In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this me...In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this method the structure is subjected to gradually intensifying acceleration functions and its performance is evaluated based on the length of the time duration that can satisfy required performance criteria. For this purpose Dez arch dam is selected as case study, fluid-structure interaction is taken into account and F.E. model of the system is excited in three performance levels. ETA method gives an approximation of maximum response at the equivalent target time, resulted from analyzing the system based on natural records. Extracted results are displacement, velocity and acceleration of the crest at crown cantilever. Results show using of ETA method can reduce at least 50% in number of analyses and 70% in total time of analyses at the current case. Furthermore, it is found that although the results of the ETA are not exactly consistent with the results of time-history analyses using real ground motions, errors are reasonable and ETA can identify performance levels of the dam with acceptable accuracy.展开更多
This paper reviews the precursory phenomena of the 2011 Mw9 Tohoku earthquake in Japan that emerge solely when we analyze the seismicity data in a new time domain termed natural time. If we do not consider this analys...This paper reviews the precursory phenomena of the 2011 Mw9 Tohoku earthquake in Japan that emerge solely when we analyze the seismicity data in a new time domain termed natural time. If we do not consider this analysis, important precursory changes cannot be identified and hence are missed. Natural time analysis has the privilege that enables the introduction of an order parameter of seismicity. In this frame, we find that the fluctuations of this parameter exhibit an unprecedented characteristic change, i.e., an evident minimum, approximately two months before Tohoku earthquake, which strikingly is almost simultaneous with unique anomalous geomagnetic field variations recorded mainly on the z component. This is consistent with our finding that such a characteristic change in seismicity appears when a seismic electric signal （SES） activity of the VAN method （from the initials of Varotsos, Alexopoulos, Nomicos） initiates, and provides a direct confirmation of the physical interconnection between SES and seismicity.展开更多
A simple immune-based multi-objective optimizer(IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions(Bi-Trap, Bi-Plateau and Bi-Jump) is presented. Th...A simple immune-based multi-objective optimizer(IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions(Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer(GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation(CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics.展开更多
In present paper application of Endurance Time Analysis （ETA） method in nonlinear seismic assessment of steel concentrically braced frames is studied. In this method structure is subjected to a set of predesigned in...In present paper application of Endurance Time Analysis （ETA） method in nonlinear seismic assessment of steel concentrically braced frames is studied. In this method structure is subjected to a set of predesigned increasing acceleration functions and various responses of the structure are recorded during synthetic seismic excitation. The averages of maximum values for various responses in Nonlinear Time-History Analyses （NTHA） extracted from real ground motions are expected to be close to those obtained from ETA up to equivalent target time. For this purpose a set of 9, 11, 13 and 15 stories steel frames were designed and plastic hinge method was selected as source of nonlinearity in all cases. Responses were compared based on interstory drift ratio and story shear in two methods and results show satisfactory consistency between two methods. Finally, it was concluded that the ETA method can be used as alternative method in nonlinear seismic analysis of concentrically braced steel frames.展开更多
In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitori...In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.展开更多
Objectives:This study aimed to explore the characteristics of outpatient blood collection center visit fluctuation and nursing workforce allocation based on a time series model,and the application effect was evaluated...Objectives:This study aimed to explore the characteristics of outpatient blood collection center visit fluctuation and nursing workforce allocation based on a time series model,and the application effect was evaluated.Methods:To enhance the efficiency of phlebotomy at the hospital outpatient window and improve patient satisfaction,the First Affliated Hospital with Nanjing Medical University implemented a time series analysis model in 2024 to optimize nursing staff allocation.The management team was led by a head nurse of the outpatient blood collection department with extensive experience.It included one director of the nursing department,six senior clinical nurses,one informatics expert,and one nursing master's degree holder.Retrospective time-series data from the hospital's smart blood collection system(including hourly blood collection volumes and waiting times)were extracted between January 2020 and December 2023.Time series analysis was used to identify annual,seasonal,monthly,and hourly variation patterns in blood collection volumes.Seasonal decomposition and the Autoregressive Integrated Moving Average Model(ARIMA)were employed to forecast blood collection fluctuations for 2024 and facilitate dynamic scheduling.A comparison was conducted to evaluate differences in blood collection efficiency and patient satisfaction before(January-June 2023)and after(January-June 2024)implementing the dynamic scheduling model based on the time series analysis and forecasting.Results:Visit volumes showed periodicity and slow growth,peaking every second and third quarter of the year and daily at 8:00-9:00 a.m.and 2:00-3:00 p.m.The ARIMA model demonstrated a good fit(R2=0.692,mean absolute percentage error=8.28%).After adjusting the nursing staff allocation based on the fluctuation characteristics of the number of phlebotomy per hour in the time series analysis model,at the peak period of the blood collection window,at least three nurses,one mobile nurse and two volunteers were added.The number of phlebotomy per hour increased from 289.74±54.55 to 327.53±37.84 person-time(t=-10.041,P<0.01),waiting time decreased from 5.79±2.68 to 4.01±0.46 min(t=11.531,P<0.01),and satisfaction rose from 92.7%to 97.3%(χ^(2)=6.877,P<0.05).Conclusions:Based on the time series analysis method,it is helpful for nursing managers to accurately allocate human resources and optimize the efficiency of outpatient service resources by mining the special change rule of the outpatient blood collection window and predicting the future fluctuation trend.展开更多
