Prediction Precision Of Model Research Articles

Flotation Height Prediction under Stable and Vibration States in Air Cushion Furnace Based on Hard Division Method

Air cushion furnace is indispensable equipment for the production of high quality strip, and it is significant to national economy. The flotation height is a key factor to the quality and efficiency of the product. However, the current prediction models can merely predict the flotation height of strip in air cushion furnace at single working state. The precision of prediction model is inaccurate at the circumstance of low flotation height. To solve the above problem, firstly, this paper proposes a framework which can predict the flotation height of strip under both stable and vibration states. The framework is composed of the hard division model and prediction model. Secondly, a hard division method is proposed based on clustering which combines stacked denoising autoencoder and floating process knowledge. Thirdly, a parallel hybrid flotation height prediction model is proposed, which can provide desirable prediction results at the circumstance of low flotation height. Finally, the LSSVR model is used to predict the maximum and minimum flotation height of strip at vibration state. The experimental results show that the framework can accurately divide the stable and vibration states of the strip and can accurately predict the flotation height of the strip under the stable and vibration states. The research contents of this paper lay an important theoretical foundation for the precise process control in air cushion furnace.

Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models.

Combinations of pharmacological treatments are increasingly being investigated for potentially higher clinical benefit, especially when the combined drugs are expected to act via synergistic interactions. The clinical development of combination treatments is particularly challenging, particularly during the dose-selection phase, where a vast range of possible combination doses exists. The purpose of this work was to evaluate the added value of using optimal design for guiding the dose allocation in drug combination dose-finding studies as compared with a typical drug-combination trial. Optimizations were performed using local [D(s)-optimality] and global [ED(s)-optimality] optimal designs to maximize the precision of model parameters in a number of potential exposure-response (E-R) surfaces. A compound criterion [D(s)/V-optimality] was used to optimize the precision of model predictions in specific parts of the E-R surfaces. Optimal designs provided unbiased estimates and significantly improved the accuracy of results relative to the typical design. It was possible to improve the efficiency and overall parameter precision up to 7832% and 96.6% respectively. When the compound criterion was used, the probability to accurately identify the optimal dose-combination increased from 71% for the typical design up to 91%. These results indicate that optimal design methodology in tandem with E-R analyses is a beneficial tool that can be used for appropriate dose allocation in dose-finding studies for drug combinations.

The modeling method on thermal expansion of CNC lathe headstock in vertical direction based on MOGA

Thermal errors often occur in spindle system due to the changing of ambient temperature and/or inner or outer heat sources of machine tools (MTs), which is the major factor restricting the machine accuracy. Therefore, constructing compensation models with high accuracy and high robustness turn to be a cost-efficient approach for minimizing thermal error and improving machine accuracy. There are two categories of modeling methods for thermal errors so far, namely, physical-based method and empirical-based method. Each modeling method has its own merits and drawbacks. In this paper, a multi-objective genetic algorithm (MOGA) was used to combine the approximative physical model based on thermal expansion mechanism in vertical direction of numerical control (NC) lathe headstock with the accurate empirical model based on experimental data obtained from thermal performance test of spindle system. Consequently, a prediction model of thermal error in vertical direction of headstock with high accuracy and high robustness was obtained. The new model synthesized the advantages of the two types of modeling methods and showed relatively high accuracy and robustness. According to the results of a series of verification experiments on thermal performance of the spindle system under various ambient temperatures and different working conditions, the precision of prediction model has maintained above 74%.

A Comparative Analysis of the Usefulness of Survival Prediction Models for Patients with Glioblastoma in the Temozolomide Era: The Importance of Methylguanine Methyltransferase Promoter Methylation, Extent of Resection, and Subventricular Zone Location

