Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,...Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.展开更多
During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) s...During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) site,China,and in 2006,at a background site in Duolun (DL).The long-term monitoring data of elements,ions,and black carbon showed that the major constituents of PM2.5 were black carbon (BC) crustal elements,nitrates,ammonium salts,and sulfates.These five major components accounted for 20%-80% of...展开更多
This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperatur...This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.展开更多
Three kind of application of ADCP is reported for long-term monitoring in coastal sea.(1)The routine monitoring of water qualities. The water quality and ADCP echo data (600 kHz) observed in the long-term are analgzed...Three kind of application of ADCP is reported for long-term monitoring in coastal sea.(1)The routine monitoring of water qualities. The water quality and ADCP echo data (600 kHz) observed in the long-term are analgzed at MT (Marine Tower) Station of Kansai International Airport in the Osaka Bay, Japan. The correlation between the turbidity and echo intensity in the surface layer is not good because air bubbles generated by breaking wave are not detected by the turbidity meter, but detected well by ADCP. When estimating the turbidity consists of plankton population from echo intensity, the effect of bubbles have to be eliminated. (2) Monitoring stirring up of bottom sediment. The special observation was carried out by using following two ADCP in the Osaka Bay, One ADCP was installed upward on the sea. The other ADCP was hanged downward at the gate type stand about 3 m above from the bottom. At the spring tide, high echo intensities indicating the stirring up of bottom sediment were observed. (3) The monitoring for the boundary condition of water mixing at an estuary. In summer season, the ADCP was set at the mouth of Tanabe Bay in Wakayama Prefecture, Japan. During the observation, water temperature near the bottom showed remarkable falls with interval of about 5-7 d. When the bottom temperature fell, the inflow current with low echo intensity water appears at the bottom layer in the ADCP record. It is concluded that when occasional weak northeast wind makes weak coastal upwelling at the mouth of the bay, the combination of upwelling with internal tidal flow causes remarkable water exchange and dispels the red tide.展开更多
AIM: Consecutive monitoring of intragastric pH using the Bravo? capsule. METHODS: We put threads through a Bravo? capsule and then affixed it to the gastric wall by endoscopic hemoclipping in seven subjects. Study dat...AIM: Consecutive monitoring of intragastric pH using the Bravo? capsule. METHODS: We put threads through a Bravo? capsule and then affixed it to the gastric wall by endoscopic hemoclipping in seven subjects. Study data were uploaded to a computer via Datalink every 48 h. In this way,repeated monitoring of intragastric pH was undertaken. RESULTS: All subjects were able to monitor gastric pH over a 1-wk period,and five for > 2 wk. No complications were encountered during the monitoring. After pH monitoring,we safely retrieved the capsule endoscopically. CONCLUSION: Clipping a Bravo? capsule onto the gastric wall enabled long-term intragastric pH monitoring. This is a methodological report of pH monitoring over a period of > 2 wk.展开更多
Studies presenting long-term observations of the recruitment and mobility of large wood in mountain watercourses are scarce,but they can considerably contribute to the knowledge of river/riparian forest interactions a...Studies presenting long-term observations of the recruitment and mobility of large wood in mountain watercourses are scarce,but they can considerably contribute to the knowledge of river/riparian forest interactions and the assessment of flood hazard resulting from wood mobility during floods.Widespread dieback of riparian forest along the headwater course of Kamienica Stream in the Polish Carpathians,caused by bark beetle infestation of spruce trees,has raised concerns about potential increases of large wood recruitment to the stream and of the flood hazard to downstream valley reaches.In October 2009,429 trees growing along three sections of the stream were tagged with numbered metal plates and monitored over 10 years to determine the timing and causes of their delivery to the channel and the lengths of their displacement during individual flood events.Moreover,in 2012 the mode of location of wood deposits and a degree of wood decay were determined in the second-to fourth-order stream reaches.The monitoring of tagged trees indicated that trees were recruited to the channel during highintensity meteorological and hydrological events,mostly as a result of bank erosion during floods or windthrow.With 22%of tagged trees recruited to the channel during 10 years,the rate of turnover of the riparian trees was estimated at 45 years.As the riparian area is overgrown with trees with ages up to^160 years,the rate evidences substantial intensification of large wood recruitment to the channel in the recent period.Results of large wood inventory and the 10-year-long monitoring of tagged trees indicated variable mobility of large wood along the upper course of the stream.Wood mobility was negligible in the second-order stream reach,very small in the third-order reach,and greater,but still limited in the fourth-order reach.Wood is transported longer distances only during major floods.However,the advanced state of decay of most pieces leads to their disintegration during floods,precluding distant transport.Thus,large wood retained in the upper stream course does not constitute an important flood hazard to downstream,inhabited valley reaches.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting s...Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.展开更多
