Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/dist...Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.展开更多
In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indi...In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.展开更多
An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal colloca...An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal collocation method is less time-consuming and more accurate,which can meet the requirement of real-time optimization(RTO).In this paper,the equation-oriented method combined with the orthogonal collocation method and the finite difference method is adopted to build the RTO model for catalytic reforming regenerator.The orthogonal collocation method was adopted to discretize the differential equations and sequential quadratic programming(SQP)algorithm was used to solve the algebraic equations.The rate constants,active energy and reaction order were estimated,with the sum of relative errors between actual value and simulated value serving as optimization objective function.The model can quickly predict the fields of component concentration,temperature and pressure inside the regenerator under different conditions,as well as the real-time optimized conditions for industrial reforming regenerator.展开更多
BACKGROUND Although dumping symptoms constitute the most common post-gastrectomy syndromes impairing patient quality of life,the causes,including blood sugar fluctuations,are difficult to elucidate due to limitations ...BACKGROUND Although dumping symptoms constitute the most common post-gastrectomy syndromes impairing patient quality of life,the causes,including blood sugar fluctuations,are difficult to elucidate due to limitations in examining dumping symptoms as they occur.AIM To investigate relationships between glucose fluctuations and the occurrence of dumping symptoms in patients undergoing gastrectomy for gastric cancer.METHODS Patients receiving distal gastrectomy with Billroth-I(DG-BI)or Roux-en-Y reconstruction(DG-RY)and total gastrectomy with RY(TG-RY)for gastric cancer(March 2018-January 2020)were prospectively enrolled.Interstitial tissue glycemic profiles were measured every 15 min,up to 14 d,by continuous glucose monitoring.Dumping episodes were recorded on 5 patient-selected days by diary.Within 3 h postprandially,dumping-associated glycemic changes were defined as a dumping profile,those without symptoms as a control profile.These profiles were compared.RESULTS Thirty patients were enrolled(10 DG-BI,10 DG-RY,10 TG-RY).The 47 early dumping profiles of DG-BI showed immediately sharp rises after a meal,which 47 control profiles did not(P<0.05).Curves of the 15 late dumping profiles of DG-BI were similar to those of early dumping profiles,with lower glycemic levels.DGRY and TG-RY late dumping profiles(7 and 13,respectively)showed rapid glycemic decreases from a high glycemic state postprandially to hypoglycemia,with a steeper drop in TG-RY than in DG-RY.CONCLUSION Postprandial glycemic changes suggest dumping symptoms after standard gastrectomy for gastric cancer.Furthermore,glycemic profiles during dumping may differ depending on reconstruction methods after gastrectomy.展开更多
Single-cell imaging,a powerful analytical method to study single-cell behavior,such as gene expression and protein profiling,provides an essential basis for modern medical diagnosis.The coding and localization functio...Single-cell imaging,a powerful analytical method to study single-cell behavior,such as gene expression and protein profiling,provides an essential basis for modern medical diagnosis.The coding and localization function of microfluidic chips has been developed and applied in living single-cell imaging in recent years.Simultaneously,chip-based living single-cell imaging is also limited by complicated trapping steps,low cell utilization,and difficult high-resolution imaging.To solve these problems,an ultra-thin temperature-controllable microwell array chip(UTCMA chip)was designed to develop a living single-cell workstation in this study for continuous on-chip culture and real-time high-resolution imaging of living single cells.The chip-based on ultra-thin ITO glass is highly matched with an inverted microscope(or confocal microscope)with a high magnification objective(100×oil lens),and the temperature of the chip can be controlled by combining it with a home-made temperature control device.High-throughput single-cell patterning is realized in one step when the microwell array on the chip uses hydrophilic glass as the substrate and hydrophobic SU-8 photoresist as the wall.The cell utilization rate,single-cell capture rate,and microwell occupancy rate are all close to 100%in the microwell array.This method will be useful in rare single-cell research,extending its application in the biological and medical-related fields,such as early diagnosis of disease,personalized therapy,and research-based on single-cell analysis.展开更多
