Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application

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.

Statistical static timing analysis for circuit aging prediction

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit reliability.BTI results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic circuits.The amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design time.In this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI degradation.It consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability distributions.This method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

TIMING SLACK OPTIMIZATION APPROACH USING FPGA HYBRID ROUTING STRATEGY OF RIP-UP-RETRY AND PATHFINDER

To improve the path slack of Field Programmable Gate Array(FPGA), this paper proposes a timing slack optimization approach which utilizes the hybrid routing strategy of rip-up-retry and pathfinder. Firstly, effect of process variations on path slack is analyzed, and by constructing a collocation table of delay model that takes into account the multi-corner process, the complex statistical static timing analysis is successfully translated into a simple classical static timing analysis. Then, based on the hybrid routing strategy of rip-up-retry and pathfinder, by adjusting the critical path which detours a long distance, the critical path delay is reduced and the path slack is optimized. Experimental results show that, using the hybrid routing strategy, the number of paths with negative slack can be optimized(reduced) by 85.8% on average compared with the Versatile Place and Route(VPR) timing-driven routing algorithm, while the run-time is only increased by 15.02% on average.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Non-linear buffeting response analysis of long-span suspension bridges with central buckle

The rigid central buckle employed in the Runyang Suspension Bridge (RSB) was the first time it was used in a suspension bridge in China. By using a spectral representation method and FFT technique combined with measured data,a 3D fluctuating wind field considering the tower wind effect is simulated. A novel FE model for buffeting analysis is then presented,in which a specific user-defined Matrix27 element in ANSYS is employed to simulate the aeroelastic forces and its stiffness or damping matrices are parameterized by wind velocity and vibration frequency. A nonlinear time history analysis is carried out to study the influence of the rigid central buckle on the wind-induced buffeting response of a long-span suspension bridge. The results can be used as a reference for wind resistance design of long-span suspension bridges with a rigid central buckle in the future.

Lateral load pattern in pushover analysis

The seismic capacity curves of three types of buildings including frame,frame-shear wall and shear wall ob- tained by pushover analysis under different lateral load patterns are compared with those from nonlinear time history analy- sis.Based on the numerical results obtained a two-phase load pattern:an inverted triangle(first mode)load pattern until the base shear force reaches β times its maximum value,V_(max)followed by a(x/H)~α form,here β and α being some coeffi- cients depending on the type of the structures considered,is proposed in the paper,which can provide excellent approxima- tion of the seismic capacity curve for low-to-mid-rise shear type buildings.Furthermore,it is shown both the two-phase load pattern proposed and the invariant uniform pattern can be used for low-to-mid-rise shear-bending type and low-rise bending type of buildings.No suitable load patterns have been found for high-rise buildings.

Species distribution of polymeric aluminium ferrum——timed complexation colorimetric analysis method of Al-Fe-Ferron

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..

Nonlinear earthquake response analysis and energy calculation for seismic slit shear wall structures

Based on the concept of structural passive control,a new type of slit shear wall,with improved seismic performance when compared to an ordinary solid shear wall,was proposed by the authors in 1996.The idea has been verified by a series of pseudo-static and dynamic tests.In this paper a macro numerical model is developed for the wall element and the energy dissipation device.Then,nonlinear time history analysis is carried out for a 10-story slit shear wall model tested on a shaking table.Furthermore,the seismic input energy and the individual energy dissipated by the components are calculated by a method based on Newmark-β assumptions for this shear wall model,and the advantages of this shear wall are further demonstrated by the calculation results from the viewpoint of energy.Finally,according to the seismic damage criterion on the basis of plastic accumulative energy and maximum response,the optimal analysis is carried out to select design parameters for the energy dissipation device.

NYFR output pulse radar signal TOA analysis using extended Fourier transform and its TOA estimation

Nyquist folding receiver (NYFR) is a typical wideband analog-to-information architecture. Focusing on the non-cooperative receiving, the pulse radar signal intercepted by the NYFR in time domain is analyzed. The NYFR outputs under different input conditions are investigated based on the extended Fourier transform (EFT) and the sampling theorem. Combining with the characteristic of the NYFR output in time domain, a new time of arrival (TOA) estimation method based on the energy envelope and the wavelet transform is proposed. The proposed estimation method can be adapted for the non-cooperative situation. It has no requirement for prior information to determine the threshold and is not necessary to transform the signal into baseband. Simulation results prove the correctness of the NYFR output expressions and show the efficacy of the proposed estimation method. ? 2017 Beijing Institute of Aerospace Information.

Application of strength reduction method to dynamic anti-sliding stability analysis of high gravity dam with complex dam foundation

Considering that there are some limitations in analyzing the anti-sliding seismic stability of dam-foundation systems with the traditional pseudo-static method and response spectrum method, the dynamic strength reduction method was used to study the deep anti-sliding stability of a high gravity dam with a complex dam foundation in response to strong earthquake-induced ground action. Based on static anti-sliding stability analysis of the dam foundation undertaken by decreasing the shear strength parameters of the rock mass in equal proportion, the seismic time history analysis was carried out. The proposed instability criterion for the dynamic strength reduction method was that the peak values of dynamic displacements and plastic strain energy change suddenly with the increase of the strength reduction factor. The elasto-plastic behavior of the dam foundation was idealized using the Drucker-Prager yield criterion based on the associated flow rule assumption. The result of elasto-plastic time history analysis of an overflow dam monolith based on the dynamic strength reduction method was compared with that of the dynamic linear elastic analysis, and the reliability of elasto-plastic time history analysis was confirmed. The results also show that the safety factors of the dam-foundation system in the static and dynamic cases are 3.25 and 3.0, respectively, and that the F2 fault has a significant influence on the anti-sliding stability of the high gravity dam. It is also concluded that the proposed instability criterion for the dynamic strength reduction method is feasible.