Several survival prediction models for patients with glioblastoma have been proposed, but none is widely used. This study aims to identify the predictors of overall survival (OS) and to conduct an independent comparative analysis of 5 prediction models. Multi-institutional data from 159 patients with newly diagnosed glioblastoma who received adjuvant temozolomide concomitant chemoradiotherapy (CCRT) were collected. OS was assessed by Cox proportional hazards regression and adjusted for known prognostic factors. An independent CCRT patient cohort was used to externally validate the 1) RTOG (Radiation Therapy Oncology Group) recursive partitioning analysis (RPA) model, 2) Yang RPA model, and 3) Wee RPA model, Chaichana model, and the RTOG nomogram model. The predictive accuracy for each model at 12-month survival was determined by concordance indices. Calibration plots were performed to ascertain model prediction precision. The median OS for patients who received CCRT was 19.0 months compared with 12.7 months for those who did not (P < 0.001). Independent predictors were:1)subventricular zone II tumors (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.0-2.5); 2) methylguanine methyltransferase promoter methylation (HR, 0.36; 95% CI, 0.2-0.6); and 3) extent of resection of >85% (HR, 0.59; 95% CI, 0.4-0.9). For 12-month OS prediction, the RTOG nomogram model was superior to the RPA models with a c-index of 0.70. Calibration plots for 12-month survival showed that none of the models was precise, but the RTOG nomogram performed relatively better. The RTOG nomogram best predicted 12-month OS. Methylguanine methyltransferase promoter methylation status, subventricular zone tumor location, and volumetric extent of resection should be considered when constructing prediction models.

Estimating in situ maximum net photosynthetic rate of Larix olgensis based on abscised mea-surement

The data of needle in situ maximum net photosynthetic rate (SPn max) and abscised maximum net photosynthetic rate (APn max) were measured for the 15 year-old planted Larix olgensis stand in the Maoershan Forest Farm, Heilongjiang Province, China. The change pattern between APn max and abscised time (ta) was analyzed and the functional relationship between SPn max and APn max with ta was also established. Finally, the prediction model of SPn max for planted L. olgensis trees was developed by analyzing the effect of tree size and environmental factors on the decline of APnmax. The results showed that needle APn max decreased with the increase of ta without restoring water supply. The higher vapor pressure deficit (VPD) and the leaf temperature (Tleaf) would lead to faster and lager reduction of APn max. Taking VPD and ta as the independent variables of the linear regression model had the best goodness of fit for SPn max (Ra2 were 0.774 and RMSE was 20.73). The model prediction precision decreased with the increase of ta, but after 20 min it would be stabilized at 97%. Overall, estimating SPn max of L. olgensis trees by developing regression model based on abscised measurement not only had a well predictive ability but also had a stable predictive precision, and greatly improved the efficiency of field measurement. The results of this study could be suitably applied to measure SPn max in practice.

Factors governing the prevalence and richness of avian haemosporidian communities within and between temperate mountains.

Mountains are well-suited systems to disentangle the factors driving distribution of parasites due to their heterogeneity of climatic and habitat conditions. However, the information about the relative importance of environmental factors governing the distribution of avian haemosporidians on temperate mountains is very limited. The main goal of the present study is to identify the factors determining prevalence and richness in avian haemosporidians (Plasmodium, Haemoproteus and Leucocytozoon) at the community level along elevational gradients on two mountain ranges located around the northern and southern limits of the Iberian Peninsula (Spain). We used samples from 68 avian species and 1,460 breeding individuals caught over widespread woodland and open habitats. Our findings confirmed the importance of climatic variables explaining prevalence and richness on Iberian mountains. However, landscape variables and other factors named host richness and migration behaviour explained more variation than climatic ones. Plasmodium genus preferred open and warm habitats. Water sources were also important for the southern but not for the northern mountain. Haemoproteus and Leucocytozoon showed affinities for woodland areas. Climatic conditions for Haemoproteus and Leucocytozoon were dependent on the mountain range suggesting some adaptation of avian haemosporidian and their invertebrate vectors to the climatic particularities of both mountain massifs. In contrast to Plasmodium and Haemoproteus genera, Leucocytozoon prevalence and richness values were significantly higher in the southern mountain range. Overall, our findings at the community level has enriched the relative weight and effect direction of environmental factors governing the distribution and prevalence of the avian haemosporidian community. Also, our results provide a caution message about the precision of predictive models on parasite distributions based on climatic variables, since such predictions could overestimate the effect of climate change scenarios on the transmission of the haemosporidians.