A low power dissipation control system for continuous cyclic peritoneal dialysis (CCPD) cycler and its characteristics are reported. Combined withhemodialysis and renal transplantation, peritoneal dialysis is used mai...A low power dissipation control system for continuous cyclic peritoneal dialysis (CCPD) cycler and its characteristics are reported. Combined withhemodialysis and renal transplantation, peritoneal dialysis is used mainly for thetreatment of renal failure. CCPD has been developed during 1980＇s. It provided automatic dialysis procedures during the night to avoid interruptions in patients＇dailyroutine. Furthermore,there is a remarkable decrease in peritonitis occurance usingCCPD. The control system is a critical part for CCPD cycler. The system is approvedto be reliable and flexible in practical experiments. When AC power failure,the system can still ensure the completion of dialysis.展开更多
Objective To assess the factors that influence the accuracy of real-time continuous glucose monitoring system(RT-CGM).Methods A total of 79 diabetic patients wore RT-CGM for three days continuously while calibrating b...Objective To assess the factors that influence the accuracy of real-time continuous glucose monitoring system(RT-CGM).Methods A total of 79 diabetic patients wore RT-CGM for three days continuously while calibrating by interphalangeal glucose values 4-8 times a day.We counted matching rate of interphalangeal glucose values and RT-CGM probe value,and analyzed correlation of the matching rate with MAGE,SDBG,MBG,AUC10,AUC3.9,and NGE by Pearson correlation analysis and multiple展开更多
During extensive gully land consolidation projects on China's Loess Plateau,many loess-bedrock fill slopes were formed,which frequently experience shallow landslides induced by rainfall.However,studies on loess-be...During extensive gully land consolidation projects on China's Loess Plateau,many loess-bedrock fill slopes were formed,which frequently experience shallow landslides induced by rainfall.However,studies on loess-bedrock slope failure triggered by continuous heavy rainfall are limited,and the role of the soilerock interface between the original bedrock slope and fill slope in the hydrological and failure process of the slope remains unclear.In this study,we conducted a continuous rainfall model test on a loess-bedrock fill slope.During the test,the responses of volume water content,pore pressure,micro deformation,and movement of the infiltration front were observed.The hydrological process and failure mechanism were then analysed.The findings suggest that the soilerock interface is a predominant infiltration surface within the slope.Rainfall infiltration rates at the interface reach 1.24-2.80 times those of the fill slope,with peak interfacial pore water pressure exceeding that of the loess fill.Furthermore,the infiltration front moves rapidly along the interface toward the bottom of the slope,reducing interfacial cohesion between bedrock and loess.The slope failure modes are summarised into three phases:local failure→flow slide and crack penetration→multistage block retrogressive slides.The cracks generated at the slope surface serve as key determinants of the geometry and scale of shallow landslides.Therefore,we recommend targeted engineering interventions to mitigate the instability and erosion of loessebedrock fill slopes.展开更多
Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exa...Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
The application of liquid core reduction(LCR)technology in thin slab continuous casting can refine the internal microstruc-tures of slabs and improve their production efficiency.To avoid crack risks caused by large de...The application of liquid core reduction(LCR)technology in thin slab continuous casting can refine the internal microstruc-tures of slabs and improve their production efficiency.To avoid crack risks caused by large deformation during the LCR process and to minimize the thickness of the slab in bending segments,the maximum theoretical reduction amount and the corresponding reduction scheme for the LCR process must be determined.With SPA-H weathering steel as a specific research steel grade,the distributions of tem-perature and deformation fields of a slab with the LCR process were analyzed using a three-dimensional thermal-mechanical finite ele-ment model.High-temperature tensile tests were designed to determine the critical strain of corner crack propagation and intermediate crack initiation with various strain rates and temperatures,and a prediction model of the critical strain for two typical cracks,combining the effects of strain rate and temperature,was proposed by incorporating the Zener-Hollomon parameter.The crack risks with different LCR schemes were calculated using the crack risk prediction model,and the maximum theoretical reduction amount for the SPA-H slab with a transverse section of 145 mm×1600 mm was 41.8 mm,with corresponding reduction amounts for Segment 0 to Segment 4 of 15.8,7.3,6.5,6.4,and 5.8 mm,respectively.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monit...Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.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
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing perme...In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
基金supported by the Natural Science Foundation of Zhejiang Province(No.ZCLQN25C0301)the National Key Research and Development Program of China(No.2016YFC0502700)the General Program of Education Department of Zhejiang(No.23056209-F).
文摘Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.
