Статті в журналах: "SIZE PREDICTION"

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Treme, Gehron, David R. Diduch, Mark J. Billante, Mark D. Miller, and Joseph M. Hart. "Hamstring Graft Size Prediction." American Journal of Sports Medicine 36, no. 11 (August 25, 2008): 2204–9. http://dx.doi.org/10.1177/0363546508319901.

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Background Recently we retrospectively collected clinical data to predict hamstring graft diameter. Prospective data collection will improve and further define prediction of hamstring graft size. Hypothesis Clinical anthropometric data can be used to predict hamstring graft size. Study Design Cohort study (prevalence); Level of evidence, 1. Methods Fifty consecutive patients with anterior cruciate ligament deficiency scheduled for reconstruction using hamstring autograft were prospectively evaluated. Preoperatively we recorded height, weight, body mass index, age, gender, leg length, thigh length, shank length, bilateral thigh circumference, and Tegner score. Intraoperative measurements of both the gracilis and semitendinosus tendons were made, including absolute length before fashioning the graft and final diameter of the quadrupled graft using sizing tubes calibrated to 0.5 mm. Bivariate correlation coefficients (Pearson r) were calculated to identify relationships among clinical data and intraoperatively measured hamstring graft length and diameter. Results Strongest correlations for graft lengths were height and leg length measurements. Shorter persons with shorter leg, thigh, and shank lengths tended to have shorter gracilis and semitendinosus grafts. Likewise, the strongest correlations for graft diameter were weight and thigh circumference. Self-reported activity level and age did not correlate. Gender comparison revealed that women who were shorter, lighter, and had smaller body mass indices were more likely to have smaller graft diameters and shorter graft lengths. Conclusion Patients weighing less than 50 kg, less than 140 cm in height, with less than 37 cm thigh circumference, and with body mass index less than 18 should be considered at high risk for having a quadrupled hamstring graft diameter less than 7 mm. When separated by gender, small graft diameters are most likely in older, short, female subjects with small thigh circumferences or young, skinny, male subjects with small thigh circumferences and low body mass index. Common clinical measurements can be used for preoperative identification of patients at risk for insufficient graft tissue and would be useful for patient counseling and alternative graft source planning.

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Wang, Hsin-Yao, Yu-Hsin Liu, Yi-Ju Tseng, Chia-Ru Chung, Ting-Wei Lin, Jia-Ruei Yu, Yhu-Chering Huang, and Jang-Jih Lu. "Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance." Diagnostics 12, no. 2 (February 5, 2022): 413. http://dx.doi.org/10.3390/diagnostics12020413.

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The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

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Zhang, Hao, Chong Wang, Zhengyan Chen, Qingyu Kang, Xiaohua Xu, and Tianpeng Gao. "Performance Comparison of Different Particle Size Distribution Models in the Prediction of Soil Particle Size Characteristics." Land 11, no. 11 (November 17, 2022): 2068. http://dx.doi.org/10.3390/land11112068.

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Particle size distribution (PSD) is a rich source of information about soil properties, including soil gradation and soil particle size characteristics. This paper compared the PSD prediction ability of three types of mathematical model. We selected nine models that have been proven to accurately predict sample points in previous studies, and we fit 144 pieces of experimental data on 12 texture classes of soil samples from the UNSODA database. We compared the models’ capability for predicting non-sample points, which is important for predicting soil particle size characteristics. Each model’s ability to predict non-sample points of different texture classes of soil was studied using a comprehensive ranking method. The relative differences in the models’ prediction of non-sample points of different texture classes of soil were analyzed using the relative error method. The results showed no considerable correlation between the number of model parameters and the prediction accuracy. For the various texture classes of soil, the Skaggs model and Weipeng model had the highest accuracy in predicting non-sample points, and the Skaggs model had the widest range of application. The Zhongling model and the Weibull model were better in predicting only one texture class of soil, respectively. The Fredlund model, Kolve model, Rosin model, Van Genuchten model and Best model were not as successful as other models. The Weipeng model overestimated the solid particle mass proportion, while the Skaggs model underestimated it when the clay particle content was greater than 20%. Both the Weipeng model and the Skaggs model demonstrated good prediction accuracy when the particle size was within the silt particle size range. The Skaggs model overestimated the particle mass proportion, while the Weipeng model underestimated it when the particle size was within the sand particle size range.

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Rangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar, and Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets." Agronomy 11, no. 11 (November 5, 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.

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Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.

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Gill, Nasib S., and P. S. Grover. "Software size prediction before coding." ACM SIGSOFT Software Engineering Notes 29, no. 5 (September 2004): 1–4. http://dx.doi.org/10.1145/1022494.1022514.

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van Smeden, Maarten, Karel GM Moons, Joris AH de Groot, Gary S. Collins, Douglas G. Altman, Marinus JC Eijkemans, and Johannes B. Reitsma. "Sample size for binary logistic prediction models: Beyond events per variable criteria." Statistical Methods in Medical Research 28, no. 8 (July 3, 2018): 2455–74. http://dx.doi.org/10.1177/0962280218784726.