Biomarkers in amyotrophic lateral sclerosis: combining metabolomic and clinical parameters to define disease progression.

The objectives of this study were to define the metabolomic profile of cerebrospinal fluid in amyotrophic lateral sclerosis (ALS) patients, to model outcome through combined clinical and metabolomic parameters and independently to validate predictive models. In all, 74 consecutive newly diagnosed patients were enrolled into training (Tr, n = 49) and test (Te, n = 25) cohorts. Investigators recorded clinical data and the metabalomic profile of cerebrospinal fluid at baseline was analyzed with (1)H nuclear magnetic resonance spectroscopy. Markers of disease progression, collected in 1-year prospective follow-up, included change in ALS Functional Rating Scale (var_ALSFRS), change in weight (var_weight) and survival time. Stepwise multiple regression selected from metabolomic and clinical parameters to model rate of progression in the Tr cohort. Best fit models were validated independently in the Te cohort. The best-fit statistical models, using both metabolomic and clinical covariates, predicted outcome with 70.8% (var_weight), 72% (var_ALSFRS) and 76% (survival) accuracy in the Te cohort. Models that used metabolomics or clinical data alone predicted outcome less well. Highlighted metabolites are involved in pathophysiological pathways previously described in ALS. Cerebrospinal fluid metabolomics can aid in predicting the clinical course of ALS and tap into pathophysiological processes. The precision of predictive models, independently reproduced in this study, is enhanced through inclusion of both metabolomic and clinical parameters. The findings bring the field closer to a clinically meaningful disease marker.

A Feasibility Study on the Evaluation of Quality Properties of Chinese Rice Wine Using Raman Spectroscopy

Rapid analysis of Chinese rice wine (CRW) is an important activity for quality assurance and control investigations. In recent years, due to its insensitivity to water and fewer overlapped bands, Raman spectroscopy (RS) may provide more useful qualitative and quantitative information on functional groups of various chemical compounds in CRWs than the conventional spectroscopic technique (e.g., infrared spectroscopy); there has been a growing interest in the application of RS in the qualitative and quantitative analysis in food industry. In this study, the applicability of RS hyphenated with chemometrics using different pretreated spectra was examined to develop rapid, low-cost, and non-destructive method for quantification of four enological parameters involved in CRW quality control. Partial least square (PLS) was used for building the calibration models for the four chemical parameters based on the full RS spectrum. The model was also optimized by using efficient wavelength selection algorithm, i.e., synergy interval partial least square (SiPLS) algorithm. In addition, soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA) were used as classification techniques to predict the brands (wineries) of CRW samples. The results demonstrated that compared with the PLS model using all wavelengths of RS spectra, the prediction precision of model based on the spectral variables selected by SiPLS was significantly improved with high values of the coefficient of determination (>0.90), residual predictive deviation (>3.0), and range error ratio (>10) for all of the four quality parameters. The SIMCA and LDA results, characterized by high percentages of correct classification (96.67 and 100.00 % as average value in prediction for SIMCA and LDA, respectively), showed that samples belonging to a particular brand could be correctly classified. The overall results indicated the suitability of RS combined with efficient variable selection algorithm to rapidly control the quality of CRW.

Measurement of fermentation parameters of Chinese rice wine using Raman spectroscopy combined with linear and non-linear regression methods

Effective fermentation monitoring is a growing need during the manufacture of wine due to the rapid pace of change in the wine industry. Ethanol and reducing sugar are two most important process variables indicating the status of Chinese rice wine (CRW) fermentation process. In this study, the potentials of Raman spectroscopy (RS) as a rapid process analytical technique to monitor the evolution of these two chemical parameters involved in CRW fermentation process and to group samples according to different fermentation stages were investigated. The results demonstrated that compared with the PLS model using all wavelengths of Raman spectra, the prediction precision of model based on the spectral variables selected by competitive adaptive reweighted sampling (Cars) was significantly improved. In addition, nonlinear models outperformed linear models in predicting fermentation parameters. After systemically comparison and discussion, it was found that for both ethanol and glucose, Cars-support vector machine (Cars-SVM) models gave the best results with the highest prediction precisions. Moreover, the results obtained from discriminant partial least squares analysis (DPLS) showed that good performances were obtained with an average correct classification rate of 94.9% for different fermentation stages. The overall results indicated that RS combined with efficient variable selection algorithm and nonlinear regression tool could be utilized as a rapid method to monitor CRW fermentation process.

