Taking Yutou Dong Minority Village in Tongdao County for example, this study carried out questionnaire survey and interview survey from the perspective of environment element perceptibility and culture psychology reco...Taking Yutou Dong Minority Village in Tongdao County for example, this study carried out questionnaire survey and interview survey from the perspective of environment element perceptibility and culture psychology recognizability, on the basis of identifying 9 landscape genes, namely drum tower, wind-rain bridge, the village gate, Sa altar, overall layout, totem signs, and environmental factors. The survey results showed that 1 Sa altar has the highest perceptibility, and drum tower has the lowest perceptibility; 2 Sa altar and drum tower have the highest recognizability, overall layout, totem signs and environment have the lowest; 3 Location has the highest perceptibility and size has the poorest; 4 Sentiment has the highest perceptibility, function the lowest; 5 Men have higher perception and sense of recognition than women; 6 Education background has greater impact on perception, but no obvious impact on sense of recognition; 7 Length of residence in the village has positive effect on perception and sense of recognition; 8 Distance from the village has positive effect on perception and sense of recognition.展开更多
Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize...Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize electrical stimulation to directly or indirectly target specific brain regions,modulating neural activity and influencing broader brain networks,thereby regulating cognitive function.Regulating cognitive function involves an understanding of aspects such as perception,learning and memory,attention,spatial cognition,and physical function.To enhance the application of cognitive regulation in the general population,this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions.This review covers various neuromodulation techniques for cognitive intervention,including deep brain stimulation,vagus nerve stimulation,and invasive methods using microelectrode arrays.The non-invasive techniques discussed include transcranial magnetic stimulation,transcranial direct current stimulation,transcranial alternating current stimulation,transcutaneous electrical acupoint stimulation,and time interference stimulation for activating deep targets.Invasive stimulation methods,which are ideal for studying the pathogenesis of neurological diseases,tend to cause greater trauma and have been less researched in the context of cognitive function regulation.Non-invasive methods,particularly newer transcranial stimulation techniques,are gentler and more appropriate for regulating cognitive functions in the general population.These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets.This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology.It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation.Additionally,researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.展开更多
Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations...Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.展开更多
Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged desi...Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.展开更多
BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship ...BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.展开更多
As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety o...As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.展开更多
The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumpt...The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumptions,primarily due to constraints such as computational and sensor deployment costs.We propose an explicit posture estimation method through a neural network,and implement it using an embedded camera for vision-based proprioception.We design a continuous location encoding neural network(LENN)by encoding continuous locational information.The LENN can capture deformation from changes in internal texture observed by an integrated camera,and output pose information—both position and orientation—for any point along the robot backbone,rather than only discrete points.Compared with interpolation-based estimation using a reduced model,our method reduces single-point estimation error by 33.6%.Furthermore,a systematic evaluation of hardware configurations demonstrates that our prototype achieves sub-millimetre accuracy in shape estimation(0.383 mm)while maintaining real-time inference speeds below 12 ms per frame.By combining a learning-based approach with a simple mechanical design,our method leverages internal visual information to estimate the whole-body pose,providing an effective solution for accurate shape estimation in continuum robots.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental ...Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental trajectories of discrimination perception,vocational identity,and psychological resilience in this population.It further examined the longitudinal mediating role of vocational identity in the relationship between discrimination perception and psychological resilience.Methods:A total of 526 students from five vocational high schools in Guangdong,China,were assessed via convenience sampling at two time points:baseline(T1,September 2023)and six-month follow-up(T2,March 2024).Measures of discrimination perception,psychological resilience,and vocational identity were administered.Data were analyzed using a cross-lagged panel model to test for bidirectional relationships.Results:Over the six-month period,students showed significant decreases in discrimination perception and vocational identity,but a significant increase in psychological resilience.The cross-lagged model revealed significant bidirectional relationships:discrimination perception and psychological resilience negatively predicted each other over time(β=−0.124,p<0.01;β=−0.200,p<0.001),while psychological resilience and vocational identity positively predicted each other(β=0.084,p<0.05;β=0.076,p<0.05).The mediation analysis revealed a dual-pathway mechanism.T1 discrimination perception exerted both a significant direct negative effect on T2 psychological resilience(β=−0.332,p<0.001)and a significant indirect positive effect via T1 vocational identity(indirect effect=0.020,95%CI[0.001,0.046]).This confirms a partial mediating role,indicating that vocational identity functions as a compensatory mechanism,transforming the experience of discrimination perception into a potential source of psychological resilience.Conclusions:For vocational high school students,perception of discrimination directly undermines psychological resilience,but also indirectly fosters it through the positive development of vocational identity.These findings highlight vocational identity as a pivotal mechanism in the complex relationship between social adversity and mental resilience.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.展开更多
Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in th...Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discom...This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discomfort,but also carry a significant emotional burden,often exacerbated by social stigma.Psycho-logical factors,including stress,anxiety,and depression,activate neuroendocrine pathways,such as the hypothalamic–pituitary–adrenal axis,disrupting the gut microbiota and leading to dysbiosis.This disruption can delay wound healing,prolong hospital stay,and intensify pain.Drawing on the findings of Hou et al,our article highlights the critical role of illness perception and negative emotions in shaping recovery outcomes.It advocates for a holistic approach that integrates psychological support and gut microbiota modulation,to enhance healing and improve overall patient outcomes.展开更多
Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers...Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.展开更多
Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their us...Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.展开更多
This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 emp...This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.展开更多
The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,...The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.展开更多
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng...In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.展开更多
Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors c...Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.展开更多
基金Sponsored by The National Natural Science Fund(41401148)Hunan Provincial Natural Science Innovation Group Fund(12JJ7003)Humanities and Social Science Fund of the Ministry of Education(11YJCZH210)
文摘Taking Yutou Dong Minority Village in Tongdao County for example, this study carried out questionnaire survey and interview survey from the perspective of environment element perceptibility and culture psychology recognizability, on the basis of identifying 9 landscape genes, namely drum tower, wind-rain bridge, the village gate, Sa altar, overall layout, totem signs, and environmental factors. The survey results showed that 1 Sa altar has the highest perceptibility, and drum tower has the lowest perceptibility; 2 Sa altar and drum tower have the highest recognizability, overall layout, totem signs and environment have the lowest; 3 Location has the highest perceptibility and size has the poorest; 4 Sentiment has the highest perceptibility, function the lowest; 5 Men have higher perception and sense of recognition than women; 6 Education background has greater impact on perception, but no obvious impact on sense of recognition; 7 Length of residence in the village has positive effect on perception and sense of recognition; 8 Distance from the village has positive effect on perception and sense of recognition.
基金supported by STI 2030-Major Projects,No.2021ZD0201603(to JL)the Joint Foundation Program of the Chinese Academy of Sciences,No.8091A170201(to JL)+1 种基金the National Natural Science Foundation of China,Nos.T2293730(to XC),T2293731(to XC),T2293734(to XC),62471291(to YW),62121003(to XC),61960206012(to XC),62333020(to XC),and 62171434(to XC)the National Key Research and Development Program of China,Nos.2022YFC2402501(to XC),2022YFB3205602(to XC).
文摘Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize electrical stimulation to directly or indirectly target specific brain regions,modulating neural activity and influencing broader brain networks,thereby regulating cognitive function.Regulating cognitive function involves an understanding of aspects such as perception,learning and memory,attention,spatial cognition,and physical function.To enhance the application of cognitive regulation in the general population,this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions.This review covers various neuromodulation techniques for cognitive intervention,including deep brain stimulation,vagus nerve stimulation,and invasive methods using microelectrode arrays.The non-invasive techniques discussed include transcranial magnetic stimulation,transcranial direct current stimulation,transcranial alternating current stimulation,transcutaneous electrical acupoint stimulation,and time interference stimulation for activating deep targets.Invasive stimulation methods,which are ideal for studying the pathogenesis of neurological diseases,tend to cause greater trauma and have been less researched in the context of cognitive function regulation.Non-invasive methods,particularly newer transcranial stimulation techniques,are gentler and more appropriate for regulating cognitive functions in the general population.These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets.This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology.It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation.Additionally,researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.
基金supported by the National Natural Science Foundation of China(General Program)under Grant 52571385National Key R&D Program of China(Grant No.2024YFC2815000 and No.2024YFB3816000)+12 种基金Open Fund of State Key Laboratory of Deep-sea Manned Vehicles(Grant No.2025SKLDMV07)Shenzhen Science and Technology Program(WDZC20231128114452001,JCYJ20240813112107010 and JCYJ20240813111910014)the Tsinghua SIGS Scientific Research Startup Fund(QD2022021C)the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM 01 Z006)the Ocean Decade International Cooperation Center(ODCC)(GHZZ3702840002024020000026)Shenzhen Key Laboratory of Advanced Technology for Marine Ecology(ZDSYS20230626091459009)Shenzhen Science and Technology Program(No.KJZD20240903100905008)the National Natural Science Foundation of China(No.22305141)Pearl River Talent Program(No.2023QN10C114)General Program of Guangdong Province(No.2025A1515011700)the Guangdong Innovative and Entrepreneurial Research Team Program(2023ZT10C040)Scientific Research Foundation from Shenzhen Finance Bureau(No.GJHZ20240218113600002)Tsinghua University(JC2023001).
文摘Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.
基金supported by the National Natural Science Foundation of China(No.52375031)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers Research and Development Plan of Zhejiang Province(No.2023C01045)。
文摘Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.
文摘BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.
文摘As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.