基金Projects(41502283,41772309)supported by the National Natural Science Foundation of ChinaProject(2017YFC1501302)supported by the National Key Research and Development Program of ChinaProject(2017ACA102)supported by the Major Program of Technological Innovation of Hubei Province,China。
文摘In this study,the micro-failure process and failure mechanism of a typical brittle rock under uniaxial compression are investigated via continuous real-time measurement of wave velocities.The experimental results indicate that the evolutions of wave velocities became progressively anisotropic under uniaxial loading due to the direction-dependent development of micro-damage.A wave velocity model considering the inner anisotropic crack evolution is proposed to accurately describe the variations of wave velocities during uniaxial compression testing.Based on which,the effective elastic parameters are inferred by a transverse isotropic constitutive model,and the evolutions of the crack density are inversed using a self-consistent damage model.It is found that the propagation of axial cracks dominates the failure process of brittle rock under uniaxial loading and oblique shear cracks develop with the appearance of macrocrack.
基金This work was supported by the Science and Technology Development Project of SINOPEC,China(No.319026).
文摘An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal collocation method is less time-consuming and more accurate,which can meet the requirement of real-time optimization(RTO).In this paper,the equation-oriented method combined with the orthogonal collocation method and the finite difference method is adopted to build the RTO model for catalytic reforming regenerator.The orthogonal collocation method was adopted to discretize the differential equations and sequential quadratic programming(SQP)algorithm was used to solve the algebraic equations.The rate constants,active energy and reaction order were estimated,with the sum of relative errors between actual value and simulated value serving as optimization objective function.The model can quickly predict the fields of component concentration,temperature and pressure inside the regenerator under different conditions,as well as the real-time optimized conditions for industrial reforming regenerator.
文摘BACKGROUND Although dumping symptoms constitute the most common post-gastrectomy syndromes impairing patient quality of life,the causes,including blood sugar fluctuations,are difficult to elucidate due to limitations in examining dumping symptoms as they occur.AIM To investigate relationships between glucose fluctuations and the occurrence of dumping symptoms in patients undergoing gastrectomy for gastric cancer.METHODS Patients receiving distal gastrectomy with Billroth-I(DG-BI)or Roux-en-Y reconstruction(DG-RY)and total gastrectomy with RY(TG-RY)for gastric cancer(March 2018-January 2020)were prospectively enrolled.Interstitial tissue glycemic profiles were measured every 15 min,up to 14 d,by continuous glucose monitoring.Dumping episodes were recorded on 5 patient-selected days by diary.Within 3 h postprandially,dumping-associated glycemic changes were defined as a dumping profile,those without symptoms as a control profile.These profiles were compared.RESULTS Thirty patients were enrolled(10 DG-BI,10 DG-RY,10 TG-RY).The 47 early dumping profiles of DG-BI showed immediately sharp rises after a meal,which 47 control profiles did not(P<0.05).Curves of the 15 late dumping profiles of DG-BI were similar to those of early dumping profiles,with lower glycemic levels.DGRY and TG-RY late dumping profiles(7 and 13,respectively)showed rapid glycemic decreases from a high glycemic state postprandially to hypoglycemia,with a steeper drop in TG-RY than in DG-RY.CONCLUSION Postprandial glycemic changes suggest dumping symptoms after standard gastrectomy for gastric cancer.Furthermore,glycemic profiles during dumping may differ depending on reconstruction methods after gastrectomy.
基金supported by the National Natural Science Foundation of China(Nos.21625501,21936001)the Beijing Outstanding Young Scientist Program(No.BJJWZYJH01201910005017).
文摘Single-cell imaging,a powerful analytical method to study single-cell behavior,such as gene expression and protein profiling,provides an essential basis for modern medical diagnosis.The coding and localization function of microfluidic chips has been developed and applied in living single-cell imaging in recent years.Simultaneously,chip-based living single-cell imaging is also limited by complicated trapping steps,low cell utilization,and difficult high-resolution imaging.To solve these problems,an ultra-thin temperature-controllable microwell array chip(UTCMA chip)was designed to develop a living single-cell workstation in this study for continuous on-chip culture and real-time high-resolution imaging of living single cells.The chip-based on ultra-thin ITO glass is highly matched with an inverted microscope(or confocal microscope)with a high magnification objective(100×oil lens),and the temperature of the chip can be controlled by combining it with a home-made temperature control device.High-throughput single-cell patterning is realized in one step when the microwell array on the chip uses hydrophilic glass as the substrate and hydrophobic SU-8 photoresist as the wall.The cell utilization rate,single-cell capture rate,and microwell occupancy rate are all close to 100%in the microwell array.This method will be useful in rare single-cell research,extending its application in the biological and medical-related fields,such as early diagnosis of disease,personalized therapy,and research-based on single-cell analysis.