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Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.

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Di, Yu, Ying Li, and Yan Luo. "Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters." Journal of Refractive Surgery 39, no. 5 (May 2023): 332–39. http://dx.doi.org/10.3928/1081597x-20230207-03.

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Purpose: To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters. Methods: This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), and Bland-Altman diagrams were used to evaluate the prediction formula. Results: Anterior chamber depth (ACD) had the greatest influence on vault 1 week after ICL V4c implantation, followed by ICL V4c size and angle-to-angle distance (ATA). The prediction formula was obtained according to the partial regression coefficient, which was vault (mm) = −1.279 + 0.291 × ACD (mm) + 0.210 × ICL V4c size (mm) – 0.144 × ATA (mm) ( R 2 = 0.661). In the formula validation group, the mean predictive vault, MAE, MedAE, and RMSE were 628.10, 135.09, 130.42, and 150.46 µm, respectively. The Bland-Altman diagram showed the predictive vault was in good agreement with the actual vault. Conclusions: A novel ICL V4c vault prediction formula was developed and shown to be an effective method for predicting the vault to reduce surgical complications. [ J Refract Surg . 2023;39(5):332–339.]

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Ajana, Soufiane, Niyazi Acar, Lionel Bretillon, Boris P. Hejblum, Hélène Jacqmin-Gadda, Cécile Delcourt, Niyazi Acar, et al. "Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size." Bioinformatics 35, no. 19 (April 1, 2019): 3628–34. http://dx.doi.org/10.1093/bioinformatics/btz135.

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Abstract Motivation In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS. Results Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group. Availability and implementation R codes for the prediction methods are freely available at https://github.com/SoufianeAjana/Blisar. Supplementary information Supplementary data are available at Bioinformatics online.

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Kumar, Arun, and Mingyue Chen. "Inherent Predictability, Requirements on the Ensemble Size, and Complementarity." Monthly Weather Review 143, no. 8 (August 1, 2015): 3192–203. http://dx.doi.org/10.1175/mwr-d-15-0022.1.

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Abstract Faced with the scenario when prediction skill is low, particularly in conjunction with long-range predictions, a commonly proposed solution is that an increase in ensemble size will rectify the issue of low skill. Although it is well known that an increase in ensemble size does lead to an increase in prediction skill, the general scope of this supposition, however, is that low prediction skill is not a consequence of constraints imposed by inherent predictability limits, but an artifact of small ensemble sizes, and further, increases in ensemble sizes (that are often limited by computational resources) are the major bottlenecks for improving long-range predictions. In proposing that larger ensemble sizes will remedy the issue of low skill, a fact that is not well appreciated is that for scenarios with high inherent predictability, a small ensemble size is sufficient to realize high predictability, while for scenarios with low inherent predictability, much larger ensemble sizes are needed to realize low predictability. In other words, requirements on ensemble size (to realize the inherent predictability) and inherent predictability are complementary variables. A perceived need for larger ensembles, therefore, may also imply the presence of low predictability.

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Gomez, Ana Isabel, Marcos Cruz, and Luis Manuel Cruz-Orive. "VARIANCE PREDICTION FOR POPULATION SIZE ESTIMATION." Image Analysis & Stereology 38, no. 2 (July 18, 2019): 131. http://dx.doi.org/10.5566/ias.1991.

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Design unbiased estimation of population size by stereological methods is an efficient alternative to automatic computer vision methods, which are generally biased. Moreover, stereological methods offer the possibility of predicting the error variance from a single sample. Here we explore the statistical performance of two alternative variance estimators on a dataset of 26 labelled crowd pictures. The empirical mean square errors of the variance predictors are compared by means of Monte Carlo resampling.

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Jokiel-Rokita, Alicja. "Minimax prediction under random sample size." Applicationes Mathematicae 29, no. 2 (2002): 127–34. http://dx.doi.org/10.4064/am29-2-1.

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Sarkar, Sabita, Govind Sharan Gupta, and Shin-ya Kitamura. "Prediction of Raceway Shape and Size." ISIJ International 47, no. 12 (2007): 1738–44. http://dx.doi.org/10.2355/isijinternational.47.1738.

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13

Wang, Ming-Dauh. "Sample Size Reestimation by Bayesian Prediction." Biometrical Journal 49, no. 3 (June 2007): 365–77. http://dx.doi.org/10.1002/bimj.200310273.

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Wang, Ming-Dauh. "Sample Size Reestimation by Bayesian Prediction." Biometrical Journal 49, no. 3 (June 2007): NA. http://dx.doi.org/10.1002/bimj.200510273.

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Lan, Yu, and Daniel F. Heitjan. "Adaptive parametric prediction of event times in clinical trials." Clinical Trials 15, no. 2 (January 29, 2018): 159–68. http://dx.doi.org/10.1177/1740774517750633.