Leveraging big data signal processing offers a pathway to the development of artificial intelligencedriven equipment.The analysis of fluid flow signals and the characterization of fluid flow behavior are of critical i...Leveraging big data signal processing offers a pathway to the development of artificial intelligencedriven equipment.The analysis of fluid flow signals and the characterization of fluid flow behavior are of critical in two-phase flow studies.Significant research efforts have focused on discerning flow regimes using various signal analysis methods.In this review,recent advances in time series signals analysis algorithms for stirred tank reactors have been summarized,and the detailed methodologies are categorized into the frequency domain methods,time-frequency domain methods,and state space methods.The strengths,limitations,and notable findings of each algorithm are highlighted.Additionally,the interrelationships between these methodologies have also been discussed,as well as the present progress achieved in various applications.Future research directions and challenges are also predicted to provide an overview of current research trends in data mining of time series for analyzing flow regimes and chaotic signals.This review offers a comprehensive summary for extracting and characterizing fluid flow behavior and serves as a theoretical reference for optimizing the characterization of chaotic signals in future research endeavors.展开更多
GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiologi...The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.展开更多
This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch si...This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.展开更多
Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including at...Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.展开更多
The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving ...The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National University, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.展开更多
In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh enviro...In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.展开更多
Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2....Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.展开更多
Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis i...Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.展开更多
In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. ...In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
The effects of the calorimetric buffer solutions were investigated while the two colorimetric reactions of AI-ferron complex and Fe-ferron complex occurred individually, and the effects of the testing wavelength and t...The effects of the calorimetric buffer solutions were investigated while the two colorimetric reactions of AI-ferron complex and Fe-ferron complex occurred individually, and the effects of the testing wavelength and the pH of the solutions were also investigated. A timed complexatian colorimetric analysis method of Al-Fe-ferron in view of the total concentration of {AI + Fe} was then established to determine the species distribution of polymeric Al-Fe. The testing wavelength was recommended at 362 net and the testing pH value was 5. With a comparison of the ratios of n(Al)/n(Fe), the standard adsorption curves of the polymeric Al-Fe solutions were derived from the experimental results. Furthermore, the solutions' composition were carious in both the molar n(Al)/n(Fe) ratios, i.e. 0/0, 5/5, 9/1 and 0/10, and the concentrations associated with the total ( Al + Fe which ranged from 10(-5) to 10(-4) mol/L..展开更多
Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used ...Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used to describe and predict the IDD related indicators,and spatial analysis was used to analyze the spatial distribution of salt iodine levels.Results In China,the median urinary iodine concentration increased in 1995–1997,then decreased to adequate levels,and are expected to remain appropriate in 2019–2022.The goiter rate continually decreased and is expected to be maintained at a low level.Since 2002,the coverage rates of iodized salt and the consumption rates of qualified iodized salt(the percentage of qualified iodized salt in all tested salt) increased and began to decline in 2012;they are expected to continue to decrease.Spatial epidemiological analysis indicated a positive spatial correlation in 2016–2018 and revealed feature regarding the spatial distribution of salt related indicators in coastal areas and areas near iodine-excess areas.Conclusions Iodine nutrition in China showed gradual improvements.However,a recent decline has been observed in some areas following changes in the iodized salt supply in China.In the future,more regulations regarding salt management should be issued to strengthen IDD control and prevention measures,and avoid the recurrence of IDD.展开更多
The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the ...The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave.In this research,a novel time domain approach combining dynamic finite element method and state-space model(SSM)is established for the refined analysis of floating bridges.The dynamic coupled effects induced by wave excitation load,radiation load and buffeting load are carefully simulated.High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain.The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons.The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory.The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions.The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.展开更多
文摘In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this method the structure is subjected to gradually intensifying acceleration functions and its performance is evaluated based on the length of the time duration that can satisfy required performance criteria. For this purpose Dez arch dam is selected as case study, fluid-structure interaction is taken into account and F.E. model of the system is excited in three performance levels. ETA method gives an approximation of maximum response at the equivalent target time, resulted from analyzing the system based on natural records. Extracted results are displacement, velocity and acceleration of the crest at crown cantilever. Results show using of ETA method can reduce at least 50% in number of analyses and 70% in total time of analyses at the current case. Furthermore, it is found that although the results of the ETA are not exactly consistent with the results of time-history analyses using real ground motions, errors are reasonable and ETA can identify performance levels of the dam with acceptable accuracy.