文摘A low power dissipation control system for continuous cyclic peritoneal dialysis (CCPD) cycler and its characteristics are reported. Combined withhemodialysis and renal transplantation, peritoneal dialysis is used mainly for thetreatment of renal failure. CCPD has been developed during 1980＇s. It provided automatic dialysis procedures during the night to avoid interruptions in patients＇dailyroutine. Furthermore,there is a remarkable decrease in peritonitis occurance usingCCPD. The control system is a critical part for CCPD cycler. The system is approvedto be reliable and flexible in practical experiments. When AC power failure,the system can still ensure the completion of dialysis.
文摘Objective To assess the factors that influence the accuracy of real-time continuous glucose monitoring system(RT-CGM).Methods A total of 79 diabetic patients wore RT-CGM for three days continuously while calibrating by interphalangeal glucose values 4-8 times a day.We counted matching rate of interphalangeal glucose values and RT-CGM probe value,and analyzed correlation of the matching rate with MAGE,SDBG,MBG,AUC10,AUC3.9,and NGE by Pearson correlation analysis and multiple
基金supported by the National Key R&D Program of China(Grant No.2023YFC3008404)the National Key Research and Development Program,China(Grant No.2017YFD0800501)the National Natural Science Foundation of China(No.41790443).
文摘During extensive gully land consolidation projects on China's Loess Plateau,many loess-bedrock fill slopes were formed,which frequently experience shallow landslides induced by rainfall.However,studies on loess-bedrock slope failure triggered by continuous heavy rainfall are limited,and the role of the soilerock interface between the original bedrock slope and fill slope in the hydrological and failure process of the slope remains unclear.In this study,we conducted a continuous rainfall model test on a loess-bedrock fill slope.During the test,the responses of volume water content,pore pressure,micro deformation,and movement of the infiltration front were observed.The hydrological process and failure mechanism were then analysed.The findings suggest that the soilerock interface is a predominant infiltration surface within the slope.Rainfall infiltration rates at the interface reach 1.24-2.80 times those of the fill slope,with peak interfacial pore water pressure exceeding that of the loess fill.Furthermore,the infiltration front moves rapidly along the interface toward the bottom of the slope,reducing interfacial cohesion between bedrock and loess.The slope failure modes are summarised into three phases:local failure→flow slide and crack penetration→multistage block retrogressive slides.The cracks generated at the slope surface serve as key determinants of the geometry and scale of shallow landslides.Therefore,we recommend targeted engineering interventions to mitigate the instability and erosion of loessebedrock fill slopes.
基金supported by National Key Research and Development Program of China(Grant No.2023YFB4604100)National Key Research and Development Program of China(Grant No.2022YFB3806104)+4 种基金Key Research and Development Program in Shaanxi Province(Grant No.2021LLRH-08-17)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)K C Wong Education Foundation of ChinaYouth Innovation Team of Shaanxi Universities of ChinaKey Research and Development Program of Shaanxi Province(Grant 2021LLRH-08-3.1).
文摘Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
基金supported by the National Natural Science Foundation of China(No.52474355)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project),China(Nos.2022JH25/10200003 and 2023JH2/101800058).
文摘The application of liquid core reduction(LCR)technology in thin slab continuous casting can refine the internal microstruc-tures of slabs and improve their production efficiency.To avoid crack risks caused by large deformation during the LCR process and to minimize the thickness of the slab in bending segments,the maximum theoretical reduction amount and the corresponding reduction scheme for the LCR process must be determined.With SPA-H weathering steel as a specific research steel grade,the distributions of tem-perature and deformation fields of a slab with the LCR process were analyzed using a three-dimensional thermal-mechanical finite ele-ment model.High-temperature tensile tests were designed to determine the critical strain of corner crack propagation and intermediate crack initiation with various strain rates and temperatures,and a prediction model of the critical strain for two typical cracks,combining the effects of strain rate and temperature,was proposed by incorporating the Zener-Hollomon parameter.The crack risks with different LCR schemes were calculated using the crack risk prediction model,and the maximum theoretical reduction amount for the SPA-H slab with a transverse section of 145 mm×1600 mm was 41.8 mm,with corresponding reduction amounts for Segment 0 to Segment 4 of 15.8,7.3,6.5,6.4,and 5.8 mm,respectively.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
文摘Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
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supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
基金supported by the National Natural Science Foundation of China (Grant No.52122405)Science and Technology Major Project of Shanxi Province,China (Grant No.202101060301024)Science and Technology Major Project of Xizang Autonomous Region,China (Grant No.XZ202201ZD0004G0204).
文摘In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