Research on Several Problems in Partial Least Squares Regression Analysis

Purpose: preliminary discussion on model prediction precision in the partial least squares regression analysis method; Method: introduce current development conditions of partial least squares regression analysis, analyze problems of traditional regression analysis method such as multiple linear regression analysis, introduce the mathematic principle and modeling method of the partial least squares regression analysis method, and conduct detailed analysis on the partial least squares regression analysis modeling and prediction by using the classical Linnerud data. Result: The partial least squares regression analysis has the basic features of the multiple linear regression analysis and principal component analysis, can precisely predict multiple data and establish a precise mathematical model; Conclusion: The partial least squares regression analysis can provide precise mathematical model and can reserve the explaining variants remarkably associated to explained variants to most extent, so it is feasible to some extent and can meet the general requirements of engineering, economy, biology and medical statistical analysis.

Detection in situ of carotenoid in microalgae by transmission spectroscopy

Carotenoids, which can be part of the food additives and drug additive, are one of important internal quality indexes for living microalgae. In the present study, visible/near infrared (Vis/NIR) transmission spectra in situ of Spirulina sp. suspension were required using an Ocean Optics USB 4000 Spectrometer in the wavelength range of 346–1038nm, and the loading weights (LW) analysis, uninformative variables elimination (UVE) and successive projections algorithm (SPA) were used to select important variables related to the carotenoids content (CC) for the Spirulina sp. suspension. Different concentrations of 100 samples of Spirulina sp. suspension were selected. The results showed the correlation coefficient (r), root mean square error (RMSE) and residual predictive deviation (RPD) in the prediction sets were 0.96, 0.23mg/L, 3.40, 0.89, 0.39mg/L, 1.59 and 0.96, 0.24mg/L, 3.44 for x-LW-PLS, UVE-PLS and SPA-PLS model respectively. It indicated that SPA-PLS gave the best result, while x-LW-PLS was better than UVE-PLS. So, Vis/NIR transmission spectra combined with SPA method was feasible to assess CC of Spirulina sp. suspension. And SPA variable selection method can simplify the prediction model and improve the model prediction precision. Furthermore, the method can be used as a good example for the detection in situ of other pigment content in other microalgae.

Predictive performance of a gentamicin population pharmacokinetic model in neonates receiving full-body hypothermia.

Population pharmacokinetic (popPK) models derived from small pharmacokinetics (PK) studies in neonates are often underpowered to detect clinically important characteristics that drive dosing. External validation of such models is crucial. In this study, the predictive performance of a gentamicin popPK model in neonates receiving hypothermia was evaluated. A previously published gentamicin popPK model was developed in neonates with hypoxic ischemic encephalopathy undergoing hypothermia using a retrospective single-institution (University of California-San Francisco) data set. The predictive performance of this model was evaluated in an external retrospective data set from the University of California-San Francisco (validation A) and another from Duke University (validation B). Both institutions used the same hypothermia protocol and collected similar clinical and PK data. Gentamicin dosing and samples were collected per routine care. Predictive performance was evaluated by quantifying the accuracy and precision of model predictions and using simulation-based diagnostics to detect bias in predictions. Forty-one neonates (n = 18 validation A; n = 23 validation B) with median (range) gestational age of 40 weeks (33-42) and birth weight of 3.3 kg (1.9-4.6) and 76 samples (55% troughs, 33% and 28% drawn at 24 and 36 hours after dose, respectively) were analyzed. The model adequately predicted gentamicin concentrations from the same institution (validation A; median average fold error = 1.1 and numerical prediction distribution error P > 0.05) but underpredicted concentrations from the outside institution (validation B; median average fold error = 0.6 and numerical prediction distribution error P < 0.05). The model demonstrated adequate predictive performance for an external data set in the same institution but not from an outside institution. Larger sample sizes, use of data from multiple institutions, and external evaluation in development of popPK models in neonates may improve generalizability of dosing recommendations arising from single-institution studies.

