Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source...With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green's functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green 's functions between the airgun source and stations.展开更多
The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regressi...The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.展开更多
Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it...Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.展开更多
A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.Howeve...A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.展开更多
This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution langua...This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution language(BPEL)is modified in company with the analysis of data dependency and an exact representation of dead path elimination(DPE)is proposed,which over-comes the difficulties brought to dataflow analysis.Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language(WSDL)documents and the def-use annotated control flow graph is created.Based on this model,data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph,and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated,then testers can design the test cases according to the collected constraints for each path selected.展开更多
In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so o...In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.展开更多
In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of ...In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.展开更多
With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This...With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.展开更多
This study proposes a new educational assessment framework to realize the organic integration of traditional assessment methods and digital technology.The model builds a three-dimensional assessment system by integrat...This study proposes a new educational assessment framework to realize the organic integration of traditional assessment methods and digital technology.The model builds a three-dimensional assessment system by integrating dynamic data flow and educational big data.Considering the limitation of traditional assessment methods relying on periodic summary,a continuous dynamic feedback mechanism and a personalized learning tracking module are innovatively introduced.The technical program deeply integrates the educational theoretical system and intelligent algorithm technology to establish a panoramic assessment model in multiple dimensions such as academic development,class participation,and learning emotion.The system integrates classroom recording data,online assessment results,learning behavior logs,and other multi-source information to form a holographic portrait of learners'growth.In the research process,real teaching areas are selected to conduct technical verification.Experimental data show that the framework can effectively capture the dynamic characteristics of the learning process and provide real-time decision support for adapting teaching strategies.The technology optimization direction focuses on fine-tuning the granularity of data acquisition and explores the possibility of integrating new data inputs such as wearable devices.The practical application of this evaluation framework marks an important breakthrough in the direction of an intelligent and real-time education quality monitoring system.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
This paper presents the methodology of process driven analysis to eliminate the redundant information conducted from homogeneous procedures in management system. Process flow is emphasized and events triggered by pro...This paper presents the methodology of process driven analysis to eliminate the redundant information conducted from homogeneous procedures in management system. Process flow is emphasized and events triggered by process based states are described.展开更多
Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such a...Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental monitoring.However,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell analysis.In this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image processing.Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and analyzed.The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples.This work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.展开更多
Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as elec...Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.展开更多
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金jointly sponsored by the Special Fund for Earthquake Scientific Research in the Public Welfare of China Earthquake Administration(201508008)the tundamental Research Funds for the Central University(WK2080000053)Academician Chen Yong Workstation Project in Yunnan Province
文摘With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green's functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green 's functions between the airgun source and stations.
文摘The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.
基金partly supported by the National Natural Science Foundation of China(grants no.41272132 and 41572080)the Fundamental Research Funds for central Universities(grant no.2-9-2013-97)the Major State Science and Technology Research Programs(grants no.2008ZX05056-002-02-01 and 2011ZX05010-001-009)
文摘Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.
基金supported by the National Natural Science Foundation of China (under grants 41874048,41790464,41790462).
文摘A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.
基金the National Natural Science Foundation of China(60425206,60503033)National Basic Research Program of China(973 Program,2002CB312000)Opening Foundation of State Key Laboratory of Software Engineering in Wuhan University
文摘This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution language(BPEL)is modified in company with the analysis of data dependency and an exact representation of dead path elimination(DPE)is proposed,which over-comes the difficulties brought to dataflow analysis.Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language(WSDL)documents and the def-use annotated control flow graph is created.Based on this model,data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph,and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated,then testers can design the test cases according to the collected constraints for each path selected.
基金supported in part by the National Key R&D Program of China(2017YFB1001804)Shanghai Science and Technology Innovation Action Plan Project(16511100900)
文摘In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.
文摘In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.
文摘With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.
文摘This study proposes a new educational assessment framework to realize the organic integration of traditional assessment methods and digital technology.The model builds a three-dimensional assessment system by integrating dynamic data flow and educational big data.Considering the limitation of traditional assessment methods relying on periodic summary,a continuous dynamic feedback mechanism and a personalized learning tracking module are innovatively introduced.The technical program deeply integrates the educational theoretical system and intelligent algorithm technology to establish a panoramic assessment model in multiple dimensions such as academic development,class participation,and learning emotion.The system integrates classroom recording data,online assessment results,learning behavior logs,and other multi-source information to form a holographic portrait of learners'growth.In the research process,real teaching areas are selected to conduct technical verification.Experimental data show that the framework can effectively capture the dynamic characteristics of the learning process and provide real-time decision support for adapting teaching strategies.The technology optimization direction focuses on fine-tuning the granularity of data acquisition and explores the possibility of integrating new data inputs such as wearable devices.The practical application of this evaluation framework marks an important breakthrough in the direction of an intelligent and real-time education quality monitoring system.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
文摘This paper presents the methodology of process driven analysis to eliminate the redundant information conducted from homogeneous procedures in management system. Process flow is emphasized and events triggered by process based states are described.
基金supported by the National Key R&D Program of China(2023YFF0723300)National Natural Science Foundation of China(62475198,62075200,12374295)+8 种基金Fundamental Research Funds for the Central Universities(2042024kf0003,2042024kf1010,2042023kf0105)Hubei Provincial Natural Science Foundation of China(2023AFB133)Jiangsu Science and Technology Program(BK20221257)Shenzhen Science and Technology Program(JCYJ20220530140601003,JCYJ20230807090207014)Translational Medicine and Multidisciplinary Research Project of Zhongnan Hospital of Wuhan University(ZNJC202217,ZNJC202232)The Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University(JCRCYR-2022-006)Hubei Province Young Science and Technology Talent Morning Hight Lift Project(202319)The Fund of National Key Laboratory of Plasma Physics(6142A04230201)We gratefully acknowledge Serendipity Lab for facilitating collaboration opportunities.
文摘Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental monitoring.However,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell analysis.In this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image processing.Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and analyzed.The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples.This work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.
文摘Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.