文摘This paper reviews the precursory phenomena of the 2011 Mw9 Tohoku earthquake in Japan that emerge solely when we analyze the seismicity data in a new time domain termed natural time. If we do not consider this analysis, important precursory changes cannot be identified and hence are missed. Natural time analysis has the privilege that enables the introduction of an order parameter of seismicity. In this frame, we find that the fluctuations of this parameter exhibit an unprecedented characteristic change, i.e., an evident minimum, approximately two months before Tohoku earthquake, which strikingly is almost simultaneous with unique anomalous geomagnetic field variations recorded mainly on the z component. This is consistent with our finding that such a characteristic change in seismicity appears when a seismic electric signal （SES） activity of the VAN method （from the initials of Varotsos, Alexopoulos, Nomicos） initiates, and provides a direct confirmation of the physical interconnection between SES and seismicity.
基金the National Natural Science Foundation of China(Nos.61703183,61773410,61375053)the Public Welfare Technology Research Plan of Zhejiang Province(No.LGG19F030010)
文摘A simple immune-based multi-objective optimizer(IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions(Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer(GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation(CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics.
文摘In present paper application of Endurance Time Analysis （ETA） method in nonlinear seismic assessment of steel concentrically braced frames is studied. In this method structure is subjected to a set of predesigned increasing acceleration functions and various responses of the structure are recorded during synthetic seismic excitation. The averages of maximum values for various responses in Nonlinear Time-History Analyses （NTHA） extracted from real ground motions are expected to be close to those obtained from ETA up to equivalent target time. For this purpose a set of 9, 11, 13 and 15 stories steel frames were designed and plastic hinge method was selected as source of nonlinearity in all cases. Responses were compared based on interstory drift ratio and story shear in two methods and results show satisfactory consistency between two methods. Finally, it was concluded that the ETA method can be used as alternative method in nonlinear seismic analysis of concentrically braced steel frames.
基金European Commission(CZ.10.03.01/00/22-003/0000048)Fundacao para a Ciencia e a Tecnologia(PTDC/EEI-EEE/0415/2021),CICECO(UIDB/50011/2020,UIDP/50011/2020,LA/P/0006/2020)+1 种基金VSB-Technical University of Ostrava(SP2025/039)FCT/MCTES(UI/BD/153066/2022)。
文摘In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.
基金funded by the Nursing project,“Clinical ability improvement project”in the First Affliated Hospital with Nanjing Medical University(JSPH-NC-2021-09).
文摘Objectives:This study aimed to explore the characteristics of outpatient blood collection center visit fluctuation and nursing workforce allocation based on a time series model,and the application effect was evaluated.Methods:To enhance the efficiency of phlebotomy at the hospital outpatient window and improve patient satisfaction,the First Affliated Hospital with Nanjing Medical University implemented a time series analysis model in 2024 to optimize nursing staff allocation.The management team was led by a head nurse of the outpatient blood collection department with extensive experience.It included one director of the nursing department,six senior clinical nurses,one informatics expert,and one nursing master's degree holder.Retrospective time-series data from the hospital's smart blood collection system(including hourly blood collection volumes and waiting times)were extracted between January 2020 and December 2023.Time series analysis was used to identify annual,seasonal,monthly,and hourly variation patterns in blood collection volumes.Seasonal decomposition and the Autoregressive Integrated Moving Average Model(ARIMA)were employed to forecast blood collection fluctuations for 2024 and facilitate dynamic scheduling.A comparison was conducted to evaluate differences in blood collection efficiency and patient satisfaction before(January-June 2023)and after(January-June 2024)implementing the dynamic scheduling model based on the time series analysis and forecasting.Results:Visit volumes showed periodicity and slow growth,peaking every second and third quarter of the year and daily at 8:00-9:00 a.m.and 2:00-3:00 p.m.The ARIMA model demonstrated a good fit(R2=0.692,mean absolute percentage error=8.28%).After adjusting the nursing staff allocation based on the fluctuation characteristics of the number of phlebotomy per hour in the time series analysis model,at the peak period of the blood collection window,at least three nurses,one mobile nurse and two volunteers were added.The number of phlebotomy per hour increased from 289.74±54.55 to 327.53±37.84 person-time(t=-10.041,P<0.01),waiting time decreased from 5.79±2.68 to 4.01±0.46 min(t=11.531,P<0.01),and satisfaction rose from 92.7%to 97.3%(χ^(2)=6.877,P<0.05).Conclusions:Based on the time series analysis method,it is helpful for nursing managers to accurately allocate human resources and optimize the efficiency of outpatient service resources by mining the special change rule of the outpatient blood collection window and predicting the future fluctuation trend.