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Background: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. Purpose: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions. Methods: At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model. Results: A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129. Limitations: Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival. Conclusions: An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models.

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Zhu, Yongjie, Zhiqun Hu, Shujie Yuan, Jiafeng Zheng, Dejin Lu, and Fujiang Huang. "Raindrop Size Distribution Prediction by an Improved Long Short-Term Memory Network." Remote Sensing 14, no. 19 (October 7, 2022): 4994. http://dx.doi.org/10.3390/rs14194994.

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The observation of and research on raindrop size distribution (DSD) is important for mastering and understanding the mutual restriction relationship between cloud dynamics and cloud microphysics in a process of precipitation; it also plays an irreplaceable role in many fields, such as radar meteorology, weather modification, boundary layer land surface processes, aerosols, etc. Using more than 1.7 million minutes of raindrop data observed with 17 laser disdrometers at 17 stations in Anhui Province, China, from 7 August 2009 to 30 April 2020, a DSD training dataset was constructed. Furthermore, the data are fitted to a normalized Gamma function and used to obtain its three parameters, i.e., the normalized intercept Nw, the mass weighted average diameter Dm, and the shape factor μ. Based on the long short-term memory network (LSTM), a DSD Gamma distribution prediction network (DSDnet) was designed. In the process of modeling based on DSDnet, a self-defined loss function (SLF) was proposed in order to improve the DSD prediction by increasing the weight values in the poor fitting regions according to the common mean square error loss function (MLF). By means of the training dataset, a DSDnet-based model was trained to realize the prediction of Nw, Dm, and μ minute-to-minute over the course of 30 min, and then was evaluated by the test dataset according to three indicators, namely, mean relative error (MRE), mean absolute error (MAE), and correlation coefficient (CC). The CC of lgNw, Dm, and μ can reach 0.93403, 0.90934, and 0.89741 for 12-min predictions, and 0.87559, 0.85261, and 0.84564 for 30-min predictions, respectively, which means that the DSD prediction accuracy within 30 min can basically reach the application level. Furthermore, the 12- and 30-min predictions of 3 precipitation processes were taken as examples to fully demonstrate the application effect of model. The prediction effects of Nw and Dm are better than that of μ, and the stratiform precipitation is better than the convective and convective-stratiform mixed cloud precipitation.

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Hassouna, Fady M. A., and Khaled Al-Sahili. "Practical Minimum Sample Size for Road Crash Time-Series Prediction Models." Advances in Civil Engineering 2020 (December 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/6672612.

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Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries (Denmark, Turkey, Germany, and Israel) were used to develop the crash prediction models based on different sample sizes (45, 35, 25, and 15 years). Then, crash data for 2016 and 2017 were used to verify the accuracy of the developed models. Furthermore, crash data for Palestine were used to test the validity of the results. The used data included fatality, injury, and property damage crashes. The results showed similar trends in the models’ prediction accuracy for all four countries when predicting road crashes for year 2016. Decreasing the sample sizes led to less prediction accuracy up to a sample size of 25; then, the accuracy increased for the 15-year sample size. Whereas there was no specific trend in the prediction accuracy for year 2017, a higher range of prediction error was also obtained. It is concluded that the prediction accuracy would vary based on the varying socioeconomic, traffic safety programs and development conditions of the country over the study years. For countries with steady and stable conditions, modeling using larger sample sizes would yield higher accuracy models with higher prediction capabilities. As for countries with less steady and stable conditions, modeling using smaller sample sizes (15 years, for example) would lead to high accuracy models with good prediction capabilities. Therefore, it is recommended that the socioeconomic and traffic safety program status of the country is considered before selecting the practical minimum sample size that would give an acceptable prediction accuracy, therefore saving efforts and time spent in collecting data (more is not always better). Moreover, based on the data analysis results, long-term ARIMA prediction models should be used with caution.

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Whiteaker, Brian, and Peter Gerstoft. "Reducing echo state network size with controllability matrices." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073116. http://dx.doi.org/10.1063/5.0071926.

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Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models benefit from quick training and low complexity, computation demands from a large reservoir matrix are a bottleneck. Using control theory, a reduced size replacement reservoir matrix is found. Starting from a large, task-effective reservoir matrix, we form a controllability matrix whose rank indicates the active sub-manifold and candidate replacement reservoir size. Resulting time speed-ups and reduced memory usage come with minimal error increase to chaotic climate reconstruction or short term prediction. Experiments are performed on simple time series signals and the Lorenz-1963 and Mackey–Glass complex chaotic signals. Observing low error models shows variation of active rank and memory along a sequence of predictions.

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Minasny, Budiman, and Alex B. McBratney. "Evaluation and development of hydraulic conductivity pedotransfer functions for Australian soil." Soil Research 38, no. 4 (2000): 905. http://dx.doi.org/10.1071/sr99110.

