The performance of multiple processor based on Network on Chip (NoC) is limited to the communication efficiency of network. It is difficult to be optimized of routing and arbitration algorithm and be assessed of perfo...The performance of multiple processor based on Network on Chip (NoC) is limited to the communication efficiency of network. It is difficult to be optimized of routing and arbitration algorithm and be assessed of performance in the beginning of design because of its complex test cases. This paper constructs a scalable and monitored system level model with SystemC for NoC with Packet Connected Circuit (PCC) protocol. The overall performance and transfer details can be evaluated particularly by running the model, and the statistical basis can also be provided to the optimization of designing NoC.展开更多
【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided tech...【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.展开更多
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
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew e...0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew et al.,2017).Traditional monitoring methods primarily target the acceleration stage preceding disasters(such as displacement monitoring for landslides and debris flows),which is effective for early warning of plastic collapse disasters but often inadequate for brittle failure modes(Walter et al.,2019;Chao et al.,2018;Crosta et al.,2017).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
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
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
To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge gen...To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.展开更多
Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exp...Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.展开更多
Monitoring cardiac function is a fundamental component of the diagnosis and management of critically ill patients.While pulmonary artery catheterization has long served as the standard for hemodynamic assessment,its i...Monitoring cardiac function is a fundamental component of the diagnosis and management of critically ill patients.While pulmonary artery catheterization has long served as the standard for hemodynamic assessment,its invasive nature and associated risks have shifted clinical practice toward non-invasive modalities.^([1]) Among these methods,point-of-care ultrasound(POCUS) has gained widespread acceptance,offering real-time bedside evaluation of cardiac function.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
Health monitoring is becoming increasingly critical for disease prevention,early diagnosis,and highquality living.Polymeric materials,with their mechanical flexibility,biocompatibility,and tunable biochemical properti...Health monitoring is becoming increasingly critical for disease prevention,early diagnosis,and highquality living.Polymeric materials,with their mechanical flexibility,biocompatibility,and tunable biochemical properties,offer unique advantages for creating next-generation personalized devices.In recent years,flexible polymer-based platforms have shown remarkable potential to capture diverse physiological signals in both daily and clinical contexts,including electrophysiological,biochemical,mechanical,and thermal indicators.In this review,we introduce a safety-leveloriented framework to evaluate material and device strategies for health monitoring,spanning the continuum from noninvasive wearables to deeply embedded implants.Physiological signals are systematically classified by use case,and application-specific requirements such as stability,comfort,and long-term compatibility are highlighted as critical factors guiding the selection of polymers,interfacial designs,and device architectures.Special emphasis is placed on mapping material types—including hydrogels,elastomers,and conductive composites—to their most suitable applications.Finally,we propose design principles for developing safe,functional,and adaptive polymer-based systems,aiming at reliable integration with the human body and enabling personalized,preventive healthcare.展开更多
Satellite hydrology has reached a new era where a variety of earth observing systems,in conjunction with the development of data integrations,can provide a consistent monitoring of groundwater change,wetland dynamics,...Satellite hydrology has reached a new era where a variety of earth observing systems,in conjunction with the development of data integrations,can provide a consistent monitoring of groundwater change,wetland dynamics,and drought evolution in regions where observations on the ground remain sparse or patchy.This synthesis review highlights the physical observables that are most useful to terrestrial hydrology:variable time-dependent gravity,passive and active microwave signals,optical reflectance,thermal infrared measurements,and altimetry,and this has been translated into hydrologic states and water fluxes:terrestrial water storage,ground water storage anomalies,inundation extent and hydroperiod,evapotranspiration,and multi-timescale drought indicators.We call attention to the role of satellite gravimetry in the transformation of basin-scale groundwater evaluation,synthetic aperture radar(SAR),and optical time series in transforming wetland science,however,to the