Computational Fluid Dynamics (CFD) Modeling of Gaseous Pollutants Dispersion in Low Wind Speed Condition: Isfahan Refinery, a Case Study

A 3D computational fluid dynamics (CFD) model is developed to predict the dispersion of gaseous pollutants released from different stacks in Isfahan refinery in Iran. Three types of turbulent models including the standard k–ϵ, the RNG k–ϵ, and the realizable k–ϵ models are compared and considered. The results of model are compared with the experimental data obtained by measuring the CO2 concentration inside and close to the refinery boundaries. The comparison shows the sufficient precision of model predictions. By using the design of experiment (DOE) technique, the effects of model parameters are investigated on the results. The results of standard k–ϵ model for Sc t = 0.5 and hr = 2 m, the realizable k–ϵ model for hr = 2 m, and the RNG.k–ϵ model for hr = 2.5 m provide more acceptable results when these results are compared with the models responses with ideal values of these parameters. The latter gives some better results for the case of Isfahan refinery.

Optimization of Temperature and Force Adaptation Algorithm in Hot Strip Mill

In the hot strip rolling control system, the temperature distribution and deformation resistance are the main parameters affecting prediction of rolling force. In order to improve the model prediction precision, an optimization algorithm based on objective function was put forward, in which the penalty function index was adopted. During the adaptation process, the temperature distribution and deformation resistance were taken as the optimized parameters, and the Nelder-Mead simplex algorithm was used to search the optimal solution of the objective function. Furthermore, the temperature adaptation and force adaptation were solved simultaneously. Application results show that the method can improve the accuracy of the rolling force model obviously, and it can meet the demand of the industrial production and has a good application prospect.

Fuzzy Modeling, a Novel Approach to Studying Pharmacodynamics

Fuzzy logic can handle questions to which the answers may be "yes" at one time and "no" at the other, or may be partially true and untrue. Pharmacodynamic data deal with questions such as "Does a patient respond to a particular drug dose or not," or "Does a drug cause the same effects at the same time in the same subject or not." Such questions are typically of a fuzzy nature and might, therefore, benefit from an analysis based on fuzzy logic.The objective was to assess whether fuzzy logic can improve the precision of predictive models for pharmacodynamic data.The methods and results were as follows: (1). The quantal pharmacodynamic effects of different induction dosages of thiopental on numbers of responding subjects were used as the first example. Regression analysis of the fuzzy-modeled outcome data on the input data provided a much better fit than did the unmodeled output values with r-square values of 0.852 (F-value = 40.34) and 0.555 (F-value = 8.74), respectively. (2). The time-response effect propranolol on peripheral arterial flow was used as a second example. Regression analysis of the fuzzy-modeled outcome data on the input data provided a better fit than did the unmodeled output values with r-square values of 0.990 (F-value = 416) and 0.977 (F-value = 168), respectively.Fuzzy modeling may better than conventional statistical method fit and predict pharmacodynamic data, such as, for example, quantal dose response and time response data. This may be relevant to future pharmacodynamic research.

Combustion kinetic modeling using multispecies time histories in shock-tube oxidation of heptane

Recently, species time histories have been measured during n-heptane oxidation behind reflected shock waves [D.F. Davidson, Z. Hong, G.L. Pilla, A. Farooq, R.D. Cook, R.K. Hanson, Combust. Flame 157 (2010) 1899–1905]. The highly precise nature of these measurements is expected to impose critical constraints on chemical kinetic models of hydrocarbon combustion. In this paper, we apply the Method of Uncertainty Analysis using Polynomial Chaos Expansions (MUM-PCE) [D.A. Sheen, X. You, H. Wang, T. Løvås, Proc. Combust. Inst. 32 (2009) 535–542] to demonstrate how the multispecies measurement may be utilized beyond simple model validation. The results show that while an as-compiled, prior reaction model of n-alkane combustion can be accurate in its prediction of the detailed species profiles, the kinetic parameter uncertainty in the model remains to be too large to obtain a precise prediction of the data. Constraining the prior model against the species time histories within the measurement uncertainties led to notable improvements in the precision of model predictions against the species data as well as the global combustion properties considered. Lastly, we show that while the capability of the multispecies measurement presents a step-change in our precise knowledge of the chemical processes in hydrocarbon combustion, accurate data of global combustion properties are still necessary to predict fuel combustion.