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Pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity (Ks) were evaluated using published Australian soil data sets. Eight published PTFs were evaluated. Generally, published PTFs provide a satisfactory estimation of Ks depending on the spatial scale and accuracy of prediction. Several PTFs were developed in this study, including the power function of effective porosity, multiple linear regression, fractal model, and artificial neural networks. Different methods for estimating the fractal dimension of particle-size distributions showed no significant differences in predicting Ks . The simplest model for estimating fractal dimension from the log–log plot of particle-size distribution is therefore recommended. The data set was also stratified into 3 broad classes of texture: sandy, loamy, and clayey. Stratification of PTFs based on textural class showed small improvements in estimation. The published PTF of Dane and Puckett (1994) Proc. Int. Workshop (Univ. of California: Riverside, CA) gives the best prediction for sandy soil; the PTF of Cosby et al. (1984) Water Resources Research 20, 682–90 gives the best production for loamy soil; and the PTF of Schaap et al. (1998) Soil Science Society of America Journal 62, 847–55 gives the best prediction for clayey soil. The data set used comprised different field and laboratory measurements over large areas, and limited predictive variables were available. The PTFs developed here may predict adequately in large areas (residuals = 10–20 mm/h), but for site-specific applications, local calibration is needed.

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Yin, Peng-Yeng, Shyong Jian Shyu, Shih-Ren Yang, and Yu-Chung Chang. "Reinforcement Learning for Improving Gene Identification Accuracy by Combination of Gene-Finding Programs." International Journal of Applied Metaheuristic Computing 3, no. 1 (January 2012): 34–47. http://dx.doi.org/10.4018/jamc.2012010104.

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Due to the explosive and growing size of the genome database, the discovery of gene has become one of the most computationally intensive tasks in bioinformatics. Many such systems have been developed to find genes; however, there is still some room to improve the prediction accuracy. This paper proposes a reinforcement learning model for a combination of gene predictions from existing gene-finding programs. The model learns the optimal policy for accepting the best predictions. The fitness of a policy is reinforced if the selected prediction at a nucleotide site correctly corresponds to the true annotation. The model searches for the optimal policy which maximizes the expected prediction accuracy over all nucleotide sites in the sequences. The experimental results demonstrate that the proposed model yields higher prediction accuracy than that obtained by the single best program.

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Afshartous, David, and Jan de Leeuw. "Prediction in Multilevel Models." Journal of Educational and Behavioral Statistics 30, no. 2 (June 2005): 109–39. http://dx.doi.org/10.3102/10769986030002109.

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Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2 (group) sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, prior, and multilevel estimators for the Level 1 coefficientsβj The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups, J, increases. Finally, this article investigates the robustness of the multilevel prediction rule to misspecifications of the Level 2 model.

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Soo, Adrianne Elizabeth, Eric Olsson, and Moe Lim. "Prediction of Cervical Endplate Size: One Size Does Not Fit All." Orthopedics 39, no. 3 (May 1, 2016): e526-e531. http://dx.doi.org/10.3928/01477447-20160427-11.

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Ferro, Christopher A. T., Tim E. Jupp, F. Hugo Lambert, Chris Huntingford, and Peter M. Cox. "Model complexity versus ensemble size: allocating resources for climate prediction." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1962 (March 13, 2012): 1087–99. http://dx.doi.org/10.1098/rsta.2011.0307.

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A perennial question in modern weather forecasting and climate prediction is whether to invest resources in more complex numerical models or in larger ensembles of simulations. If this question is to be addressed quantitatively, then information is needed about how changes in model complexity and ensemble size will affect predictive performance. Information about the effects of ensemble size is often available, but information about the effects of model complexity is much rarer. An illustration is provided of the sort of analysis that might be conducted for the simplified case in which model complexity is judged in terms of grid resolution and ensemble members are constructed only by perturbing their initial conditions. The effects of resolution and ensemble size on the performance of climate simulations are described with a simple mathematical model, which is then used to define an optimal allocation of computational resources for a range of hypothetical prediction problems. The optimal resolution and ensemble size both increase with available resources, but their respective rates of increase depend on the values of two parameters that can be determined from a small number of simulations. The potential for such analyses to guide future investment decisions in climate prediction is discussed.

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Doi, Takeshi, Swadhin K. Behera, and Toshio Yamagata. "Merits of a 108-Member Ensemble System in ENSO and IOD Predictions." Journal of Climate 32, no. 3 (February 2019): 957–72. http://dx.doi.org/10.1175/jcli-d-18-0193.1.

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This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

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Zhu, Jieshun, Bohua Huang, Ben Cash, James L. Kinter, Julia Manganello, Rondrotiana Barimalala, Eric Altshuler, Frederic Vitart, Franco Molteni, and Peter Towers. "ENSO Prediction in Project Minerva: Sensitivity to Atmospheric Horizontal Resolution and Ensemble Size." Journal of Climate 28, no. 5 (February 26, 2015): 2080–95. http://dx.doi.org/10.1175/jcli-d-14-00302.1.

