal-setting theory,while the conventional group(34 cases)received routine nursing care.Blood glucose control,health behavior scores,and other indicators were compared between the two groups.Results:After nursing,the blood glucose level in the combined group was lower than that in the conventional group,and the scores for health behavior and self-management ability were higher than those in the conventional group(P<0.05).Conclusion:Combined nursing can improve blood glucose control in patients with GDM,standardize their health behaviors,and cultivate their self-management skills.展开更多
Combination flexible and stretchable textiles with self-powered sensors bring a novel insight into wearable functional electronics and cyber security in the era of Internet of Things.This work presents a highly flexib...Combination flexible and stretchable textiles with self-powered sensors bring a novel insight into wearable functional electronics and cyber security in the era of Internet of Things.This work presents a highly flexible and self-powered fully fabric-based triboelectric nanogenerator(F-TENG)with sandwiched structure for biomechanical energy harvesting and real-time biometric authentication.The prepared F-TENG can power a digital watch by low-frequency motion and respond to the pressure change by the fall of leaves.A self-powered wearable keyboard(SPWK)is also fabricated by integrating large-area F-TENG sensor arrays,which not only can trace and record electrophysiological signals,but also can identify individuals’typing characteristics by means of the Haar wavelet.Based on these merits,the SPWK has promising applications in the realm of wearable electronics,self-powered sensors,cyber security,and artificial intelligences.展开更多
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)the Scientific Research Project of PowerChina Huadong Engineering Corporation Limited(HDEC-2022-0301).
文摘Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.
基金financially supported by the Natural Science Foundation of China(Nos.22109120,62104170 and 82202757)Zhejiang Provincial Natural Science Foundation of China(Nos.LQ21B030002 and LY23F040001)。
文摘Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriously hinder its development.Herein,we report a robust route to a liquid metal(LM)/polyvinyl alcohol(PVA)hydrogel-based TENG(LP-TENG).Owing to the intrinsically liquid feature of conductive LM within the flexible PVA hydrogel,the as-prepared LP-TENG exhibited comprehensiye advantages of adaptability,biocompatibility,outstanding electrical performance,superior stability,recyclability and diverse applications,which were unattainable by traditional systems.Concretely,the LP-TENG delivered appealing open circuit voltage of 250 V,short circuit current of 4μA and transferred charge of 120 nC with high stability,outperforming most advanced TENG systems.The LP-TENG was successfully employed for versatile applications with multifunctionality,including human motion detection,handwriting recognition,energy collection,message transmission and human-machine interaction.This work presents significant prospects for crafting advanced materials and devices in the fields of wearable electronics,flexible skin and smart robots.
基金supported by the Fundamental Research Funds for the Central Universities(xxj022019009)。
文摘Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.
基金supported by the National Natural Science Foundation of China(No.41473068)supported by China Postdoctoral Science Foundation(No.2022M722667)。
文摘Fertilization or atmospheric deposition of nitrogen(N)and phosphorus(P)to terrestrial ecosystems can alter soil N(P)availability and the nature of nutrient limitation for plant growth.Changing the allocation of leaf P fractions is potentially an adaptive strategy for plants to cope with soil N(P)availability and nutrient-limiting conditions.However,the impact of the interactions between imbalanced anthropogenic N and P inputs on the concentrations and allocation proportions of leaf P fractions in forest woody plants remains elusive.We conducted a metaanalysis of data about the concentrations and allocation proportions of leaf P fractions,specifically associated with individual and combined additions of N and P in evergreen forests,the dominant vegetation type in southern China where the primary productivity is usually considered limited by P.This assessment allowed us to quantitatively evaluate the effects of N and P additions alone and interactively on leaf P allocation and use strategies.Nitrogen addition(exacerbating P limitation)reduced the concentrations of leaf total P and different leaf P fractions.Nitrogen addition reduced the allocation to leaf metabolic P but increased the allocation to other fractions,while P addition showed opposite trends.The simultaneous additions of N and P showed an antagonistic(mutual suppression)effect on the concentrations of leaf P fractions,but an additive(summary)effect on the allocation proportions of leaf P fractions.These results highlight the importance of strategies of leaf P fraction allocation in forest plants under changes in environmental nutrient availability.Importantly,our study identified critical interactions associated with combined N and P inputs that affect leaf P fractions,thus aiding in predicting plant acclimation strategies in the context of intensifying and imbalanced anthropogenic nutrient inputs.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
文摘The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.
