To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail stee...To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica...It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting s...Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the s...Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the structural coloration of Cyanocitta stelleri feathers,we developed a dual-mode sensor by utilizing black conductive polymer hydrogel(CPH)-enhanced structural color strategy.This sensor integrates a hydroxypropyl cellulose(HPC)-based structural color interface with a designed CPH sensing component.Highly visible light-absorbing CPH(absorption rate>88%)serves as the critical substrate for enhancing structural color performance.By absorbing incoherent scattered light and suppressing background interference,it significantly enhances the saturation of structural color,thereby achieving a high contrast index of 4.92.Unlike the faint and hardly visible structural colors on non-black substrates,the HPC on CPH displays vivid,highly perceptible colors and desirable mechanochromic behavior.Moreover,the CPH acts as a flexible sensing element,fortified by hydrogen and coordination bond networks,and exhibits exceptional electromechanical properties,including 867.1 kPa tensile strength,strain sensitivity(gauge factor of 4.24),and outstanding durability(over 4400 cycles).Compared to traditional single-mode sensors,the integrated sensor provides real-time visual and digital dual feedback,enhancing the accuracy and interactivity of rehabilitation assessments.This technology holds promise for advancing next-generation rehabilitation medicine.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show...The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.展开更多
As structural damage patterns and service environments become more complex,digital twin-based structural health monitoring,with its unique advantages,can compensate for the limitations of data-driven methods regarding...As structural damage patterns and service environments become more complex,digital twin-based structural health monitoring,with its unique advantages,can compensate for the limitations of data-driven methods regarding data dependency and model interpretability.However,it still faces challenges in modeling complexity,simulation accuracy,and discrepancies between real and virtual features.This study proposes a balanced fidelity digital twin for structural damage monitoring based on Lamb wave multilevel feature enhancement and adaptive space interaction.Firstly,multilevel refined features are extracted from few-shot guided wave signals obtained in physical and digital space,and the adversarial synthetic balancing algorithm is proposed for feature enhancement.Additionally,the learning phase of the damage monitoring model based on the feature-mapping convolutional network is driven by virtual samples of readily accessible balanced fidelity in digital space.To reduce the feature distributional difference between the two spaces,an interactive transfer approach is introduced to establish a shared feature digital twin space.Overall,this study provides a feasible technique to enhance the accessibility and generalizability of digital twins for real engineering structures.展开更多
With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods oft...With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods often struggle to address dynamic changes under complex operando.In recent years,flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures.Meanwhile,empowered by artificial intelligence(AI)for data analysis,the collected data enables efficient and accurate state assessment,offering robust support for accident prevention.Against this background,this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions,while analyzing the potential of AI in battery state analysis.Subsequently,it systematically reviews mainstream flexible sensing technologies(e.g.,film sensors,thermocouples,and optical fiber sensors),elucidating their mechanisms for revealing intricate internal battery processes during operation.Finally,the paper discusses AI’s role in enhancing monitoring efficiency and accuracy,and envisions future research directions and application prospects.This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and predic...The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and prediction are quite important.Currently,there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle,and the level of intelligence is relatively low.To address this problem,an error motion monitoring system based on digital twin(DT)technology was established for the aerostatic spindle.A spindle error motion prediction method based on a mechanism and data fusion model(MDFM)was proposed.Additionally,a highly available and interactive aerostatic spindle DT service platform was developed.Experimental results have verified the good performance of this platform.The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle,enabling three-dimensional visualization,monitoring,prediction,and simulation of spindle error motion,and shows good potential for engineering applications.展开更多
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
