In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed i...In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.展开更多
Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a...Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.展开更多
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great con...In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.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
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
This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is...This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.展开更多
Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require t...Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require timely information regarding the current(diagnosis)and future(prognosis)condition of their assets to make informed decisions on maintenance and renewal actions.In recent years,in-service vehicles equipped with on-board monitoring(OBM)measuring devices,such as accelerometers,have been introduced on railroad networks,traversing the network almost daily.This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction,primarily under the prism of track geometry quality.The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements(high frequency and relatively lower accuracy data)compared to those generated from higher precision,yet temporally sparser,data collected by traditional track recording vehicles(TRVs)for infrastructure management purposes.The findings demonstrate the performance of the two approaches,further revealing the value of OBM information for monitoring the track status degradation process.This work makes a case for the advantageous use of OBM data for railway infrastructure management,and attempts to aid understanding in the application of OBM techniques for engineers and operators.展开更多
The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties ...The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the...Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel.An in situ microseismic(MS)monitoring system is established in the plateau soft tock tunnel.This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation,which agree well with field convergence deformation results.The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results.The results demonstrate that:(1)Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section.As excavation continues,the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding;(2)Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass,vertical tension fractures(account for 45%)exist in deep rockmass,which play a crucial role in controlling the macroscopic failure of surrounding rock;and(3)Based on the radiated MS energy,a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock.The model depicted varying degrees of damage,and three high damage zones were identified.Generally,the depth of high damage zone ranged from 4 m to 12 m.This study may be a valuable reference for the warning and controlling of large deformations in similar projects.展开更多
With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, ...With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.展开更多
Wearable sensing systems have been designed to monitor health conditions in real-time by detecting analytes in human biofluids.Wound diagnosis remains challenging,necessitating suitable materials for high-performance ...Wearable sensing systems have been designed to monitor health conditions in real-time by detecting analytes in human biofluids.Wound diagnosis remains challenging,necessitating suitable materials for high-performance wearable sensors to offer prompt feedback.Existing devices have limitations in measuring pH and the concentration of pH-dependent electroactive species simultaneously,which is crucial for obtaining a comprehensive understanding of wound status and optimizing biosensors.Therefore,improving materials and analysis system accuracy is essential.This article introduces the first example of a flexible array capable of detecting pyocyanin,a bacterial virulence factor,while correcting dynamic pH fluctuations.We demonstrate that this combined sensor enhances accuracy by mitigating the impact of pH variability on pyocyanin sensor response.Customized screen-printable inks were developed to enhance analytical performance.The analytical performances of two sensitive sensor systems(i.e.,fully-printed porous graphene/multiwalled carbon nanotube(CNT)and polyaniline/CNT composites for pyocyanin and pH sensors)are evaluated.Partial least square regression is employed to analyze nonzero-order data arrays from square wave voltammetric and potentiometric measurements of pyocyanin and pH sensors to establish a predictive model for pyocyanin concentration in complex fluids.This sensitive and effective strategy shows potential for personalized applications due to its affordability,ease of use,and ability to adjust for dynamic pH changes.展开更多
Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityh...Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.展开更多
Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data...Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.展开更多
基金Research Committee,National Technical University of Athens。
文摘In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the National Natural Science Foundation of China(42022028)+1 种基金the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144)。
文摘In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
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 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.
文摘This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.
基金supported financially by the project OMISM from the ETH Zurich Mobility Initiative。
文摘Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require timely information regarding the current(diagnosis)and future(prognosis)condition of their assets to make informed decisions on maintenance and renewal actions.In recent years,in-service vehicles equipped with on-board monitoring(OBM)measuring devices,such as accelerometers,have been introduced on railroad networks,traversing the network almost daily.This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction,primarily under the prism of track geometry quality.The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements(high frequency and relatively lower accuracy data)compared to those generated from higher precision,yet temporally sparser,data collected by traditional track recording vehicles(TRVs)for infrastructure management purposes.The findings demonstrate the performance of the two approaches,further revealing the value of OBM information for monitoring the track status degradation process.This work makes a case for the advantageous use of OBM data for railway infrastructure management,and attempts to aid understanding in the application of OBM techniques for engineers and operators.
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
基金financially supported by the National Natural Science Foundation of China(No.52371049)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(YESS,No.2020QNRC001)the National Science and Technology Resources Investigation Program of China(Nos.2021FY100603 and 2019FY101404)。
文摘The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金the funding support from the National Natural Science Foundation of China(Grant Nos.U23A2060,42177143 and 42277461).
文摘Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel.An in situ microseismic(MS)monitoring system is established in the plateau soft tock tunnel.This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation,which agree well with field convergence deformation results.The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results.The results demonstrate that:(1)Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section.As excavation continues,the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding;(2)Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass,vertical tension fractures(account for 45%)exist in deep rockmass,which play a crucial role in controlling the macroscopic failure of surrounding rock;and(3)Based on the radiated MS energy,a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock.The model depicted varying degrees of damage,and three high damage zones were identified.Generally,the depth of high damage zone ranged from 4 m to 12 m.This study may be a valuable reference for the warning and controlling of large deformations in similar projects.
基金supported by the National Natural Science Foundation of China(Nos.52373093 and 12072325)the Outstanding Youth Fund of Henan Province(No.242300421062)+1 种基金National Key R&D Program of China(No.2019YFA0706802)the 111 project(No.D18023).
文摘With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
基金the Talent Management Project of Prince of Songkla University
文摘Wearable sensing systems have been designed to monitor health conditions in real-time by detecting analytes in human biofluids.Wound diagnosis remains challenging,necessitating suitable materials for high-performance wearable sensors to offer prompt feedback.Existing devices have limitations in measuring pH and the concentration of pH-dependent electroactive species simultaneously,which is crucial for obtaining a comprehensive understanding of wound status and optimizing biosensors.Therefore,improving materials and analysis system accuracy is essential.This article introduces the first example of a flexible array capable of detecting pyocyanin,a bacterial virulence factor,while correcting dynamic pH fluctuations.We demonstrate that this combined sensor enhances accuracy by mitigating the impact of pH variability on pyocyanin sensor response.Customized screen-printable inks were developed to enhance analytical performance.The analytical performances of two sensitive sensor systems(i.e.,fully-printed porous graphene/multiwalled carbon nanotube(CNT)and polyaniline/CNT composites for pyocyanin and pH sensors)are evaluated.Partial least square regression is employed to analyze nonzero-order data arrays from square wave voltammetric and potentiometric measurements of pyocyanin and pH sensors to establish a predictive model for pyocyanin concentration in complex fluids.This sensitive and effective strategy shows potential for personalized applications due to its affordability,ease of use,and ability to adjust for dynamic pH changes.
基金supported by the National Natural Science Foundation of China(52303257,52321006,T2394480,and T2394484)the National Key R&D Program of China(Grant No.2023YFE0111500)+3 种基金Key Research&Development and Promotion of Special Project(Scientific Problem Tackling)of Henan Province(242102211090)the China Postdoctoral Science Foundation(2023TQ0300,and 2023M743171)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(GZB20230666)College Student Innovation and Entrepreneurship Training Program of Zhengzhou University(202410459200)。
文摘Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.
文摘Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.