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Abstract This study examines El Niño–Southern Oscillation (ENSO) prediction in Project Minerva, a recent collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The focus is primarily on the impact of the atmospheric horizontal resolution on ENSO prediction, but the effect from different ensemble sizes is also discussed. Particularly, three sets of 7-month hindcasts performed with ECMWF prediction system are compared, starting from 1 May (1 November) during 1982–2011 (1982–2010): spectral T319 atmospheric resolution with 15 ensembles, spectral T639 with 15 ensembles, and spectral T319 with 51 ensembles. The analysis herein shows that simply increasing either ensemble size from 15 to 51 or atmospheric horizontal resolution from T319 to T639 does not necessarily lead to major improvement in the ENSO prediction skill with current climate models. For deterministic prediction skill metrics, the three sets of predictions do not produce a significant difference in either anomaly correlation or root-mean-square error (RMSE). For probabilistic metrics, the increased atmospheric horizontal resolution generates larger ensemble spread, and thus increases the ratio between the intraensemble spread and RMSE. However, there is little change in the categorical distributions of predicted SST anomalies, and consequently there is little difference among the three sets of hindcasts in terms of probabilistic metrics or prediction reliability.

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Mathew, Sunil, Sachin Chacko, Tomy Philip, R. N. Sharma, and Kanniyan Binub. "Platelet count splenic diameter ratio as predictor for esophageal varices in patients with cirrhosis: a diagnostic evaluation study." International Journal of Research in Medical Sciences 7, no. 12 (November 27, 2019): 4535. http://dx.doi.org/10.18203/2320-6012.ijrms20195513.

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Background: Esophageal variceal bleeding is one among the common complication of cirrhosis which is fatal. Latest studies are focusing more on using non-invasive techniques to classify cirrhotic patients according to their risk of having varices. The platelet count-splenic diameter ratio is considered as one such parameter and is used in predicting esophageal varices in patients with cirrhosis. Objectives of the study was to assess the utility of platelet count-splenic diameter ratio as a useful non- invasive parameter in predicting the presence/ absence /size of esophageal varices in patients with cirrhosis.Methods: Diagnostic evaluation study was done in a tertiary hospital of Kerala state India. 93 adults above the age of 18 yrs with diagnosis of cirrhosis was selected and detailed history, physical, systemic examination and imaging was done. The degree of correlation between platelet count-splenic size ratio and the presence/absence/size of esophageal varices was studied along with its utility as an independent non- invasive marker. Frequency was expressed in percentages.Results: Best cut-off for prediction of esophageal varices Grade 1 was platelet count/spleen diameter ratio of 954, which had Specificity of 85.7% and Positive predictive value of 94.1% Cut-off for prediction of Grade 2 esophageal varices was platelet count/spleen diameter ratio of 916 which had a Sensitivity of 78.9%, Specificity of 88.9%. Whereas cut-off for prediction of Grade 3 esophageal varices was a ratio of 899 which had a high Sensitivity of 88% and Negative predictive value of 93.6 % but Specificity was only 64.7% and Positive predictive value of 47.8% only.Conclusions: The platelet count splenic diameter ratio is accurate to be used as screening tool to predict the presence of Grade 2 Esophageal varices in Patients with Cirrhosis. More studies need to be done around the globe for more evidence.

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27

Liu, An, Liang Liu, Dun Zhu Li, and Yun Tao Guan. "Development of Prediction Models for Particle Size Composition on Urban Road Surfaces." Applied Mechanics and Materials 743 (March 2015): 450–57. http://dx.doi.org/10.4028/www.scientific.net/amm.743.450.

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It is commonly known that particles play a critical role in urban stormwater quality because other pollutants can be attached to the particles and transported into receiving waters. Previous research studies have shown a strong relationship between pollutant build-up loads and particle sizes. In this context, accurately estimating the particle amounts in different sizes is extremely important since it can assist in predicting stormwater quality and hence contribute to effective stormwater quality improvement measures. This paper presents a robust model to predict particle size composition on urban road surfaces using heavy-duty vehicle volumes, traffic coefficient and road texture depth by multiple linear regression (MLR) method. The pollutants build-up data was used for model development and was collected on typical urban roads in Shenzhen, China. The relative prediction error and coefficient of variation values were found within the acceptable limits and hence indicated that the developed prediction models are relatively reliable. This developed model can assist in predicting particle size composition on urban road surfaces and thereby contribute to effective stormwater quality assessment and treatment design. Additionally, this developed modelling approach can also provide a guide in terms of particle size composition prediction using more influential factors.

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28

Kosel, Alison E., and Patrick J. Heagerty. "Semi-supervised neighborhoods and localized patient outcome prediction." Biostatistics 20, no. 3 (April 18, 2018): 517–41. http://dx.doi.org/10.1093/biostatistics/kxy015.