Despite effective antiretroviral therapy(ART),many individuals with human immunodeficiency virus(HIV)/acquired immune deficiency syndrome(AIDS)achieve viral suppression but fail to fully restore cluster of differentia...Despite effective antiretroviral therapy(ART),many individuals with human immunodeficiency virus(HIV)/acquired immune deficiency syndrome(AIDS)achieve viral suppression but fail to fully restore cluster of differentiation 4(CD4)^(+)T lymphocyte(CD4 cell)counts—a condition known as immunological non-response(INRs).INRs are associated with elevated health risks,including increased susceptibility to AIDS-related and non-AIDS-related complications.The pathogenesis of INRs remains incompletely understood,and no established therapeutic interventions exist,posing a major challenge in contemporary HIV/AIDS management.Emerging evidence indicates that INRs exhibit significant alterations in gut microbiota composition.Dysbiosis of the gut microbiota may contribute to persistent immune activation,cytokine imbalance,and cellular pyroptosis,all of which could impair immune reconstitution in people living with HIV/AIDS.Traditional Chinese medicine(TCM)has demonstrated potential immunomodulatory effects and is increasingly utilized in the management of INRs.Targeting the gut microbiota and elucidating the mechanisms by which TCM modulates this microbial ecosystem may offer new avenues for preventing and treating INRs.This review explores the interplay between gut microbiota and TCM,examines the association between gut dysbiosis and INRs,discusses the mechanistic pathways through which microbiota imbalance contributes to INRs development,and highlights how TCM interventions regulate gut microbiota to promote immune recovery.By focusing on the gut microbiota as a therapeutic interface,this article provides novel insights into TCM-based strategies for improving outcomes in INRs and supports the development of innovative treatment approaches.展开更多
BACKGROUND Continuous glucose monitoring(CGM)metrics,such as time in range(TIR)and glycemic risk index(GRI),have been linked to various diabetes-related complications,including diabetic foot(DF).AIM To investigate the...BACKGROUND Continuous glucose monitoring(CGM)metrics,such as time in range(TIR)and glycemic risk index(GRI),have been linked to various diabetes-related complications,including diabetic foot(DF).AIM To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus(T2DM).METHODS A total of 591 individuals with T2DM(297 with DF and 294 without DF)were enrolled.Relevant clinical data,complications,comorbidities,hematological parameters,and 72-hour CGM data were collected.Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.RESULTS Individuals with DF exhibited higher mean blood glucose(MBG)levels and increased proportions of time above range(TAR),TAR level 1,and TAR level 2,but lower TIR(all P<0.001).Patients with DF had significantly lower rates of achieving target ranges for TIR,TAR,and TAR level 2 than those without DF(all P<0.05).Logistic regression analysis revealed that GRI,MBG,and TAR level 1 were positively associated with DF risk,while TIR was inversely correlated(all P<0.05).Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels(P<0.05).Additionally,achieving TAR was influenced by fasting plasma glucose,body mass index,diabetes duration,and antidiabetic medication use.CONCLUSION CGM metrics,particularly TIR and GRI,are significantly associated with the risk of DF in T2DM,emphasizing the importance of improved glucose control.展开更多
A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to t...A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.展开更多
Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution,which carries inherent risks.Consequently,a variety of engineering practices are employed to monitor bridge co...Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution,which carries inherent risks.Consequently,a variety of engineering practices are employed to monitor bridge construction.This paper presents a case study of a large-span prestressed concrete(PC)variable-section continuous girder bridge in China,proposing a feedback system for construction monitoring and establishing a finite element(FE)analysis model for the entire bridge.The alignment of the completed bridge adheres to the initial design expectations,with maximum displacement and pre-arch differences from the ideal state measuring 6.39 and 17.7 mm,respectively,which were less than the 20 mm limit required by the specification.Additionally,the stress monitoring showed that the maximum compressive stress was 10.44 MPa,which was 7.5%different from the finite element results,and better predicted the most unfavorable possible location.These results demonstrate that a scientifically rigorous construction monitoring and feedback system can ensure the safety of bridge construction and meet the expected construction standards.The findings presented in this paper provide valuable insights for bridge construction monitoring practices.展开更多
基金supported by the Key Technologies Research and Development Program under Grant 2021YFB1600300.
文摘To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
基金the support from National Natural Science Foundation of China(No.52275153)the Frontier Technologies R&D Program of Jiangsu,China(No.BF2024068)+1 种基金The Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics),China(Nos.MCAS-I-0425K01,MCAS-I-0423G01)。
文摘It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金supported by the National Key Research and Development Program of China under grant 2022YFF0902701the National Natural Science Foundation of China under grant U21A20468,61972043,61921003+1 种基金Zhejiang Lab under grant 2021PD0AB 02the Fundamental Research Funds for the Central Universities under grant 2020XD-A07-1.