Photochemical modeling of Titan atmosphere at the “10 percent uncertainty horizon”

Titan's atmospheric chemistry modeling is presently limited by the lack of knowledge about many reaction rate coefficients at low temperature (50-200 K). Considering the difficulty of measuring such data, the only way to improve this situation is to identify key reactions as the ones for which better estimations of reaction rates is guaranteed to have a strong influence on the precision of model predictions. This is a slow iterative process, the limit of which has never been clearly defined in terms of model precision. The fact is that this limit is not a fully deterministic simulation, since one should not expect all reaction rate coefficients ever to become available with null uncertainty. The present study considers a quite optimistic scenario, in which all reaction rate coefficients in the chemical model are assumed to be known with a 10% relative uncertainty. The implications for chemical growth modeling are discussed.

Design of Optimal Sequential Experiments to Improve Model Predictions from a Polyethylene Molecular Weight Distribution Model

AbstractReliable model predictions require an appropriate model structure and also good parameter estimates. For good parameter estimates to be obtained, it is important that the data used in parameter estimation are informative. Alphabet‐optimal experimental designs can be used to ensure that new experiments are as informative as possible. This work presents the development of D‐ and A‐optimal sequential experimental designs for improving parameter precision in a molecular‐weight‐distribution model for Ziegler‐Natta‐catalyzed polyethylene. Novel V‐optimal designs techniques are developed to improve the precision of model predictions, and anticipated benefits are quantified. Problems with local minima are discussed and comparisons between the optimality criteria are made.magnified image

Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation (BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm (GA) is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM(R) Tog-L-Loc system with different technologic parameters. The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network (ANN) for training. The validating samples' experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

Delay Prior to Calling 9-1-1 Is Associated with Increased Mortality After Out-of-Hospital Cardiac Arrest

Objective. We sought to characterize the collapse-to-9-1-1 call interval, to evaluate the frequency of pre–9-1-1 delay, andto assess whether delay is associated with decreased survival after out-of-hospital cardiac arrest (OHCA). Methods. This was a five-year prospective survey of bystanders to adult OHCA cases in which the victims were transported to seven local teaching hospitals in Michigan. Bystander data were obtained by telephone interview beginning two weeks after the event, andthrough review of emergency medical services (EMS) documents. Criteria for pre–9-1-1 delay were prospectively developed. Two paramedic reviewers were trained on these criteria andreviewed bystander andEMS data for each cardiac arrest case. Multivariate regression analysis was used to assess the independent impact of delay on survival. We collected common bystander andEMS OHCA demographics, as well as bystander description of events prior to the 9-1-1 call. Outcome was survival to hospital discharge. Results. During the study period we identified 1,004 OHCAs, for which 779 bystanders completed interviews. Of these interviews, 688 had adequate data for analysis. Raters showed moderate to strong agreement for a 15% subsample of cases. Of all cases, 330 (48%) were identified as having had pre–9-1-1 delay. Delay was less commonly associated with witnessed arrest (odds ratio [OR] 2.7; 95% confidence interval [CI] 2.0–3.7%) andpublic location (OR 1.57; 95% CI 1.1–2.2%). In a multivariate model, only initial-rhythm ventricular tachycardia/ventricular fibrillation was associated with improved survival (OR 2.28; 95% CI 1.3–4.1), andpre–9-1-1 delay was associated with decreased survival (OR 0.46; 95% CI 0.3–0.9%). Conclusion. This method demonstrated that prehospital delay is common in OHCA andis associated with increased mortality. Measurement of pre–9-1-1 delay may improve precision of predictive models for OHCA survival.