characterization of hydroperiod and connectivity.In the case of drought,we recommend the use of both fast-responsive(soil moisture,evapotranspiration,thermal stress,and vegetation condition)in conjunction with slowly integrating storage anomalies to detect onset,intensification,and recovery,as well as rely on to diagnose the spread of deficits through the hydrologic system.In all these themes,we recognize the central action of enabling approaches that include multi-sensor fusion,data assimilation,and hybrid machine learning schemes in reducing ambiguity,scale gaps,and producing products of decision interest,and also highlighting ongoing issues in the quantification of uncertainty,consistency across long periods of time,and the separation of climate influence on human management of water.Research and operational priorities come as our final deduction to develop satellite hydrology to more trustworthy,explanation-seeking,and operationalizing monitoring designs of water security and ecosystem resilience.展开更多
Water quality is fundamental to water security,ecosystem integrity,and public health.This study assessed the impact of wastewater effluent on the Trichardspruit River,which receives effluent from two wastewater treatm...Water quality is fundamental to water security,ecosystem integrity,and public health.This study assessed the impact of wastewater effluent on the Trichardspruit River,which receives effluent from two wastewater treatment works(WWTWs).Over a 12-month period,water samples were collected across six study sites(S1–S6)to evaluate physicochemical and microbiological parameters relative to national Resource Quality Objectives(RQOs)and Water Use Licence(WUL)limits.One-way ANOVA showed significant(p<0.05)spatial differences for all parameters,and Tukey’s HSD identified the strongest pairwise contrasts at and below the discharge of WWTW-2(S5).Orthophosphate peaked at 7.26 mg/L at S5,and remained elevated at 3.6 mg/L at S6.Chemical oxygen demand reached 92 mg/L at S5,and likewise,EC and major ions(Cl^(−)and SO_(4)^(2−))were higher at S5&S6 than upstream.Microbial contamination exceeded permissible standards throughout the monitoring period,with E.coli counts as high as 7230 CFU/100 mL at S6,indicating a severe public health risk.Overall,WWTW-2(S5)showed a stronger local impact than WWTW-1(S2),while downstream persistence of salts and microbes indicates that dilution alone is insufficient.The study underscores the need for improved sewer system maintenance,infrastructure upgrades,and stricter enforcement of discharge standards to achieve compliance with Water Use Licenses.Enhanced real-time monitoring and implementation of RQOs are essential to safeguard the Trichardspruit River and regional water security.展开更多
Water infiltration in loess is one of the most significant processes that can lead to geological disasters on the Loess Plateau in China.However,the spatiotemporal characteristics of infiltration in intact loess,parti...Water infiltration in loess is one of the most significant processes that can lead to geological disasters on the Loess Plateau in China.However,the spatiotemporal characteristics of infiltration in intact loess,particularly the heterogeneity of three-dimensional(3D)seepage and its relationship with soil properties,remain poorly understood.This study conducted field infiltration tests on a 3 m×3 m loess column,instrumented with 48 moisture sensors to monitor 3D seepage and infiltration rates.Both horizontal and vertical infiltration behaviors were analyzed,alongside a detailed characterization of soil physical properties.The results demonstrated that the water infiltration process was significantly heterogeneous in intact loess,with wetting front velocities varying considerably across profiles and depths,despite relatively uniform physical properties.The infiltration process also exhibited pronounced anisotropy,with vertical wetting front velocities at least 10 times higher than those of the horizontal velocities.The in situ tests indicated that the physical properties were not the primary factors influencing infiltration.However,the microfeatures that facilitated the formation of preferential pathways exerted the most significant influence on the water infiltration process in thick unsaturated loess.Additionally,prior infiltration events negatively impacted subsequent infiltration in thick unsaturated loess.These findings enhance the understanding of loess infiltration dynamics,providing insights into unsaturated flow processes and their implications for geological stability in loess regions.展开更多
Responsive colorimetric materials exhibit significant potential for application in fields such as smart food packaging and wound monitoring.The functional integration of pH-indicators with material carriers enables br...Responsive colorimetric materials exhibit significant potential for application in fields such as smart food packaging and wound monitoring.The functional integration of pH-indicators with material carriers enables breakthrough applications in nontraditional domains.In this study,we developed a novel material covalently grafted with a pH indicator that exhibited naked-eye pH-responsive color shifts.The covalent grafting of pH-responsive bromothymol blue onto carboxymethyl cellulose(CMC)was confirmed using advanced characterization techniques,including Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy.The pH-sensitive chromophore was covalently immobilized onto the CMC matrix through esterification,thereby establishing firm chemical conjugation.Moreover,a superior color-changing performance was achieved within several minutes in response to different pH values.The reusability and stability of this material offer distinct advantages over single-use pH test strips.pH-responsive colorimetric materials hold promise for efficient,noninvasive monitoring in intelligent packaging(food freshness),medical diagnostics(wound status,infections),biosensing,and environmental applications.展开更多
Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this ...Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this end,we herein develop a portable self-calibrating platform namely carbon dots(C-dots)-embedded hydrogel sensors with a smartphone-assisted high-throughput imaging device,for DOX sensing.The prepared green-emitting(λ_(em)=508 nm)and negatively-charged C-dots(−11.40±1.21 mV),which have sufficient spectral overlap with the absorption band of DOX(∼500 nm),can strongly bind with positively-charged DOX molecules by electrostatic attraction effects.As a result,DOX molecules are selectively and rapid(20 s)determined with a detection limit of 10.26 nmol/L via Förster resonance energy transfer processes,demonstrating a remarkably chromatic shift from green to red.Further integrated with a 3D-printed smartphone-assisted device,the platform enabled high-throughput quantification,achieving recoveries of 96.40%-101.85%in human urine/serum(RSDs<2.94%,n=3).Notably,the dual linear detection ranges of the platform align with the reported clinical DOX concentrations in urine and plasma(0-4 h post-administration),validating their capability for direct quantification of DOX in clinical samples without special pre-treatment processes.By virtue of attractive analytical performances and robust feasibility,this platform bridges laboratory precision and point-of-care testing needs,offering promising potential for personalized chemotherapy and multiplexed analyte screening.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
文摘The performance of multiple processor based on Network on Chip (NoC) is limited to the communication efficiency of network. It is difficult to be optimized of routing and arbitration algorithm and be assessed of performance in the beginning of design because of its complex test cases. This paper constructs a scalable and monitored system level model with SystemC for NoC with Packet Connected Circuit (PCC) protocol. The overall performance and transfer details can be evaluated particularly by running the model, and the statistical basis can also be provided to the optimization of designing NoC.
基金National Natural Science Foundation of China(32270237)Guangxi Key Research and Development Plan Project(Guike AB21238002)Basic Scientific Research Project of Guangxi Academy of Agricultural Sciences(Guinongke 2024YP082)。
文摘【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.
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funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
基金supported by the National Key R&D Program of China(No.2023YFC3081400)the National Natural Science Foundation of China(No.41702371)+3 种基金the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention in Deep Coal Mines(No.SKLMRDPC22KF13)the Supported by State key Laboratory of Mining Disaster Prevention and Control(Shandong University of Science and Technology),Ministry of Education(No.DPEPM202502)the Open Fund Research Project Supported by State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology(No.SICGM202503)the Fund of Chongqing Key Laboratory of Facility Damage Mechanism and Protection in Highland Mountain Environment(No.LQ24KFJJ09)。
文摘0 INTRODUCTION Due to the sudden and highly destructive nature of slope rock collapse,developing effective early warning systems has become an urgent challenge in geotechnical engineering(Cai and Detournay,2024;Loew et al.,2017).Traditional monitoring methods primarily target the acceleration stage preceding disasters(such as displacement monitoring for landslides and debris flows),which is effective for early warning of plastic collapse disasters but often inadequate for brittle failure modes(Walter et al.,2019;Chao et al.,2018;Crosta et al.,2017).
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 Key Research and Development Project of the National Natural Science Foundation of China(Grant No.2022YFC3004605)the National Natural Science Foundation of China Youth Science Fund(Grant No.52104087).
文摘To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.
基金Sponsored by the National Natural Science Foundation of China[grant number 12571305]the Natural Science Foundation of Shanghai[grant number 25ZR1401113].
文摘Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.
文摘Monitoring cardiac function is a fundamental component of the diagnosis and management of critically ill patients.While pulmonary artery catheterization has long served as the standard for hemodynamic assessment,its invasive nature and associated risks have shifted clinical practice toward non-invasive modalities.^([1]) Among these methods,point-of-care ultrasound(POCUS) has gained widespread acceptance,offering real-time bedside evaluation of cardiac function.