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Summary Robust statistical methods that can provide patients and their healthcare providers with individual predictions are needed to help guide informed medical decisions. Ideally an individual prediction would display the full range of possible outcomes (full predictive distribution), would be obtained with a user-specified level of precision, and would be minimally reliant on statistical model assumptions. We propose a novel method that satisfies each of these criteria via the semi-supervised creation of an axis-parallel covariate neighborhood constructed around a given point defining the patient of interest. We then provide non-parametric estimates of the outcome distribution for the subset of subjects in this neighborhood, which we refer to as a localized prediction. We implement local prediction methods using dynamic graphical methods which allow the user to vary key options such as the choice of the variables defining the neighborhood, and the size of the neighborhood.

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29

Brédart, Xavier. "Bankruptcy prediction: Mediating effects of board size." Recherches en Sciences de Gestion N° 138, no. 3 (December 23, 2020): 243–62. http://dx.doi.org/10.3917/resg.138.0243.

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30

Ding, Ning, Shi Qiang Ma, Yu Mei Song, and Long Shan Wang. "Size Prediction Control Modeling in Cylindrical Grinding." Advanced Materials Research 154-155 (October 2010): 977–80. http://dx.doi.org/10.4028/www.scientific.net/amr.154-155.977.

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A dynamic size control model during cylindrical grinding is built. The model consists of Elman neural network, fuzzy control subsystem and deformation optimal adaptive control subsystem. To improve the size prediction accuracy, the first and the second derivative of the actual amount removed from the workpiece are added into the Elman network input; To self-adapt and adjust the quantification factor and scale factor in the fuzzy control, the flexible factor is introduced to the fuzzy control model. Simulation and experiment verify that the developed prediction control model is feasible and has high prediction and control precision.

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31

Liu, Fu Qiang, Sheng Liang Hu, and Pei Kang Bai. "Size Prediction of Carbon-Encapsulated Nickel Nanoparticles." Advanced Materials Research 531 (June 2012): 207–10. http://dx.doi.org/10.4028/www.scientific.net/amr.531.207.

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A simple theoretical model to predict the size control of carbon-encapsulated metal nanoparticles is developed using heat transfer and carbon diffusion theories. Taking carbon-encapsulated nickel nanoparticles as an example, the minimum size of carbon-encapsulated structure that can be formed as a function of the ambient temperature is calculated and the effect of activation energies for carbon diffusion on the size of carbon-encapsulated nickel nanoparticles is examined. The theoretical results are in good agreement with the experiment, suggesting that our model can be used to guide the size-controlled synthesis of carbon-encapsulated metal nanoparticles.

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32

Wang, J., E. Santiago, and A. Caballero. "Prediction and estimation of effective population size." Heredity 117, no. 4 (June 29, 2016): 193–206. http://dx.doi.org/10.1038/hdy.2016.43.

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33

Buizza, R., and T. N. Palmer. "Impact of Ensemble Size on Ensemble Prediction." Monthly Weather Review 126, no. 9 (September 1998): 2503–18. http://dx.doi.org/10.1175/1520-0493(1998)126<2503:ioesoe>2.0.co;2.

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34

van Hoeve, W., B. Dollet, J. M. Gordillo, M. Versluis, L. van Wijngaarden, and D. Lohse. "Bubble size prediction in co-flowing streams." EPL (Europhysics Letters) 94, no. 6 (June 1, 2011): 64001. http://dx.doi.org/10.1209/0295-5075/94/64001.

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35

Kamran, Sophia C., Ellen Marqusee, Mathew I. Kim, Mary C. Frates, Julie Ritner, Hope Peters, Carol B. Benson, et al. "Thyroid Nodule Size and Prediction of Cancer." Journal of Clinical Endocrinology & Metabolism 98, no. 2 (February 2013): 564–70. http://dx.doi.org/10.1210/jc.2012-2968.

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36

Sharma, Anshu, A. N. Goswami, and B. S. Rawat. "Drop size prediction in liquid membrane systems." Journal of Membrane Science 60, no. 2-3 (1991): 261–74. http://dx.doi.org/10.1016/s0376-7388(00)81539-2.

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37

Nieto, Belén, and Gonzalo Rubio. "Volatility Bounds, Size, and Real Activity Prediction*." Review of Finance 18, no. 1 (March 18, 2013): 373–415. http://dx.doi.org/10.1093/rof/rft003.

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38

Weiss, Sholom M., and Nitin Indurkhya. "Selecting the right-size model for prediction." Applied Intelligence 6, no. 4 (October 1996): 261–73. http://dx.doi.org/10.1007/bf00132733.

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39

Varley, J. "Submerged gas-liquid jets: bubble size prediction." Chemical Engineering Science 50, no. 5 (March 1995): 901–5. http://dx.doi.org/10.1016/0009-2509(94)00212-a.