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
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
The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show...The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.展开更多
Accurate quantification of exercise interventions and changes in muscle function is essential for personalized health management.Electrical impedance myography(EIM)technology offers an innovative,noninvasive,painless,...Accurate quantification of exercise interventions and changes in muscle function is essential for personalized health management.Electrical impedance myography(EIM)technology offers an innovative,noninvasive,painless,and easy-to-perform solution for muscle health monitoring.However,current EIM platforms face a number of limitations,including large device size,wired connections,and instability of the electrode-skin interface,which limit their applicability for monitoring mus-cle movement.In this study,a miniature wireless EIM platform with a user-friendly smartphone app is proposed and devel-oped.The miniature,wireless,multi-frequency(20 kHz-1 MHz)EIM platform is equipped with flexible microneedle array elec-trodes(MAE).The advantages of MAEs over conventional electrodes were demonstrated by physical field modeling simula-tions and skin-electrode contact impedance comparison tests.The smartphone APP was developed to wirelessly operate the EIM platform,and to transmit and process real-time muscle impedance data.To validate its effectiveness,a seven-day adaptive fatigue training study was conducted,which demonstrated that the EIM platform was able to detect muscle adaptations and serve as a reliable indicator of fatigue.This study presents an innovative approach to applying EIM technology to muscle health monitoring and exercise testing,thereby advancing the development of personalized health management and athletic performance assessment.展开更多
Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detec...Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.展开更多
In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Poin...In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.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
A landslide always results from a progressive process of slope deformation. In recent years, an increasing number of slope instabilities have occurred with regard to human engineering activities such as hydropower or ...A landslide always results from a progressive process of slope deformation. In recent years, an increasing number of slope instabilities have occurred with regard to human engineering activities such as hydropower or traffic construction in mountainous area, which cause even greater casualties and economic loss compared with the natural hazards. The development of such earth surface process may hold long period with mechanisms still not fully understood. Using monitoring technology is an effective and intuitive approach to assist analyzing the slope deformation process and their driving factors. This study presents an engineering slope excavated during the construction of Changheba Hydropower Station, which is located in the upper reaches of Dadu River, Sichuan Province, southwest China. The engineering slope experienced and featured a five-year continuous deformation which caused continuous high risks to the engineering activities. We conducted in-depth analysis for such a long-term deformation process based on ground and subsurface monitoring data, collected successive data with a series of monitoring equipment such as automated total station, borehole inclinometers and other auxiliary apparatus, and identified the deformation process based on the comprehensive analysis of monitoring data as well as field investigation. After analyzing the effects of engineering activities and natural factors on the continuous deformation, we found that the overburden strata provided deformable mass while the excavation-produced steep terrain initiated the slope deformation in limit equilibrium state over a long period of time;afterwards, the intense rainwater accelerated slope deformation in the rainy season.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
Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficu...Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.展开更多
General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relyin...General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.展开更多
基金funded by the So Lo Mon project“Monitoraggio a Lungo Termine di Grandi Frane basato su Sistemi Integrati di Sensori e Reti”(Longterm monitoring of large-scale landslides based on integrated systems of sensors and networks),Program EFRE-FESR 2014–2020,Project EFRE-FESR4008 South Tyrol–Person in charge:V.Mair。
文摘Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.
基金the National Science Fund for Distinguished Young Scholars (No.20725723)
文摘During 2001-2006,PM2.5 (particle matter with aerodynamic diameter less than 2.5 microns) and PM10 (particle matter with aerodynamic diameter less than 10 microns) were collected at the Beijng Normal University (BNU) site,China,and in 2006,at a background site in Duolun (DL).The long-term monitoring data of elements,ions,and black carbon showed that the major constituents of PM2.5 were black carbon (BC) crustal elements,nitrates,ammonium salts,and sulfates.These five major components accounted for 20%-80% of...
