scholarly journals Estimation of Heat-Attributable Mortality Using the Cross-Validated Best Temperature Metric in Switzerland and South Korea

2021 ◽  
Vol 18 (12) ◽  
pp. 6413
Author(s):  
Jae Young Lee ◽  
Martin Röösli ◽  
Martina S. Ragettli
Keyword(s):  
South Korea ◽  
Minimum Temperature ◽  
Cross Validation ◽  
Specific Model ◽  
Apparent Temperature ◽  
Maximum Temperature ◽  
Attributable Mortality ◽  
Validation Method ◽  
Attributable Fractions ◽  
The Cross

This study presents a novel method for estimating the heat-attributable fractions (HAF) based on the cross-validated best temperature metric. We analyzed the association of eight temperature metrics (mean, maximum, minimum temperature, maximum temperature during daytime, minimum temperature during nighttime, and mean, maximum, and minimum apparent temperature) with mortality and performed the cross-validation method to select the best model in selected cities of Switzerland and South Korea from May to September of 1995–2015. It was observed that HAF estimated using different metrics varied by 2.69–4.09% in eight cities of Switzerland and by 0.61–0.90% in six cities of South Korea. Based on the cross-validation method, mean temperature was estimated to be the best metric, and it revealed that the HAF of Switzerland and South Korea were 3.29% and 0.72%, respectively. Furthermore, estimates of HAF were improved by selecting the best city-specific model for each city, that is, 3.34% for Switzerland and 0.78% for South Korea. To the best of our knowledge, this study is the first to observe the uncertainty of HAF estimation originated from the selection of temperature metric and to present the HAF estimation based on the cross-validation method.

Identifying protein subcellular location with embedding features learned from networks

2020 ◽  
Vol 17 ◽  
Author(s):  
Hongwei Liu ◽  
Bin Hu ◽  
Lei Chen ◽  
Lin Lu
Keyword(s):  
Computational Methods ◽  
Protein Function ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Subcellular Location ◽  
Support Vector ◽  
Network Embedding ◽  
Validation Method ◽  
Protein Subcellular Location ◽  
The Cross

Background: Identification of protein subcellular location is an important problem because the subcellular location is highly related to protein function. It is fundamental to determine the locations with biology experiments. However, these experiments are of high costs and time-consuming. The alternative way to address such problem is to design effective computational methods. Objective: To date, several computational methods have been proposed in this regard. However, these methods mainly adopted the features derived from proteins themselves. On the other hand, with the development of network technique, several embedding algorithms have been proposed, which can encode nodes in the network into feature vectors. Such algorithms connected the network and traditional classification algorithms. Thus, they provided a new way to construct models for the prediction of protein subcellular location. Method: In this study, we analyzed features produced by three network embedding algorithms (DeepWalk, Node2vec and Mashup) that were applied on one or multiple protein networks. Obtained features were learned by one machine learning algorithm (support vector machine or random forest) to construct the model. The cross-validation method was adopted to evaluate all constructed models. Results: After evaluating models with the cross-validation method, embedding features yielded by Mashup on multiple networks were quite informative for predicting protein subcellular location. The model based on these features were superior to some classic models. Conclusion: Embedding features yielded by a proper and powerful network embedding algorithm were effective for building the model for prediction of protein subcellular location, providing new pipelines to build more efficient models.

scholarly journals Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation

2015 ◽  
Vol 9 (1) ◽  
pp. 107-114
Author(s):  
Zhou Shengquan ◽  
Zhao Xiaolong ◽  
Yao Zhaoming
Keyword(s):  
Prediction Model ◽  
Kernel Function ◽  
Cross Validation ◽  
Small Sample ◽  
Support Vector ◽  
Cauchy Kernel ◽  
Deep Soil ◽  
Deep Foundation ◽  
Validation Method ◽  
The Cross

In order to forecast the displacement of deep foundation pit support, this document proposes a new method which combines the cross validation method and supports vector machine (SVM) based on random small samples. Because the random small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function of support vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model of underground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that this method can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In the aspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practical engineering.

Wind Speed and Direction on Water Application Uniformity of a Mechanical Lateral-Move Irrigation System

2017 ◽  
Vol 33 (4) ◽  
pp. 543-549 ◽  
Author(s):  
Bernardo Gomes Nörenberg ◽  
Lessandro Coll Faria ◽  
Osvaldo Rettore Neto ◽  
Samuel Beskow ◽  
Alberto Colombo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Irrigation System ◽  
Field Tests ◽  
Sprinkler Irrigation ◽  
Support Tool ◽  
Validation Method ◽  
Wind Speeds ◽  
Statistical Measures ◽  
The Cross

Abstract. In order to develop models for representation of Christiansen’s Uniformity (CU) and Distribution Uniformity (DU) as a function of wind speed, 32 in-field tests evaluating a mechanical lateral-move irrigation system, used in rice production, were carried out in southern Rio Grande do Sul, Brazil. These tests were used to generate two third-order polynomial models for estimation of CU and DU, which were then validated based on a cross-validation approach. The generated models had their accuracy quantified by means of the following statistical measures: determination coefficient (R2), reliability and performance index (c), root mean square error (RMSE), and Nash-Sutcliffe coefficient (CNS). Wind direction had no significant influence on CU and DU. The CU values estimated from the cross-validation method were compared to those observed, resulting in R2 = 0.44, c = 0.53, RMSE = 1.82%, and CNS = 0.43. Likewise, DU values estimated from the cross-validation method were compared to the observed values, culminating in R2, c, RMSE, and CNS equal to 0.41%, 0.51%, 2.81% and 0.40%, respectively. The models developed in this study can be useful as a support tool for decision making when applying mechanical lateral-move irrigation systems, allowing estimation of CU and DU values with satisfactory precision for wind speeds less than 5.5 m s-1. Keywords: In-field tests, Rice, Sprinkler irrigation.

