Cross-Correlation between Global Temperature and Atmospheric CO2 with a Temperature-Leading Time Lag

The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

Decay rates and time lags of heat conduction in building construction under field conditions

The field measurements of decay rates and time lags of heat conduction in a building construction taken in Nanjing during the summer of 2001 are presented.The decay rates and time lags are calculated according to the frequency responses of the heat absorbed by the room＇s internal surfaces,inside surface temperature,indoor air temperature and outdoor synthetic temperature.The measured results match very well with the theoretical results of the zeroth and the first order values of the decay rates and time lags of heat conduction in the building construction,but the difference between the measured values and the theoretical values for the second order is too great to be accepted.It is therefore difficult to accurately test the second order value.However,it is still advisable to complete the analysis using the zeroth-and the first-orders values of the decay rates and time lags of heat conduction in building construction under field conditions,because in these cases the decay rates of heat conduction reach twenty which meets the requirements of engineering plans.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.

Impact of time lags on diurnal estimates of canopy transpiration and canopy conductance from sap-flow measurements of Populus cathayana in the Qinghai–Tibetan Plateau

Recently, canopy transpiration （Ec） has been often estimated by xylem sap-flow measurements. However, there is a significant time lag between sap flow measured at the base of the stem and canopy transpiration due to the capacitive exchange between the transpiration stream and stem water storage. Significant errors will be introduced in canopy conductance （gc） and canopy transpiration estimation if the time lag is neglected. In this study, a cross-correlation analysis was used to quantify the time lag, and the sap flowbased transpiration was measured to pararneterize Jarvistype models of gc and thus to simulate Ec of Populus cathayana using the Penman-Monteith equation. The results indicate that solar radiation （Rs） and vapor pressure deficit （VPD） are not fully coincident with sap flow and have an obvious lag effect; the sap flow lags behind Rs and precedes VPD, and there is a 1-h time shift between Eo and sap flow in the 30-min interval data set. A parameterized Jarvis-type gc model is suitable to predict P. cathayana transpiration and explains more than 80% of the variation observed in go, and the relative error was less than 25%, which shows a preferable simulation effect. The root mean square error （RMSEs） between the predicted and measured Ec were 1.91×10^-3 （with the time lag） and 3.12×10^-3cm h^-1 （without the time lag）. More importantly, Ec simulation precision that incorporates time lag is improved by 6% compared to the results without the time lag, with the mean relative error （MRE） of only 8.32% and the mean absolute error （MAE） of 1.48 × 10^-3 cm h^-1.

Effects of time lag and stress loading rate on permeability in low permeability reservoirs

In order to study the effect of time lag and stress loading rates on rock deformation,the conventional stepped stress loading mode was changed into a continuous mode to investigate the effect of effective pressure on permeability and porosity.The time lag effect of rock deformation illustrating the relationship between changes in permeability and steady time was studied.Permeability reduction ratios were measured under different stress loading rates which were achieved by different pump rate settings.The results show that permeability and porosity gradually decrease with increases in effective pressure.Permeability at high effective pressure attains stability quickly.Steady times at low effective pressure are very long.Reduction in permeability at lower stress loading rates is small,while,in contrast,it is large at high stress loading rates.

Time lag characteristics of sap flow in seed-maize and their implications for modeling transpiration in an arid region of Northwest China

Plant capacity for water storage leads to time lags between basal stem sap flow and transpiration in various woody plants. Internal water storage depends on the sizes of woody plants. However, the changes and its influencing factors in time lags of basal stem flow during the development of herbaceous plants including crops remain unclear. A field experiment was conducted in an arid region of Northwest China to examine the time lag characteristics of sap flow in seed-maize and to calibrate the transpiration modeling. Cross-correlation analysis was used to estimate the time lags between stem sap flow and meteorological driving factors including solar radiation（R_s） and vapor pressure deficit of the air（VPD_（air））. Results indicate that the changes in seed-maize stem sap flow consistently lagged behind the changes in R_s and preceded the changes in VPD_（air） both on hourly and daily scales, suggesting that light-mediated stomatal closures drove sap flow responses. The time lag in the maize＇s sap flow differed significantly during different growth stages and the difference was potentially due to developmental changes in capacitance tissue and/or xylem during ontogenesis. The time lags between stem sap flow and R_s in both female plants and male plants corresponded to plant use of stored water and were independent of total plant water use. Time lags of sap flow were always longer in male plants than in female plants. Theoretically, dry soil may decrease the speed by which sap flow adjusts ahead of shifts in VPD_（air） in comparison with wet soil and also increase the speed by which sap flow adjusts to R_s. However, sap flow lags that were associated with R_s before irrigation and after irrigation in female plants did not shift. Time series analysis method provided better results for simulating seed-maize sap flow with advantages of allowing for fewer variables to be included. This approach would be helpful in improving the accuracy of estimation for canopy transpiration and conductance using meteorological measurements.