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the financial support from the National University of Singapore(Grant No.A-001002800-00)the Singapore Ministry of Education(Grant No.A-8003587-00-00)。
文摘Health monitoring is becoming increasingly critical for disease prevention,early diagnosis,and highquality living.Polymeric materials,with their mechanical flexibility,biocompatibility,and tunable biochemical properties,offer unique advantages for creating next-generation personalized devices.In recent years,flexible polymer-based platforms have shown remarkable potential to capture diverse physiological signals in both daily and clinical contexts,including electrophysiological,biochemical,mechanical,and thermal indicators.In this review,we introduce a safety-leveloriented framework to evaluate material and device strategies for health monitoring,spanning the continuum from noninvasive wearables to deeply embedded implants.Physiological signals are systematically classified by use case,and application-specific requirements such as stability,comfort,and long-term compatibility are highlighted as critical factors guiding the selection of polymers,interfacial designs,and device architectures.Special emphasis is placed on mapping material types—including hydrogels,elastomers,and conductive composites—to their most suitable applications.Finally,we propose design principles for developing safe,functional,and adaptive polymer-based systems,aiming at reliable integration with the human body and enabling personalized,preventive healthcare.
文摘Satellite hydrology has reached a new era where a variety of earth observing systems,in conjunction with the development of data integrations,can provide a consistent monitoring of groundwater change,wetland dynamics,and drought evolution in regions where observations on the ground remain sparse or patchy.This synthesis review highlights the physical observables that are most useful to terrestrial hydrology:variable time-dependent gravity,passive and active microwave signals,optical reflectance,thermal infrared measurements,and altimetry,and this has been translated into hydrologic states and water fluxes:terrestrial water storage,ground water storage anomalies,inundation extent and hydroperiod,evapotranspiration,and multi-timescale drought indicators.We call attention to the role of satellite gravimetry in the transformation of basin-scale groundwater evaluation,synthetic aperture radar(SAR),and optical time series in transforming wetland science,however,to the characterization of hydroperiod and connectivity.In the case of drought,we recommend the use of both fast-responsive(soil moisture,evapotranspiration,thermal stress,and vegetation condition)in conjunction with slowly integrating storage anomalies to detect onset,intensification,and recovery,as well as rely on to diagnose the spread of deficits through the hydrologic system.In all these themes,we recognize the central action of enabling approaches that include multi-sensor fusion,data assimilation,and hybrid machine learning schemes in reducing ambiguity,scale gaps,and producing products of decision interest,and also highlighting ongoing issues in the quantification of uncertainty,consistency across long periods of time,and the separation of climate influence on human management of water.Research and operational priorities come as our final deduction to develop satellite hydrology to more trustworthy,explanation-seeking,and operationalizing monitoring designs of water security and ecosystem resilience.
文摘Water quality is fundamental to water security,ecosystem integrity,and public health.This study assessed the impact of wastewater effluent on the Trichardspruit River,which receives effluent from two wastewater treatment works(WWTWs).Over a 12-month period,water samples were collected across six study sites(S1–S6)to evaluate physicochemical and microbiological parameters relative to national Resource Quality Objectives(RQOs)and Water Use Licence(WUL)limits.One-way ANOVA showed significant(p<0.05)spatial differences for all parameters,and Tukey’s HSD identified the strongest pairwise contrasts at and below the discharge of WWTW-2(S5).Orthophosphate peaked at 7.26 mg/L at S5,and remained elevated at 3.6 mg/L at S6.Chemical oxygen demand reached 92 mg/L at S5,and likewise,EC and major ions(Cl^(−)and SO_(4)^(2−))were higher at S5&S6 than upstream.Microbial contamination exceeded permissible standards throughout the monitoring period,with E.coli counts as high as 7230 CFU/100 mL at S6,indicating a severe public health risk.Overall,WWTW-2(S5)showed a stronger local impact than WWTW-1(S2),while downstream persistence of salts and microbes indicates that dilution alone is insufficient.The study underscores the need for improved sewer system maintenance,infrastructure upgrades,and stricter enforcement of discharge standards to achieve compliance with Water Use Licenses.Enhanced real-time monitoring and implementation of RQOs are essential to safeguard the Trichardspruit River and regional water security.