基金National Natural Science Foundation of China Under Grant No.50725828 & No.50808041PhD Programs Foundation of Ministry of Education of China Under Grant No. 200802861011Scientific Research Foundation of Graduate School of Southeast University Under Grant No.YBJJ0923
文摘This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, first. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.
文摘Three kind of application of ADCP is reported for long-term monitoring in coastal sea.(1)The routine monitoring of water qualities. The water quality and ADCP echo data (600 kHz) observed in the long-term are analgzed at MT (Marine Tower) Station of Kansai International Airport in the Osaka Bay, Japan. The correlation between the turbidity and echo intensity in the surface layer is not good because air bubbles generated by breaking wave are not detected by the turbidity meter, but detected well by ADCP. When estimating the turbidity consists of plankton population from echo intensity, the effect of bubbles have to be eliminated. (2) Monitoring stirring up of bottom sediment. The special observation was carried out by using following two ADCP in the Osaka Bay, One ADCP was installed upward on the sea. The other ADCP was hanged downward at the gate type stand about 3 m above from the bottom. At the spring tide, high echo intensities indicating the stirring up of bottom sediment were observed. (3) The monitoring for the boundary condition of water mixing at an estuary. In summer season, the ADCP was set at the mouth of Tanabe Bay in Wakayama Prefecture, Japan. During the observation, water temperature near the bottom showed remarkable falls with interval of about 5-7 d. When the bottom temperature fell, the inflow current with low echo intensity water appears at the bottom layer in the ADCP record. It is concluded that when occasional weak northeast wind makes weak coastal upwelling at the mouth of the bay, the combination of upwelling with internal tidal flow causes remarkable water exchange and dispels the red tide.
文摘AIM: Consecutive monitoring of intragastric pH using the Bravo? capsule. METHODS: We put threads through a Bravo? capsule and then affixed it to the gastric wall by endoscopic hemoclipping in seven subjects. Study data were uploaded to a computer via Datalink every 48 h. In this way,repeated monitoring of intragastric pH was undertaken. RESULTS: All subjects were able to monitor gastric pH over a 1-wk period,and five for > 2 wk. No complications were encountered during the monitoring. After pH monitoring,we safely retrieved the capsule endoscopically. CONCLUSION: Clipping a Bravo? capsule onto the gastric wall enabled long-term intragastric pH monitoring. This is a methodological report of pH monitoring over a period of > 2 wk.
基金financed by the statutory funds of the Institute of Nature Conservation,Polish Academy of Sciencesby the project FLORIST(Flood risk on the northern foothills of the Tatra MountainsPSPB no 153/2010)supported by a grant from Switzerland through the Swiss Contribution to the Enlarged European Union。
文摘Studies presenting long-term observations of the recruitment and mobility of large wood in mountain watercourses are scarce,but they can considerably contribute to the knowledge of river/riparian forest interactions and the assessment of flood hazard resulting from wood mobility during floods.Widespread dieback of riparian forest along the headwater course of Kamienica Stream in the Polish Carpathians,caused by bark beetle infestation of spruce trees,has raised concerns about potential increases of large wood recruitment to the stream and of the flood hazard to downstream valley reaches.In October 2009,429 trees growing along three sections of the stream were tagged with numbered metal plates and monitored over 10 years to determine the timing and causes of their delivery to the channel and the lengths of their displacement during individual flood events.Moreover,in 2012 the mode of location of wood deposits and a degree of wood decay were determined in the second-to fourth-order stream reaches.The monitoring of tagged trees indicated that trees were recruited to the channel during highintensity meteorological and hydrological events,mostly as a result of bank erosion during floods or windthrow.With 22%of tagged trees recruited to the channel during 10 years,the rate of turnover of the riparian trees was estimated at 45 years.As the riparian area is overgrown with trees with ages up to^160 years,the rate evidences substantial intensification of large wood recruitment to the channel in the recent period.Results of large wood inventory and the 10-year-long monitoring of tagged trees indicated variable mobility of large wood along the upper course of the stream.Wood mobility was negligible in the second-order stream reach,very small in the third-order reach,and greater,but still limited in the fourth-order reach.Wood is transported longer distances only during major floods.However,the advanced state of decay of most pieces leads to their disintegration during floods,precluding distant transport.Thus,large wood retained in the upper stream course does not constitute an important flood hazard to downstream,inhabited valley reaches.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金supported by the National Key Research and Development Program of China under grant 2022YFF0902701the National Natural Science Foundation of China under grant U21A20468,61972043,61921003+1 种基金Zhejiang Lab under grant 2021PD0AB 02the Fundamental Research Funds for the Central Universities under grant 2020XD-A07-1.