ON THE STABILITY OF DIFFERENTIAL SYSTEMS WITH TIME LAG

In this paper the inequality of Lemma 1 of [1] is extended. By means of our inequality and the method of Lyapunov function we study the stability of two kinds of large scale differential systems with time lag and the stability of a higher-order differential equation with time lag. The sufficient conditions for the stability (S. ), the asymptotic stability (A. S. ), the uniformly asymptotic stability (U. A. S. ) and the exponential asymptotic stability (E. A. S. ) of the zero solutions of the systems are obtained respectively.

Boundary Control Problem of Infinite Order Distributed Hyperbolic Systems Involving Time Lags

Various optimal boundary control problems for linear infinite order distributed hyperbolic systems involving constant time lags are considered. Constraints on controls are imposed. Necessary and sufficient optimality conditions for the Neumann problem with the quadratic performance functional are derived.

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

Grey interpolation approach for small time-lag samples based on grey dynamic relation analysis

Given a non-equidistant sequence or an equidistant series with one or more outliers, a grey interpolation approach considering the time lags is established for producing the missing data or correcting the abnormal values. To accomplish this, a new grey incidence model, called the grey dynamic incidence model GDIM(t), is constructed for determining whether the factors are effective to the known factor and what the time lag is between a useful factor and the specified sequence. Based on the results of the GDIM(t) model, two programming problems are designed to obtain the upper and lower bounds of the unknown or abnormal values which are regarded as grey numbers. The solutions based on the particle swarm optimization(PSO) for the nonlinear programming problems are given. To explain how it can be used in practice, this new grey interpolation approach is applied to correct an abnormal value in the sequence of an agriculture environment problem.

Time Lag Effect of Supply Chain Management on Firm Performance

The objective of this paper is to identify time lag effect in the relationship between supply chain management and financial performance.We hypothesize that firms with higher supply chain management capability are associated with higher financial performance and the impact would last longer.In addition,we introduce two contextual variables,ownership and market orientation,to detect the moderating effect of ownership structure and organizational culture,respectively.We use firm-level empirical data from 1,131 public Chinese manufacturing firms for the 2010–2019 period to investigate the effect and the result shows that there is a two-year time lag effect in the relationship,which means the positive impact of current supply chain management would last for three years.Furthermore,we find evidence that supply chain management in non-state-owned firms is more efficient and its positive financial impact would last longer than that in state-owned enterprises.Finally,we indicate that market-orientation can’t moderate the relationship between supply chain management and financial performance.

Study on the spatial differences and its time lag effect on climatic factors of the vegetation in the Longitudinal Range-Gorge Region

这篇文章包括温度和降水分析 NDVI 和二个气象学的数据的关联，并且探查回答植被的敏感到温度和降水变化的空间模式。由构造 NDVIr 索引，在在纵的范围峡区域(LRGR ) 的植被盖子和陆地使用之间的关系被揭示。结论如下：(1 ) 时间落后那植被回答到温度和降水变化的效果被证明。不同区域有相应于最大的关联系数的不同延期时间。根据降水和温度联合的反应时间，也就是，三种温度降水形式被定义在一样的时间的降水和温度，在温度以前的降水和在降水以前的温度。(2 ) 敏感的空间分发，哪个对温度的植被回答，被几个高、低的散布关联中心描绘。与温度，二个感觉迟钝的区域和二条敏感的带不同被形成介绍植被的反应给降水。(3 ) NDVIr 被构造，它能完美地在 LRGR 反映在气象学的因素和陆地使用状况之间的相应关系。

Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.

Adaptive lag synchronization of uncertain dynamical systems with time delays via simple transmission lag feedback

In this paper we present an adaptive scheme to achieve lag synchronization for uncertain dynamical systems with time delays and unknown parameters. In contrast to the nonlinear feedback scheme reported in the previous literature, the proposed controller is a linear one which only involves simple feedback information from the drive system with signal popagation lags. Besides, the unknown parameters can also be identified via the proposed updating laws in spite of the existence of model delays and transmission lags, as long as the linear independence condition between the related function elements is satisfied. Two examples, i.e., the Mackey-Glass model with single delay and the Lorenz system with multiple delays, are employed to show the effectiveness of this approach. Some robustness issues are also discussed, which shows that the proposed scheme is quite robust in switching and noisy environment.