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40

Prikhodovsky, Andrey. "Prediction of the size distribution of precipitates." Steel Research 72, no. 11-12 (November 2001): 512–17. http://dx.doi.org/10.1002/srin.200100160.

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41

Ruff, Christopher. "Body size prediction from juvenile skeletal remains." American Journal of Physical Anthropology 133, no. 1 (May 2007): 698–716. http://dx.doi.org/10.1002/ajpa.20568.

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42

Nan, Jinrui, Bo Deng, Wanke Cao, and Zihao Tan. "Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window." World Electric Vehicle Journal 13, no. 2 (January 19, 2022): 25. http://dx.doi.org/10.3390/wevj13020025.

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Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm.

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43

Ren, Ye, Xiao Hui Li, Yan Rong Xue, and Yu Wei Li. "Combination Model for UTC-UTC (NTSC) Time Offset Long-Term Prediction and Precision Evaluation." Advanced Materials Research 718-720 (July 2013): 1341–45. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1341.

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Coordinated Universal Time (UTC) is regarded as reference time scale for the worldwide time coordination. In order to steer master clock in timekeeping laboratory, it is necessary to make a prediction of UTC-UTC(Lab) time offset for 15~45 days. The prediction is based on two considerations, data size and prediction model. Compared with quadratic polynomial model and grey model, calculating results show a combination model based on 9 months data size is fitter to secular predicting in terms of both stability and accuracy.

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44

Jackson, Wendy M., Sievert Rohwer, and Robin L. Winnegrad. "Status Signaling Is Absent within Age-and-Sex Classes of Harris' Sparrows." Auk 105, no. 3 (July 1, 1988): 424–49. http://dx.doi.org/10.1093/auk/105.3.424.

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Abstract We tested the status-signaling hypothesis in two groups of same-age and same-sex Harris' Sparrows (Zonotrichia querula). Unlike flocks of mixed age and sex composition, badge size did not correlate with social status in these groups; thus, status signaling does not appear to occur within age-and-sex classes of Harris' Sparrows. Other predictions of the status-signaling hypothesis we tested were that (1) fighting ability and social status should be correlated, and (2) fighting ability and badge size should be correlated. We used a multivariate assessment of body size as an indicator of fighting ability and found no support for either prediction in the flock of adult females. In the flock of adult males, large birds were more dominant (Prediction 1) but fighting ability and badge size were not correlated (contra Prediction 2).

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45

Park, No-Wook, and Dong-Ho Jang. "Comparison of Geostatistical Kriging Algorithms for Intertidal Surface Sediment Facies Mapping with Grain Size Data." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/145824.

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This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.

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46

Livingston, Edward H., and Scott Lee. "Body surface area prediction in normal-weight and obese patients." American Journal of Physiology-Endocrinology and Metabolism 281, no. 3 (September 1, 2001): E586—E591. http://dx.doi.org/10.1152/ajpendo.2001.281.3.e586.

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None of the equations frequently used to predict body surface area (BSA) has been validated for obese patients. We applied the principles of body size scaling to derive an improved equation predicting BSA solely from a patient's weight. Forty-five patients weighing from 51.3 to 248.6 kg had their height and weight measured on a calibrated scale and their BSA calculated by a geometric method. Data were combined with a large series of published BSA estimates. BSA prediction with the commonly used Du Bois equation underestimated BSA in obese patients by as much as 20%. The equation we derived to relate BSA to body weight was a power function: BSA (m2) = 0.1173 × Wt (kg)0.6466. Below 10 kg, this equation deviated significantly from the BSA vs. body weight curve, necessitating a different set of coefficients: BSA (m2) = 0.1037 × Wt (kg)0.6724. Covariance of height and weight for patients weighing <80 kg reduced the Du Bois BSA-predicting equation to a power function, explaining why it provides good BSA predictions for normal-size patients but fails with obesity.

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47

OSCAR, THOMAS P. "Extrapolation of a Predictive Model for Growth of a Low Inoculum Size of Salmonella Typhimurium DT104 on Chicken Skin to Higher Inoculum Sizes†." Journal of Food Protection 74, no. 10 (October 1, 2011): 1630–38. http://dx.doi.org/10.4315/0362-028x.jfp-11-127.

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Validation of model predictions for independent variables not included during model development can save time and money by identifying conditions for which new models are not needed. A single strain of Salmonella Typhimurium DT104 was used to develop a general regression neural network (GRNN) model for growth of a low inoculum size (0.9 log) on chicken skin with native microflora as a function of time (0 to 8 h) and temperature (20 to 45°C). The ability of the GRNN model to predict growth of higher inoculum sizes (2, 3, or 4.1 log) was evaluated. When the proportion of residuals in an acceptable prediction zone (pAPZ) from −1 log (fail-safe) to 0.5 log (fail-dangerous) was ≥0.7, the GRNN model was classified as providing acceptable predictions of the test data. The pAPZ for dependent data was 0.93 and for independent data for interpolation was 0.88. The pAPZs for extrapolation to higher inoculum sizes of 2, 3, or 4.1 log were 0.92, 0.73, and 0.77, respectively. However, residual plots indicated local prediction problems with pAPZs of <0.7 for an inoculum size of 3 log at 30, 35, and 40°C and for an inoculum size of 4.1 log at 35 and 40°C where predictions were fail-dangerous, indicating faster growth at higher inoculum sizes. The model provided valid predictions of Salmonella Typhimurium DT104 growth on chicken skin from inoculum sizes of 0.9 and 2 log at all temperatures investigated and from inoculum sizes of 3 and 4.1 log at some but not all temperatures investigated. Thus, the model can be improved by including inoculum size as an independent variable.