基金supported by the China Postdoctoral Science Foundation(No.2022BG011)the Fundamental Research Funds for Central Universities(No.2020CDJ-LHZZ-077)+1 种基金the Natural Science Foundation of Chongqing,China(No.c stc2020jcyj-msxmX0397)the Fundamental Research Funds for Central Universities(No.00007717).
文摘As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functionality or disabilities remain underexplored.Here,we devised a wearable one-handed keyboard with gesture recognition,employing machine learning algorithms and hydrogel-based mechanical sensors to boost productivity.PCG(PAM/CMC/rGO)hydrogels are composed of polyacrylamide(PAM),sodium carboxymethyl cellulose(CMC),and reduced graphene oxide(rGO),which function as a strain,pressure sensor,and electrode material.The PAM chains offer the gel’s elasticity by covalent cross-linking,while the biocompatible CMC improves the dispersion of rGO and promotes electromechanical properties.Integrating rGO sheets into the polymer matrix facilitates cross-linking and generates supple-mentary conductive pathways,thereby augmenting the gel system’s elasticity,sensitivity,and durability.Our hydrogel sensors include high sensitivity(gage factor(GF)=8.18,395.6%-551.96%)and superior pressure sensing capabilities(Sensitivity(S)=0.3116 kPa^(-1),0-9.82 kPa).Furthermore,we developed a wearable keyboard with up to 98.13%accuracy using convolutional neural networks and a custom data acquisition system.This study establishes the groundwork for creating multifunctional gel sensors for intelligent machines,wearable devices,and brain-computer interfaces.
基金supported by the National Natural Science Foundation of China(No.52303112)the Henan Province Science and Technology Research and Development Program Joint Fund Advantageous Discipline Cultivation Project(No.232301420033)+1 种基金the China Postdoctoral Science Foundation(Nos.2022TQ0281 and 2023M733213)the Key R&D and Promotion Special(Scientific Problem Tackling)Project of Henan Province(No.242102231014).
文摘Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as they provide interaction platforms between users and virtual environments.However,traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional(3D)gestures,which results in limited freedom of VR interactions.Here,we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor(ETS),which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane(PLA/TPU)electret nanofiber film.The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability.Integrated with a deep learning-based multilayer perceptron neural network,the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%.An intelligent noncontact VR interactive system based on the ETS is further developed,which is used to manipulate game characters for performing different actions by 3D gestures.Compared with traditional VR HMIs,the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom.This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs,and could make the interaction between human and virtual environments become more efficient and fascinating.
基金National Natural Science Foundation of China,No.42361040。
文摘Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.
基金supported by the National Key Research and Development Program of China[2023YFC2307305]Shenzhen Medical Research Fund[B2303003]+3 种基金the Zhujiang Innovation and Entrepreneurship Talents Program[2021ZT09Y544]the Shenzhen Science and Technology Program[ZDSYS20230626091203007]Shanghai CDC(Shanghai three-year(2023–2025)action plan to strengthen the public health system[GWVI–11.1–05])Top Young Talents in Shanghai。
文摘Objective To assess the independent and combined effects of air pollutants,meteorological factors,and greenspace exposure on new tuberculosis(TB)cases.Methods TB case data from Shanghai(2013–2018)were obtained from the Shanghai Center for Disease Control and Prevention.Environmental data on air pollutants,meteorological variables,and greenspace exposure were obtained from the National Tibetan Plateau Data Center.We employed a distributed-lag nonlinear model to assess the effects of these environmental factors on TB cases.Results Increased TB risk was linked to PM_(2.5),PM_(10),and rainfall,whereas NO_(2),SO_(2),and air pressure were associated with a reduced risk.Specifically,the strongest cumulative effects occurred at various lags:PM_(2.5)(RR=1.166,95%CI:1.026–1.325)at 0–19 weeks;PM_(10)(RR=1.167,95%CI:1.028–1.324)at 0–18 weeks;NO_(2)(RR=0.968,95%CI:0.938–0.999)at 0–1 weeks;SO_(2)(RR=0.945,95%CI:0.894–0.999)at 0–2 weeks;air pressure(RR=0.604,95%CI:0.447–0.816)at 0–8 weeks;and rainfall(RR=1.404,95%CI:1.076–1.833)at 0–22 weeks.Green space exposure did not significantly impact TB cases.Additionally,low temperatures amplified the effect of PM_(2.5)on TB.Conclusion Exposure to PM_(2.5),PM_(10),and rainfall increased the risk of TB,highlighting the need to address air pollutants for the prevention of TB in Shanghai.