文摘Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
基金supported by the Science and Technology Development Fund,Macao SAR(0065/2023/AFJ,0116/2022/A3)the National Natural Science Foundation of China(52402166)+4 种基金the Natural Science Foundation of Guangdong Province(2025A1515011120)the Australian Research Council(DE220100154)the financial support from the Science and Technology Development Fund(FDCT),Macao SAR(No.0149/2022/A),and(No.0046/2024/AFJ)Guangdong Science and Technology Department(2023QN10C305)for this workthe financial support from the National Natural Science Foundation of China(Grant No.22305185)。
文摘Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the structural coloration of Cyanocitta stelleri feathers,we developed a dual-mode sensor by utilizing black conductive polymer hydrogel(CPH)-enhanced structural color strategy.This sensor integrates a hydroxypropyl cellulose(HPC)-based structural color interface with a designed CPH sensing component.Highly visible light-absorbing CPH(absorption rate>88%)serves as the critical substrate for enhancing structural color performance.By absorbing incoherent scattered light and suppressing background interference,it significantly enhances the saturation of structural color,thereby achieving a high contrast index of 4.92.Unlike the faint and hardly visible structural colors on non-black substrates,the HPC on CPH displays vivid,highly perceptible colors and desirable mechanochromic behavior.Moreover,the CPH acts as a flexible sensing element,fortified by hydrogen and coordination bond networks,and exhibits exceptional electromechanical properties,including 867.1 kPa tensile strength,strain sensitivity(gauge factor of 4.24),and outstanding durability(over 4400 cycles).Compared to traditional single-mode sensors,the integrated sensor provides real-time visual and digital dual feedback,enhancing the accuracy and interactivity of rehabilitation assessments.This technology holds promise for advancing next-generation rehabilitation medicine.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
基金supported by the National Natural Science Foundation of China(NSFC 52175281,52475315)Youth Innovation Promotion Association of CAS(2021382)。
文摘The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.No.U2141245,11972314,11472308)。
文摘As structural damage patterns and service environments become more complex,digital twin-based structural health monitoring,with its unique advantages,can compensate for the limitations of data-driven methods regarding data dependency and model interpretability.However,it still faces challenges in modeling complexity,simulation accuracy,and discrepancies between real and virtual features.This study proposes a balanced fidelity digital twin for structural damage monitoring based on Lamb wave multilevel feature enhancement and adaptive space interaction.Firstly,multilevel refined features are extracted from few-shot guided wave signals obtained in physical and digital space,and the adversarial synthetic balancing algorithm is proposed for feature enhancement.Additionally,the learning phase of the damage monitoring model based on the feature-mapping convolutional network is driven by virtual samples of readily accessible balanced fidelity in digital space.To reduce the feature distributional difference between the two spaces,an interactive transfer approach is introduced to establish a shared feature digital twin space.Overall,this study provides a feasible technique to enhance the accessibility and generalizability of digital twins for real engineering structures.
基金supported by the grant of State Key Laboratory of Space Environment Interaction with Matters,the Science and Technology on Vacuum Technology and Physics Laboratory Fund(HTKJ2023KL510008)Key Program of the National Natural Science Foundation of China(No.62433017)+6 种基金the National Natural Science Foundation of China(No.62274140)the Fundamental Research Funds for the Central Universities(20720230030)the Xiaomi Young Talents Program/Xiaomi Foundation,Shenzhen Science and Technology Program(JCYJ20230807091401003)the Young Elite Scientist Sponsorship Program by Cast(No.YESS20230523)the State Key Laboratory of Space Environment Interaction with Matters(WDZC-HGD-2022-08)the Gansu Provincial Science and Technology Major Project(2244ZZDD1133GGAA000077)the China Aerospace Science and Technology Group Corporation Young Top Talents.
文摘With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods often struggle to address dynamic changes under complex operando.In recent years,flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures.Meanwhile,empowered by artificial intelligence(AI)for data analysis,the collected data enables efficient and accurate state assessment,offering robust support for accident prevention.Against this background,this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions,while analyzing the potential of AI in battery state analysis.Subsequently,it systematically reviews mainstream flexible sensing technologies(e.g.,film sensors,thermocouples,and optical fiber sensors),elucidating their mechanisms for revealing intricate internal battery processes during operation.Finally,the paper discusses AI’s role in enhancing monitoring efficiency and accuracy,and envisions future research directions and application prospects.This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
基金supported by the National Natural Science Foundation of China(Grant No.52475494),the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY22E050003),the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.RF-A2020005).
文摘The aerostatic spindle is a key component of ultra-precision machine tools,and its error motion is crucial to machining accuracy and reliability.Spindle error motion is unavoidable,and its online monitoring and prediction are quite important.Currently,there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle,and the level of intelligence is relatively low.To address this problem,an error motion monitoring system based on digital twin(DT)technology was established for the aerostatic spindle.A spindle error motion prediction method based on a mechanism and data fusion model(MDFM)was proposed.Additionally,a highly available and interactive aerostatic spindle DT service platform was developed.Experimental results have verified the good performance of this platform.The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle,enabling three-dimensional visualization,monitoring,prediction,and simulation of spindle error motion,and shows good potential for engineering applications.