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48

Vauhkonen, Jari, and Lauri Mehtätalo. "Matching remotely sensed and field-measured tree size distributions." Canadian Journal of Forest Research 45, no. 3 (March 2015): 353–63. http://dx.doi.org/10.1139/cjfr-2014-0285.

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Undetected trees and inaccuracies in the predicted allometric relationships of tree stem attributes seriously constrain single-tree remote sensing of seminatural forests. A new approach to compensate for these error sources was developed by applying a histogram matching technique to map the transformation between the cumulative distribution functions of crown radii extracted from airborne laser scanning (ALS) data and field-measured stem diameters (dbh, outside bark measured at 1.3 m aboveground). The ALS-based crown data were corrected for the censoring effect caused by overlapping tree crowns, assuming that the forest is an outcome of a homogeneous, marked Poisson process with independent marks of the crown radii. The transformation between the cumulative distribution functions was described by a polynomial regression model. The approach was tested for the prediction of plot-level stem number (N), quadratic mean diameter (DQM), and basal area (G) in a managed boreal forest. Of the 40 plots studied, a total of 18 plots met the assumptions of the Poisson process and independent marks. In these plots, the predicted N, DQM, and G had best-case root mean squared errors of 299 stems·ha−1 (27.6%), 2.1 cm (13.1%), and 2.9 m2·ha−1 (13.0%), respectively, and the null hypothesis that the mean difference between the measured and predicted values was 0 was not rejected (p > 0.05). Considerably less accurate results were obtained for the plots that did not meet the assumptions. However, the goodness-of-fit of the predicted diameter distribution was especially improved compared with the single-tree remote sensing prediction, and observations realistically obtainable with ALS data showed potential to further localize the predictions. Remarkably, predictions of N showing no evidence against zero bias were derived solely based on the ALS data for the plots meeting the assumptions made, and limited training data are proposed to be adequate for predicting the stem diameter distribution, DQM, and G. Although this study was based on ALS data, we discuss the possibility of using other remotely sensed data as well. Taken together with the low requirements for field reference data, the presented approach provides interesting practical possibilities that are not typically proposed in the forest remote sensing literature.

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49

Paydar, Z., and HP Cresswell. "Water retention in Australian soils. 2.* Prediction using particle size, bulk density, and other properties." Soil Research 34, no. 5 (1996): 679. http://dx.doi.org/10.1071/sr9960679.

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Different approaches were investigated for estimating the parameters in the Campbell soil water characteristic (SWC) equation from soil attributes such as particle size distribution (PSD), bulk density, and organic matter content. Predicted soil water characteristics were compared with measured values for soils of the wheatbelt of south-eastern Australia. A method of prediction is proposed incorporating an empirical relationship for estimating the slope of the SWC from the slope of the cumulative PSD. A power-law form is assumed for both the SWC and PSD functions. One measured SWC point is then used to locate and thus define the SWC curve. When SWC points predicted with this 'one-point' method were compared with measured values, the mean absolute value of the difference between each measured and predicted SWC point was 0.016 m3/m3 for the Geeves data and 0.027 m3/m3 for the Forrest data. Eight sets of predictive equations, previously developed using multiple regression analysis, were also evaluated. Whilst the equations predicted the slope of the SWC curves reasonably well, predictions of the air entry potential were poor. Although less accurate, the equations developed by multiple regression are less demanding in data requirement compared with alternative SWC prediction methods. The one-point method gave better predictions than the multiple regression approach but was less accurate than the 'two-point' method proposed in the first paper in this series. The one-point method should be considered where PSD data and 1 measured SWC point are available. In most other circumstances it will be more accurate and cost-effective to measure 2 SWC points to define the soil water characteristic function (the two-point method).* Part I, Aust. J. Soil Res., 1996, 34, 195–212.

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50

Fagerberg, N., S. Seifert, T. Seifert, P. Lohmander, A. Alissandrakis, B. Magnusson, J. Bergh, S. Adamopoulos, and M. K. F. Bader. "Prediction of knot size in uneven-sized Norway spruce stands in Sweden." Forest Ecology and Management 544 (September 2023): 121206. http://dx.doi.org/10.1016/j.foreco.2023.121206.

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