scholarly journals Development of an experiment-split method for benchmarking the generalization of a PTM site predictor: Lysine methylome as an example

2021 ◽  
Vol 17 (12) ◽  
pp. e1009682
Author(s):  
Guoyang Zou ◽  
Yang Zou ◽  
Chenglong Ma ◽  
Jiaojiao Zhao ◽  
Lei Li
Keyword(s):  
Experimental Data ◽  
Cross Validation ◽  
Post Translational Modification ◽  
Validation Method ◽  
Split Method ◽  
Split Test ◽  
Estimate Method ◽  
Practical Performance ◽  
The Cross ◽  
Different Sources

Many computational classifiers have been developed to predict different types of post-translational modification sites. Their performances are measured using cross-validation or independent test, in which experimental data from different sources are mixed and randomly split into training and test sets. However, the self-reported performances of most classifiers based on this measure are generally higher than their performances in the application of new experimental data. It suggests that the cross-validation method overestimates the generalization ability of a classifier. Here, we proposed a generalization estimate method, dubbed experiment-split test, where the experimental sources for the training set are different from those for the test set that simulate the data derived from a new experiment. We took the prediction of lysine methylome (Kme) as an example and developed a deep learning-based Kme site predictor (called DeepKme) with outstanding performance. We assessed the experiment-split test by comparing it with the cross-validation method. We found that the performance measured using the experiment-split test is lower than that measured in terms of cross-validation. As the test data of the experiment-split method were derived from an independent experimental source, this method could reflect the generalization of the predictor. Therefore, we believe that the experiment-split method can be applied to benchmark the practical performance of a given PTM model. DeepKme is free accessible via https://github.com/guoyangzou/DeepKme.

scholarly journals North American historical monthly spatial climate dataset, 1901–2016

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heather MacDonald ◽  
Daniel W. McKenney ◽  
Pia Papadopol ◽  
Kevin Lawrence ◽  
John Pedlar ◽  
...  
Keyword(s):  
North American ◽  
Minimum Temperature ◽  
Cross Validation ◽  
Spatial Models ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Prediction Errors ◽  
The North ◽  
Temperature And Precipitation ◽  
Predictive Error

AbstractWe present historical monthly spatial models of temperature and precipitation generated from the North American dataset version “j” from the National Oceanic and Atmospheric Administration’s (NOAA’s) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901–2016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94 °C for maximum temperature models, 1.3 °C for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01 °C, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5 mm or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7 °C for maximum temperature, 1.02 °C for minimum temperature, and 13.3 mm (19.3% of the surface mean) for monthly precipitation.

scholarly journals Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation

2015 ◽  
Vol 9 (1) ◽  
pp. 53-60
Author(s):  
Zhou Shengquan ◽  
Zhao Xiaolong ◽  
Yao Zhaoming
Keyword(s):  
Prediction Model ◽  
Kernel Function ◽  
Cross Validation ◽  
Small Sample ◽  
Support Vector ◽  
Cauchy Kernel ◽  
Deep Soil ◽  
Deep Foundation ◽  
Validation Method ◽  
The Cross

In order to forecast the displacement of deep foundation pit support, this document proposes a new method which combines the cross validation method and supports vector machine (SVM) based on random small samples.Because the random small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function of support vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model of underground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that this method can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In the aspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practical engineering.

Statistical downscaling of general circulation model outputs to evaporation, minimum temperature and maximum temperature using a key-predictand and key-station approach

2014 ◽  
Vol 6 (2) ◽  
pp. 241-262 ◽  
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. F. Barton ◽  
B. J. C. Perera
Keyword(s):  
Linear Regression ◽  
General Circulation Model ◽  
Minimum Temperature ◽  
General Circulation ◽  
Cross Correlation ◽  
Circulation Model ◽  
Maximum Temperature ◽  
Correlation Structures ◽  
The Cross ◽  
Highly Correlated

A key-predictand and key-station approach was employed in downscaling general circulation model outputs to monthly evaporation, minimum temperature (Tmin) and maximum temperature (Tmax) at five observation stations concurrently. Tmax was highly correlated (magnitudes above 0.80 at p ≤ 0.05) with evaporation and Tmin at each individual station, hence Tmax was identified as the key predictand. One station was selected as the key station, as Tmax at that station showed high correlations with evaporation, Tmin and Tmax at all stations. Linear regression relationships were developed between the key predictand at the key station and evaporation, Tmin and Tmax at all stations using observations. A downscaling model was developed at the key station for Tmax. Then, outputs of this downscaling model at the key station were introduced to the linear regression relationships to produce projections of monthly evaporation, Tmin and Tmax at all stations. This key-predictand and key-station approach was proved to be effective as the statistics of the predictands simulated by this approach were in close agreement with those of observations. This simple multi-station multivariate downscaling approach enabled the preservation of the cross-correlation structures of each individual predictand among the stations and also the cross-correlation structures between different predictands at individual stations.

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