文摘Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
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.
基金supported by the National Natural Science Foundation of China(NSFC 52175281,52475315)Youth Innovation Promotion Association of CAS(2021382)。
文摘The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.
文摘Accurate quantification of exercise interventions and changes in muscle function is essential for personalized health management.Electrical impedance myography(EIM)technology offers an innovative,noninvasive,painless,and easy-to-perform solution for muscle health monitoring.However,current EIM platforms face a number of limitations,including large device size,wired connections,and instability of the electrode-skin interface,which limit their applicability for monitoring mus-cle movement.In this study,a miniature wireless EIM platform with a user-friendly smartphone app is proposed and devel-oped.The miniature,wireless,multi-frequency(20 kHz-1 MHz)EIM platform is equipped with flexible microneedle array elec-trodes(MAE).The advantages of MAEs over conventional electrodes were demonstrated by physical field modeling simula-tions and skin-electrode contact impedance comparison tests.The smartphone APP was developed to wirelessly operate the EIM platform,and to transmit and process real-time muscle impedance data.To validate its effectiveness,a seven-day adaptive fatigue training study was conducted,which demonstrated that the EIM platform was able to detect muscle adaptations and serve as a reliable indicator of fatigue.This study presents an innovative approach to applying EIM technology to muscle health monitoring and exercise testing,thereby advancing the development of personalized health management and athletic performance assessment.
基金Supported by National Natural Science Foundation of China(Grant Nos.61403232,61327003)Shandong Provincial Natural Science Foundation of China(Grant No.ZR2014FQ025)Young Scholars Program of Shandong University,China(YSPSDU,2015WLJH30)
文摘Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.
基金Nacional de Promoción Científica y Tecnológica(Grant:PICTO 2010-0111)the Instituto Antártico Argentino-Dirección Nacional del Antártico(PINST-05)provided financial and logistical support.
文摘In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.
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funded by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(2019QZKK0904)National Natural Science Foundation of China(42077266,41825018,42090051,41941018,41902289)Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23090402)。
文摘A landslide always results from a progressive process of slope deformation. In recent years, an increasing number of slope instabilities have occurred with regard to human engineering activities such as hydropower or traffic construction in mountainous area, which cause even greater casualties and economic loss compared with the natural hazards. The development of such earth surface process may hold long period with mechanisms still not fully understood. Using monitoring technology is an effective and intuitive approach to assist analyzing the slope deformation process and their driving factors. This study presents an engineering slope excavated during the construction of Changheba Hydropower Station, which is located in the upper reaches of Dadu River, Sichuan Province, southwest China. The engineering slope experienced and featured a five-year continuous deformation which caused continuous high risks to the engineering activities. We conducted in-depth analysis for such a long-term deformation process based on ground and subsurface monitoring data, collected successive data with a series of monitoring equipment such as automated total station, borehole inclinometers and other auxiliary apparatus, and identified the deformation process based on the comprehensive analysis of monitoring data as well as field investigation. After analyzing the effects of engineering activities and natural factors on the continuous deformation, we found that the overburden strata provided deformable mass while the excavation-produced steep terrain initiated the slope deformation in limit equilibrium state over a long period of time;afterwards, the intense rainwater accelerated slope deformation in the rainy season.
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supports received from Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)+3 种基金the Natural Science Foundation of Liaoning Province(Grant No.2024-MS-075)the National Natural Science Foundation of China(32201179)National Key R&D Program of China(2023YFC2508200)Liaoning Provincial Natural Science Foundation Joint Fund(General Support Program Project)(2023-MSBA-093).
文摘Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.
基金supported by grants from the Shanghai Municipal Health Commission(2023ZDFC0203)the National Natural Science Foundation of China(32171044).
文摘General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.