文摘Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.
基金supported by National Natural Science Foundation of China(no.62376240).
文摘Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.
文摘With the development of globalization,intercultural communicative competence has become one of the core qualities of modern college students.As an important platform to cultivate students’language skills and cultural literacy,the innovation of college English teaching mode is essential.Based on this,this paper mainly discusses methods to effectively cultivate students’intercultural communicative competence in college English teaching from the perspective of multimodal interactive teaching mode,hoping to provide references for improving the quality of college English teaching and students’comprehensive quality.
文摘In an aging society,the interface design of smart home products is crucial to the quality of life of the elderly.This paper combines Quality Function Deployment(QFD)and Human-Computer Interaction(HCI)theories,taking smart washing machines as an example,to explore new paths for the interface design of smart home products for the elderly.An interdisciplinary approach is adopted to construct a design process centered on elderly users,introduce the Kano model to classify requirements,realize the mapping and sorting of requirements to design parameters,and adopt the PUGH model for comprehensive evaluation.This study provides practical and theoretical support for the interface design of smart home products for the elderly.
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by the National Key R&D Program of China(No.2019YFA0708304)the CNPC Innovation Fund(No.2022DQ02-0609)the Scientific research and technology development Project of CNPC(No.2022DJ4507).
文摘Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,including limited anti-interference capabilities and the insufficient generalization of decision-making experience.To address the intricate problem of directional well trajectory control,an intelligent algorithm design framework grounded in the high-level interaction mechanism between geology and engineering is put forward.This framework aims to facilitate the rapid batch migration and update of drilling strategies.The proposed directional well trajectory control method comprehensively considers the multi-source heterogeneous attributes of drilling experience data,leverages the generative simulation of the geological drilling environment,and promptly constructs a directional well trajectory control model with self-adaptive capabilities to environmental variations.This construction is carried out based on three hierarchical levels:“offline pre-drilling learning,online during-drilling interaction,and post-drilling model transfer”.Simulation results indicate that the guidance model derived from this method demonstrates remarkable generalization performance and accuracy.It can significantly boost the adaptability of the control algorithm to diverse environments and enhance the penetration rate of the target reservoir during drilling operations.
文摘Objective:To analyze the nursing effect of implementing continuous nursing combined with knowledge,attitude,and practice(KAP)intervention under the guidance of interactive goal-setting theory for patients with gestational diabetes mellitus(GDM).Methods:A total of 68 patients with GDM who were admitted to the hospital between December 2021 and December 2023 were selected and randomly divided into two groups using a random number table.The combined group(34 cases)received continuous nursing combined with KAP intervention under the premise of interactive goal-setting theory,while the conventional group(34 cases)received routine nursing care.Blood glucose control,health behavior scores,and other indicators were compared between the two groups.Results:After nursing,the blood glucose level in the combined group was lower than that in the conventional group,and the scores for health behavior and self-management ability were higher than those in the conventional group(P<0.05).Conclusion:Combined nursing can improve blood glucose control in patients with GDM,standardize their health behaviors,and cultivate their self-management skills.
基金the National Key R&D Project from Minister of Science and Technology(Grant No.2016YFA0202704)the Beijing Municipal Natural Science Foundation(Grant No.2212052)+1 种基金the Shanghai Sailing Program(Grant No.19S28101)the Fundamental Research Funds for the Central Universities(Grant No.19D128102).
文摘Combination flexible and stretchable textiles with self-powered sensors bring a novel insight into wearable functional electronics and cyber security in the era of Internet of Things.This work presents a highly flexible and self-powered fully fabric-based triboelectric nanogenerator(F-TENG)with sandwiched structure for biomechanical energy harvesting and real-time biometric authentication.The prepared F-TENG can power a digital watch by low-frequency motion and respond to the pressure change by the fall of leaves.A self-powered wearable keyboard(SPWK)is also fabricated by integrating large-area F-TENG sensor arrays,which not only can trace and record electrophysiological signals,but also can identify individuals’typing characteristics by means of the Haar wavelet.Based on these merits,the SPWK has promising applications in the realm of wearable electronics,self-powered sensors,cyber security,and artificial intelligences.