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
基金supported by the National Natural Science Foundation of China(No.82274474)。
文摘Despite effective antiretroviral therapy(ART),many individuals with human immunodeficiency virus(HIV)/acquired immune deficiency syndrome(AIDS)achieve viral suppression but fail to fully restore cluster of differentiation 4(CD4)^(+)T lymphocyte(CD4 cell)counts—a condition known as immunological non-response(INRs).INRs are associated with elevated health risks,including increased susceptibility to AIDS-related and non-AIDS-related complications.The pathogenesis of INRs remains incompletely understood,and no established therapeutic interventions exist,posing a major challenge in contemporary HIV/AIDS management.Emerging evidence indicates that INRs exhibit significant alterations in gut microbiota composition.Dysbiosis of the gut microbiota may contribute to persistent immune activation,cytokine imbalance,and cellular pyroptosis,all of which could impair immune reconstitution in people living with HIV/AIDS.Traditional Chinese medicine(TCM)has demonstrated potential immunomodulatory effects and is increasingly utilized in the management of INRs.Targeting the gut microbiota and elucidating the mechanisms by which TCM modulates this microbial ecosystem may offer new avenues for preventing and treating INRs.This review explores the interplay between gut microbiota and TCM,examines the association between gut dysbiosis and INRs,discusses the mechanistic pathways through which microbiota imbalance contributes to INRs development,and highlights how TCM interventions regulate gut microbiota to promote immune recovery.By focusing on the gut microbiota as a therapeutic interface,this article provides novel insights into TCM-based strategies for improving outcomes in INRs and supports the development of innovative treatment approaches.
基金Supported by Yunnan Province Academician(Expert)Workstation Project,No.202305AF150097the Basic Research Program of Yunnan Province(Kunming Medical University Joint Special Project),No.202101AY070001-276+3 种基金the National Natural Science Foundation of China,No.82160159the Key Project Program of Yunnan Province(Kunming Medical University Joint Special Project),No.202301AY070001-013the Major Science and Technology Project of Yunnan Province,No.202202AA100004the Double First-class University Construction Project of Yunnan University,No.CY22624106.
文摘BACKGROUND Continuous glucose monitoring(CGM)metrics,such as time in range(TIR)and glycemic risk index(GRI),have been linked to various diabetes-related complications,including diabetic foot(DF).AIM To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus(T2DM).METHODS A total of 591 individuals with T2DM(297 with DF and 294 without DF)were enrolled.Relevant clinical data,complications,comorbidities,hematological parameters,and 72-hour CGM data were collected.Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.RESULTS Individuals with DF exhibited higher mean blood glucose(MBG)levels and increased proportions of time above range(TAR),TAR level 1,and TAR level 2,but lower TIR(all P<0.001).Patients with DF had significantly lower rates of achieving target ranges for TIR,TAR,and TAR level 2 than those without DF(all P<0.05).Logistic regression analysis revealed that GRI,MBG,and TAR level 1 were positively associated with DF risk,while TIR was inversely correlated(all P<0.05).Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels(P<0.05).Additionally,achieving TAR was influenced by fasting plasma glucose,body mass index,diabetes duration,and antidiabetic medication use.CONCLUSION CGM metrics,particularly TIR and GRI,are significantly associated with the risk of DF in T2DM,emphasizing the importance of improved glucose control.
基金The Scientific Research Projects Coordination Unit of Akdeniz University(Türkiye)under contract No.FBA-2022-5542.
文摘A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.
基金The Guangdong Basic and Applied Basic Research Foundation(Grant#2023A1515010535).
文摘Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution,which carries inherent risks.Consequently,a variety of engineering practices are employed to monitor bridge construction.This paper presents a case study of a large-span prestressed concrete(PC)variable-section continuous girder bridge in China,proposing a feedback system for construction monitoring and establishing a finite element(FE)analysis model for the entire bridge.The alignment of the completed bridge adheres to the initial design expectations,with maximum displacement and pre-arch differences from the ideal state measuring 6.39 and 17.7 mm,respectively,which were less than the 20 mm limit required by the specification.Additionally,the stress monitoring showed that the maximum compressive stress was 10.44 MPa,which was 7.5%different from the finite element results,and better predicted the most unfavorable possible location.These results demonstrate that a scientifically rigorous construction monitoring and feedback system can ensure the safety of bridge construction and meet the expected construction standards.The findings presented in this paper provide valuable insights for bridge construction monitoring practices.