基金supported by the National Natural Science Foundation of China(Grant Nos.52188102,52505008)the National Key Research and Development Program of China(Grant No.2024YFB4707902)。
文摘The capability of whole-body proprioception,e.g.,pose estimation,is important for the control and interaction of continuum robots.However,existing pose estimation methods are often simplified through geometric assumptions,primarily due to constraints such as computational and sensor deployment costs.We propose an explicit posture estimation method through a neural network,and implement it using an embedded camera for vision-based proprioception.We design a continuous location encoding neural network(LENN)by encoding continuous locational information.The LENN can capture deformation from changes in internal texture observed by an integrated camera,and output pose information—both position and orientation—for any point along the robot backbone,rather than only discrete points.Compared with interpolation-based estimation using a reduced model,our method reduces single-point estimation error by 33.6%.Furthermore,a systematic evaluation of hardware configurations demonstrates that our prototype achieves sub-millimetre accuracy in shape estimation(0.383 mm)while maintaining real-time inference speeds below 12 ms per frame.By combining a learning-based approach with a simple mechanical design,our method leverages internal visual information to estimate the whole-body pose,providing an effective solution for accurate shape estimation in continuum robots.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
基金supported by the Guangdong Provincial Philosophy and Social Science“14th Five-Year Plan”Discipline Co-Construction Project(Grant No.GD22XJY14)the 2022 Guangdong Provincial Higher Education Teaching Reform Project(Grant No.Yue Jiao Gao[2023]4)Guangdong Polytechnic Normal University’s Project for Enhancing the Research Capacity of Doctoral Application Institution(Grant No.22GPNUZDJS48).
文摘Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental trajectories of discrimination perception,vocational identity,and psychological resilience in this population.It further examined the longitudinal mediating role of vocational identity in the relationship between discrimination perception and psychological resilience.Methods:A total of 526 students from five vocational high schools in Guangdong,China,were assessed via convenience sampling at two time points:baseline(T1,September 2023)and six-month follow-up(T2,March 2024).Measures of discrimination perception,psychological resilience,and vocational identity were administered.Data were analyzed using a cross-lagged panel model to test for bidirectional relationships.Results:Over the six-month period,students showed significant decreases in discrimination perception and vocational identity,but a significant increase in psychological resilience.The cross-lagged model revealed significant bidirectional relationships:discrimination perception and psychological resilience negatively predicted each other over time(β=−0.124,p<0.01;β=−0.200,p<0.001),while psychological resilience and vocational identity positively predicted each other(β=0.084,p<0.05;β=0.076,p<0.05).The mediation analysis revealed a dual-pathway mechanism.T1 discrimination perception exerted both a significant direct negative effect on T2 psychological resilience(β=−0.332,p<0.001)and a significant indirect positive effect via T1 vocational identity(indirect effect=0.020,95%CI[0.001,0.046]).This confirms a partial mediating role,indicating that vocational identity functions as a compensatory mechanism,transforming the experience of discrimination perception into a potential source of psychological resilience.Conclusions:For vocational high school students,perception of discrimination directly undermines psychological resilience,but also indirectly fosters it through the positive development of vocational identity.These findings highlight vocational identity as a pivotal mechanism in the complex relationship between social adversity and mental resilience.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
基金Under the auspices of the Hong Kong Research Grants Council(No.14605920,14606922,14603724,C4023-20GF,8601219,8601242,3110151)a Grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes of the Chinese University of Hong Kong(CUHK,No.3133235)the Vice-Chancellor’s One-off Discretionary Fund of CUHK(No.4930787)。
文摘Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discomfort,but also carry a significant emotional burden,often exacerbated by social stigma.Psycho-logical factors,including stress,anxiety,and depression,activate neuroendocrine pathways,such as the hypothalamic–pituitary–adrenal axis,disrupting the gut microbiota and leading to dysbiosis.This disruption can delay wound healing,prolong hospital stay,and intensify pain.Drawing on the findings of Hou et al,our article highlights the critical role of illness perception and negative emotions in shaping recovery outcomes.It advocates for a holistic approach that integrates psychological support and gut microbiota modulation,to enhance healing and improve overall patient outcomes.
基金supported by the Jiangsu Provincial Department of Science and Technology Social Development Project(No.BE2020787)。
文摘Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.
基金financially supported by the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCOthe National Research Foundation (NRF) grants (RS-2024-00462912, RS-2024-00416272, RS-2024-00337012, RS-2024-00408446) funded by the Ministry of Science and ICT (MSIT) of the Korean government+2 种基金the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean governmentthe Soseon Science fellowship funded by Community Chest of Koreathe NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government。
文摘Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.
文摘This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.
基金supported by the National Natural Science Foundation of China(U20A20111)the National key R&D Program(2022YFC3080100)。
文摘The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.
基金Funded by the Natural Science Foundation of China(No.52109168)。
文摘In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.
基金financially supported by the Sichuan Science and Technology Program(2022YFS0025 and 2024YFFK0133)supported by the“Fundamental Research Funds for the Central Universities of China.”。
文摘Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.