Impact of reduced frequency on the time lag in pressure distribution over a supercritical airfoil in a pitch-pause-return motion

Effects of reduced frequency, stop angle, and pause duration have been studied on a thin supercritical airfoil undergoing a pitch-pause-return motion, which is one of the classic maneuvers introduced by the AIAA Fluid Dynamics Technical Committee. Experiments were conducted in a low-speed wind tunnel at both a constant mean angle of attack and an oscillation amplitude with a reduced frequency ranging from 0.01 to 0.12. The desired stop angles of the airfoil were set to occur during the upstroke motion. The unsteady pressure distribution on the airfoil was measured for below, near, and beyond static stall conditions. Results showed that the reduced frequency and stop angle were the dominant contributors to the time lag in the flowfield. For stop angles in both belowand post-stall regions, the time for the flowfield to reach its steady state conditions, known as the time lag, decreased as the reduced frequency was increased. However, in the static-stall region and for a certain value of reduced frequency, a resonance phenomenon was observed, and a minimum time lag was achieved. The pressure distribution in this condition was shown to be highly influenced by this phenomenon.

The time lag spectrum of Cir X-1 on its normal branch

In the radiation-hydrodynamics model,the time lag and fractional root-mean-squared(rms) amplitude as a function of emitted photon energy should show a minimum on the normal branch oscillations.This has been seen in the Z-sources Cyg X-2 and GX 5-1.Here,using the observations from the Rossi X-ray Timing Explorer(RXTE),we study the energy dependence of normal branch QPOs in the peculiar Z-source Cir X-1.We discuss the results in the context of radiation-hydrodynamics and the Comptonization model.

Lag time,high-risk histopathological features,metastasis,and survival interrelation in retinoblastoma:a perspective from lower-middle income country

AIM:To investigate the impact of lag time to metastasis and survival rates among patients with retinoblastoma.METHODS:This retrospective study was conducted with 52 patients from the Department of Ophthalmology and the Department of Pediatrics of Dr.Sardjito General Hospital,between 1^(st) January 2014 and 31^(st) December 2020.Lag time was defined as the time delay between the first sign of retinoblastoma to the diagnosis of retinoblastoma.The subjects with lag time>one year were included in the case group,while the subjects with lag time<one year were included in the control group.RESULTS:The lag time was significantly correlated with American Joint Committee on Cancer and Intraocular Classification of Retinoblastoma staging of retinoblastoma(P=0.005 and P=0.006,respectively).The lag time was also significantly correlated with both metastasis event[odds ratio(OR):5.06,95%Cl:1.56-16.44,P=0.006]and mortality(OR:4.54,95%Cl:1.37-15.07,P=0.011).The follow-up was continued for 32 subjects for 3y after initial diagnoses.Survival analysis revealed a significant difference among these two groups(P=0.021).Furthermore,lag time was significantly correlated with survival of retinoblastoma(r=-0.53,P=0.046).CONCLUSION:The study highlights the importance of lag time between the onset of first symptoms and the time of retinoblastoma diagnosis which significantly contribute to metastasis and mortality of patients with retinoblastoma.Examinations for the early detection of retinoblastoma should be performed for individuals at-risk to minimize lag time and improve the outcomes.

Lag times in toe-to-heel air injection(THAI)operations explain underlying heavy oil production mechanisms

From a time value of revenue point of view,it is preferred that the time between reservoir stimulation and oil production response is small.Heavy oil combustion processes have a lag time between air injection and liquid production,but the common practice in production data analysis uses simultaneous injection and production data when seeking a relationship between them.In this research,the time scales of production for the Kerrobert toe-to-heel air injection(THAI)heavy oil project in Saskatchewan,Canada,is analyzed by using cross correlation analysis,i.e.time delay analysis between air injection and oil production.The results reveal two time scales with respect to production response with two distinctive recovery mechanisms:(1)a short time scale response(nearly instantaneous)where oil production peaks right after air injection(directly after opening production well)reflecting cold heavy oil production mechanisms,and(2)a longer time scale(of order of 100-300 days)response where peak production occurs associated with the collective phenomena of air injection,heat generating reactions,heat transfer,and finally,heated mobilized heavy oil drainage to the production well.This understanding of the two time scales and associated production mechanisms provides a basis for improving the performance of THAI.