Relationships of exponents in multifractal detrended fluctuation analysis and conventional multifractal analysis

Multifractal detrended fluctuation analysis （MF-DFA） is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis （DFA）, which was developed for detecting the long-range correlation and the fractal properties in stationary and non-stationary time series. Although MF-DFA has become a widely used method, some relationships among the exponents established in the original paper seem to be incorrect under the general situation. In this paper, we theoretically and experimentally demonstrate the invalidity of the expression r（q） = qh（q） - 1 stipulating the relationship between the multifractal exponent T（q） and the generalized Hurst exponent h（q）. As a replacement, a general relationship is established on the basis of the universal multifractal formalism for the stationary series as .t-（q） = qh（q） - qH - 1, where H is the nonconservation parameter in the universal multifractal formalism. The singular spectra, a and f（a）, are also derived according to this new relationship.

Hydrodynamic characteristics of a typical karst spring system based on time series analysis in northern China

In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern China.Results show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,respectively.The regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu Spring.The auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological year.The delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological year.In addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are small.All these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.

Analysis of the common model error on velocity field under Colored noise model by GPS and InSAR: A case study in the Nepal and everest region

The accuracy of the velocity field will be affected by the noise model and common mode errors through GPS time series analysis.In order to analyze the influence of these two factors on the accuracy of the velocity field,two kinds of data are used,including the three-year observation from 20 permanent GPS stations with high spatial correlation in the Everest,which is about 650 km from north to south and 1068 km from east to west,and three-year 80 ascending images and 141 descending images from sentinel-1A,which are processed by GAMIT/GLOBK software and Small Baseline Subset-Interferometric Synthetic Aperture Radar method(SBAS-InSAR),respectively.The vertical deformation rate is solved by time series analysis through a self-made adaptive algorithm.In the analysis,the linear change rate,period,half period coefficient,and residual sequence of all stations are solved by using James L.Davis periodic model.The noise type of residual sequence is analyzed by the power spectrum model.The spatio-temporal correlated noise,Common Mode Error(CME),is extracted by the Principal Component Analysis(PCA)and Karhunen-Loeve(KLE)methods.The results show that noises can be best described by“flicker noise+white noise”model.After the removal of CME,the R^(2) estimates of all stations are above 0.8,with RMS value of velocity field decreasing from 1.428 mm/yr to 1.062 mm/yr and 1.063 mm/yr to 0.815 mm/yr,in N and E directions,respectively,indicating that the influence of CME can't be ignored in the extraction of the high-precision velocity field in the Nepal and Everest region.

Emerging frontier technologies for food safety analysis and risk assessment

Access to security and safe food is a basic human necessity and essential for a sustainable world. To perform hi-end rood safety analysis and risk assessment with state of the art technologies is of utmost importance thereof. With applications as exemplified by microfiuidic immunoassay, aptasensor, direct analysis in real time, high resolution mass spectrometry, benchmark dose and chemical specific adjustment factor, this review presents frontier food safety analysis and risk assessment technologies, from which both food quality and public health will benefit undoubtedly in a foreseeable future.

NUMERICAL ANALYSIS AND EXPERI-MENT OF UNSTEADY THERMAL FIELD OF ROTOR PLATE FOR EDDY CURRENT RETARDER

The physical model based on heat transfer theory and virtual boundary method for analyzing unsteady thermal field of rotor plate for eddy current retarder used in automobile is established and boundary conditions are also defined. The finite element governing equation is derived by Galerkin method. The time differential item is discrete based on Galerkin format that is stable at any condition. And a new style of varying time step method is used in iteration process. The thermal field on the rotor plate at the radial and axle directions is analyzed and varying temperature at appointed points on two side-surfaces is measured. The testing and analytical data are uniform approximately. Finite element method can be used for estimating thermal field of the rotor plate at initial design stage of eddy current retarder.

Seismic elastic-plastic time history analysis and reliability study of quayside container crane

Quayside container crane is a kind of huge dimension steel structure,which is the major equipment used for handling container at modern ports.With the aim to validate the safety and reliability of the crane under seismic loads,besides conventional analysis,elastic-plastic time history analysis under rare seismic intensity is carried out.An ideal finite element（FEM） elastic-plastic mechanical model of the quayside container crane is presented by using ANSYS codes.Furthermore,according to elastic-plastic time history analysis theory,deformation,stress and damage pattern of the structure under rare seismic intensity are investigated.Based on the above analysis,the established reliability model according to the reliability theory,together with seismic reliability analysis based on Monte-Carlo simulation is applied to practical analysis.The results show that the overall structure of the quayside container crane is generally unstable under rare seismic intensity,and the structure needs to be reinforced.