基金supported by the National Natural Science Foundation of China(Grant No.42372307)the Natural Science Basic Research Program of Shaanxi Province(Grant No.2020JC-07)the SCEGC-XJTU Joint Research Center for Future City Construction and Management Innovation,Shaanxi Provincial Land Engineering Construction Group,and Xi’an Jiaotong University(Grant No.20221220).
文摘Water infiltration in loess is one of the most significant processes that can lead to geological disasters on the Loess Plateau in China.However,the spatiotemporal characteristics of infiltration in intact loess,particularly the heterogeneity of three-dimensional(3D)seepage and its relationship with soil properties,remain poorly understood.This study conducted field infiltration tests on a 3 m×3 m loess column,instrumented with 48 moisture sensors to monitor 3D seepage and infiltration rates.Both horizontal and vertical infiltration behaviors were analyzed,alongside a detailed characterization of soil physical properties.The results demonstrated that the water infiltration process was significantly heterogeneous in intact loess,with wetting front velocities varying considerably across profiles and depths,despite relatively uniform physical properties.The infiltration process also exhibited pronounced anisotropy,with vertical wetting front velocities at least 10 times higher than those of the horizontal velocities.The in situ tests indicated that the physical properties were not the primary factors influencing infiltration.However,the microfeatures that facilitated the formation of preferential pathways exerted the most significant influence on the water infiltration process in thick unsaturated loess.Additionally,prior infiltration events negatively impacted subsequent infiltration in thick unsaturated loess.These findings enhance the understanding of loess infiltration dynamics,providing insights into unsaturated flow processes and their implications for geological stability in loess regions.
基金financially supported by the National Natural Science Foundation of China(No.52303209)the“Lingyan”Program of Zhejiang Province(No.2024C03076)+1 种基金Zhejiang University K.P.Chao’s High Technology Development Foundationthe generous support provided by the joint research fund from the Shaoxing Institute of Zhejiang University and Shaoxing Maternity and Child Health Care Hospital。
文摘Responsive colorimetric materials exhibit significant potential for application in fields such as smart food packaging and wound monitoring.The functional integration of pH-indicators with material carriers enables breakthrough applications in nontraditional domains.In this study,we developed a novel material covalently grafted with a pH indicator that exhibited naked-eye pH-responsive color shifts.The covalent grafting of pH-responsive bromothymol blue onto carboxymethyl cellulose(CMC)was confirmed using advanced characterization techniques,including Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy.The pH-sensitive chromophore was covalently immobilized onto the CMC matrix through esterification,thereby establishing firm chemical conjugation.Moreover,a superior color-changing performance was achieved within several minutes in response to different pH values.The reusability and stability of this material offer distinct advantages over single-use pH test strips.pH-responsive colorimetric materials hold promise for efficient,noninvasive monitoring in intelligent packaging(food freshness),medical diagnostics(wound status,infections),biosensing,and environmental applications.
基金supported by the National NaturalScience Foundation of China(No.22274001)the Key Project of Natural Science Research of the Education Department of Anhui Province(No.2022AH051032)the Excellent Research and Innovation Team of Universities in Anhui Province(No.2024AH010016).
文摘Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this end,we herein develop a portable self-calibrating platform namely carbon dots(C-dots)-embedded hydrogel sensors with a smartphone-assisted high-throughput imaging device,for DOX sensing.The prepared green-emitting(λ_(em)=508 nm)and negatively-charged C-dots(−11.40±1.21 mV),which have sufficient spectral overlap with the absorption band of DOX(∼500 nm),can strongly bind with positively-charged DOX molecules by electrostatic attraction effects.As a result,DOX molecules are selectively and rapid(20 s)determined with a detection limit of 10.26 nmol/L via Förster resonance energy transfer processes,demonstrating a remarkably chromatic shift from green to red.Further integrated with a 3D-printed smartphone-assisted device,the platform enabled high-throughput quantification,achieving recoveries of 96.40%-101.85%in human urine/serum(RSDs<2.94%,n=3).Notably,the dual linear detection ranges of the platform align with the reported clinical DOX concentrations in urine and plasma(0-4 h post-administration),validating their capability for direct quantification of DOX in clinical samples without special pre-treatment processes.By virtue of attractive analytical performances and robust feasibility,this platform bridges laboratory precision and point-of-care testing needs,offering promising potential for personalized chemotherapy and multiplexed analyte screening.
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.