基金the National Natural Science Foundation of China(22078030)the National Key Research and Development Project(2019YFC1905802,2022YFB3504305)+1 种基金the Joint Funds of the National Natural Science Foundation of China(U1802255,CSTB2022NSCQ-LZX0014)the Key Project of Independent Research Project of State Key Laboratory of Coal Mine Disaster Dynamics and Control(2011DA105287-zd201902).
文摘Leveraging big data signal processing offers a pathway to the development of artificial intelligencedriven equipment.The analysis of fluid flow signals and the characterization of fluid flow behavior are of critical in two-phase flow studies.Significant research efforts have focused on discerning flow regimes using various signal analysis methods.In this review,recent advances in time series signals analysis algorithms for stirred tank reactors have been summarized,and the detailed methodologies are categorized into the frequency domain methods,time-frequency domain methods,and state space methods.The strengths,limitations,and notable findings of each algorithm are highlighted.Additionally,the interrelationships between these methodologies have also been discussed,as well as the present progress achieved in various applications.Future research directions and challenges are also predicted to provide an overview of current research trends in data mining of time series for analyzing flow regimes and chaotic signals.This review offers a comprehensive summary for extracting and characterizing fluid flow behavior and serves as a theoretical reference for optimizing the characterization of chaotic signals in future research endeavors.
基金supported by the National Natural Science Foundation of China(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF1204803)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.NJ2024029)the National Natural Science Foundation of China(Grant Nos.81701346 and 62201265).
文摘The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.
基金supported by the National Key R&D Program(No.2022YFA1602201)。
文摘This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.
基金supported by the Basic Science Center Project of the National Natural Science Foundation of China(42388102)the National Natural Science Foundation of China(42174030)+2 种基金the Special Fund of Hubei Luojia Laboratory(220100020)the Major Science and Technology Program for Hubei Province(2022AAA002)the Fundamental Research Funds for the Central Universities of China(2042022dx0001 and 2042023kfyq01)。
文摘Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.
文摘The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National University, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100)the National Natural Science Foundation of China(62332017,U22A2022)
文摘In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.
文摘Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2006AA04Z416)the National Natural Science Foundation of China (No50538020)
文摘Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.
文摘In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
基金TheNationalNaturalScienceFoundationofChina (No .2 96 770 0 4)
文摘The effects of the calorimetric buffer solutions were investigated while the two colorimetric reactions of AI-ferron complex and Fe-ferron complex occurred individually, and the effects of the testing wavelength and the pH of the solutions were also investigated. A timed complexatian colorimetric analysis method of Al-Fe-ferron in view of the total concentration of {AI + Fe} was then established to determine the species distribution of polymeric Al-Fe. The testing wavelength was recommended at 362 net and the testing pH value was 5. With a comparison of the ratios of n(Al)/n(Fe), the standard adsorption curves of the polymeric Al-Fe solutions were derived from the experimental results. Furthermore, the solutions' composition were carious in both the molar n(Al)/n(Fe) ratios, i.e. 0/0, 5/5, 9/1 and 0/10, and the concentrations associated with the total ( Al + Fe which ranged from 10(-5) to 10(-4) mol/L..
基金partly supported by the National Natural Science Foundation of China [81773370 and 82173638]the Natural Science Foundation of Heilongjiang Province [TD2019H001]
文摘Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used to describe and predict the IDD related indicators,and spatial analysis was used to analyze the spatial distribution of salt iodine levels.Results In China,the median urinary iodine concentration increased in 1995–1997,then decreased to adequate levels,and are expected to remain appropriate in 2019–2022.The goiter rate continually decreased and is expected to be maintained at a low level.Since 2002,the coverage rates of iodized salt and the consumption rates of qualified iodized salt(the percentage of qualified iodized salt in all tested salt) increased and began to decline in 2012;they are expected to continue to decrease.Spatial epidemiological analysis indicated a positive spatial correlation in 2016–2018 and revealed feature regarding the spatial distribution of salt related indicators in coastal areas and areas near iodine-excess areas.Conclusions Iodine nutrition in China showed gradual improvements.However,a recent decline has been observed in some areas following changes in the iodized salt supply in China.In the future,more regulations regarding salt management should be issued to strengthen IDD control and prevention measures,and avoid the recurrence of IDD.
基金financially supported by the Program of Science and Technology Innovation Action Plan,Shanghai,China(Grant No.20200741600).
文摘The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave.In this research,a novel time domain approach combining dynamic finite element method and state-space model(SSM)is established for the refined analysis of floating bridges.The dynamic coupled effects induced by wave excitation load,radiation load and buffeting load are carefully simulated.High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain.The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons.The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory.The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions.The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.
