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.

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.

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.

Study on Lag time Model of Fire Plume under a Sloped Ceiling

It is known that there is a lag time for smoke plume induced by fires transporting from a fire origin to the location of interest underneath an unconfined and flat ceiling.This lag behavior of smoke plume also exists for a fire under a sloped ceiling,and is fundamental to estimate the activation time of a fire detector or other fire extinguishing system.This study focuses on the lag time of smoke plume under a sloped ceiling.Based on the weak-plume theory at early-fire phase and previous studies concerning the fire plume characteristics under a sloped ceiling,a calculation method on lag time of fire plume transporting is presented in theory.Meanwhile,two dimensionless equations predicting the lag time of fire plume for steady fire and unsteady fire are proposed respectively.Furthermore,the critical time calculation equation is also proposed to determine the applicability of quasi-steady assumption for a time-dependent fire.

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.

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.

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.

SCGM(1,m)_c model with time-lag and its application in deformation analysis

Originated in the early 1990s, SCGM(1, m ) c model has enjoyed widespread application in the fields of urban planning, society economy prediction and modal control in recent years. However, none of these applications have taken account of time lag effects in the modeling process. Aiming at overcoming the defect, the authors introduced time lag items into SCGM(1, m ) c model and developed a SCGM(1, m ) c model with time lag, then discusses in detail some principal problems in the model, such as parameters estimation, model verifying, model prediction, etc. The model was used on a real slope monitoring project and compared with the conventional SCGM(1, m ) c model. The results show an improvement of average models precision from 1.321 to 0.238 and total average of relative prediction errors from 12.41% to 7.98% when the modeling data length ranges from 29 to 48 in the slope monitoring case.

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.

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.

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.

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.

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.

Estimation Accuracy for Reciprocal Analysis of Sensible and Latent Heat Flux Focusing on Radiometric Temperature and Lag-Time

There is no word to describe the importance of evapotranspiration research for water resource utilization. We have already proposed a new method for the reciprocal estimation of the sensible (H) and latent heat fluxes (lE) by using a single height temperature (Tz) and humidity (rehz) based on the observed net radiation (Rn) and ground heat flux (G). This research is more advanced than the previous research because it uses a Ts observed by a radiometer and identifies the observed data satisfactorily heat balance relationship in every hour at nine sites. First, we confirmed that the estimated H and lE are very close reproductions of the identified H and lE. Second, by analyzing the relative ground surface temperature (Ts - T0) [Ts: ground surface temperature, T0: observed temperature near the soil surface], the hourly and seasonal changes of (Ts - T0) were clarified, resulting in a marked difference in the (Ts - T0) from previous research in arid and semi-arid regions. Next, the estimation accuracy of H, lE and rehs (the humidity of the soil surface) was determined by observing the slope of the estimated and observed relationship, resulting in the reasonable accuracy (0.85 - 1.15 times) of rehs at seven of the nine sites. Furthermore, the annual evapotranspiration was estimated by comparing the identified and estimated H and lE, resulting in a reasonable accuracy (0.85 - 1.15) at five of the nine sites in the case of the application of constraint b. Moreover, the effect of the lag-time between the net radiation Rn and both Tz and Ts for the estimation accuracy on H and lE was tested, and no remarkable difference was found because the effect was included already in the original data. The above results will contribute greatly to the advance of water resource planning and hydrometeorology. This research was conducted using FLUXNET data.

SOME RESULTSON LAG INCREMENTSOF PRINCIPAL VALUE OF BROWNIAN LOCAL TIME

Let W be a standard Brownian motion,and define Y(t) =∫t 0 ds W(s) as Cauchy' s principal value related to the local time of W.We study some limitresults on lag increments of Y(t) and obtain various results all of which are related to earlier work by Hanson and Russo in 1 983

A Nonlinear Time－lag Differential Equation Model for Predicting Monthly Precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.

四川地区精细化降水预报融合订正试验及检验

精细化定量降水网格预报是天气预报业务的重点和难点,基于西南区域智能数值网格模式预报系统(Southwest China WRF-based Intelligent Numeric Grid forecast System,SWC-WINGS)1 km×1 km分辨率的小时降水预报,利用时间滞后和概率匹配方法开展融合订正试验,再利用中国气象局多源融合降水系统(CMA Multi-source Precipitation Analysis System,CMPAS)三源融合降水实况格点数据,对2022年7—8月四川地区的小时降水预报融合订正结果进行检验,并在四川盆地西部一次短时强降水天气过程中进行应用,结果表明:(1)时间滞后集合降水预报相较于模式降水预报,存在小量级预报过度,大量级预报过于保守的问题;(2)时间滞后结合概率匹配的降水预报融合订正方法有效提升了各量级降水预报的TS评分,尤其1~2 h预报时效提升显著,小时雨量超过0.1 mm、5 mm、10 mm和20 mm量级的TS评分平均提升率分别为7.2%、17.2%、28.3%和36.3%;(3)一次短时强降水天气过程的应用结果表明,时间滞后结合概率匹配的融合订正方法对模式小时降水预报有较好的改进效果,尤其对大量级降水预报有较强的订正能力。

从同步数据分析乔木树干液流与气象因子的关系

[目的]树干液流是研究植物蒸腾耗水的重要生理指标,树种和气象因子均是其关键影响因素。本文旨在消除树种因素限制,探讨树干液流与气象因子之间的关系。[方法]以哈尼梯田水源区3种林分中10种乔木树种为研究对象,采用Granier热扩散探针技术测定其树干液流速率,将其同步数据与气象因子进行关联分析。[结果]从次生落叶阔叶林和原生常绿阔叶林的7779条乔木树干液流速率数据中发现7条异常数据,且这些异常数据在各个树种中都表现出同步一致性;进一步关联分析表明,太阳辐射的突然增加是产生乔木树干液流数据同步上升的主要原因,而降雨则是产生树干液流数据同步下降的主要原因,两者的变化可导致其他气象因子发生变化,从而影响树干液流速率;树干液流与气象因子存在一定的时滞效应,在发生同步异常数据的气候条件下,树干液流滞后太阳辐射0~30 min,提前于饱和水汽压差、气温和湿度0~60 min,滞后或提前时间远小于正常气候条件下各树种的时滞时间。[结论]乔木树干液流受到太阳辐射和降雨等气象因子的综合调节,其中太阳辐射的影响最为显著。研究结果可为从生理生态角度探究乔木的蒸腾作用与气象因子的关系提供科学依据。