scholarly journals Testing the seismic quiescence hypothesis through retrospective trials of alarm-based earthquake prediction in the Kurile–Japan subduction zone

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Kei Katsumata ◽  
Masao Nakatani
Keyword(s):  
Subduction Zone ◽  
Cross Validation ◽  
Large Earthquake ◽  
Detailed Comparison ◽  
Seismic Quiescence ◽  
Validation Test ◽  
Forecast Models ◽  
Japan Subduction Zone ◽  
The Cross ◽  
Validation Tests

AbstractWe make trial binary forecasts for the Kurile–Japan subduction zone for the period 1988–2014 by hypothesizing that seismic quiescence (i.e., the absence of earthquakes of M ≥ 5 for a minimum period of Tq) is a precursor of a large (7.5 ≤ Mw < 8.5) earthquake in the coming period Ta within a radius R of the quiescence. We evaluate the receiver-operating-characteristic diagram constructed using a range of forecast models specified by (Tq, R, Ta). A forecast experiment targeting eight large earthquakes in the studied spacetime suggests that the risk of a large earthquake is modestly (probability gain G ~ 2) but significantly (p-value less than 5%) heightened for several years following a long quiescent period of Tq ≥ 9 years, within several tens of kilometers of the quiescence. We then attempt cross-validation, where we use half the data for training [i.e., optimization of (Tq, R, Ta)] and the remaining half for evaluation. With only four target earthquakes available for evaluation of the forecasts in each of the learning and evaluation periods, our forecast scheme did not pass the cross-validation test (with a criterion that the p-value is less than 5%). Hence, we cannot formally deny the possibility that our positive results for the overall period are a ghost arising from over-fitting. However, through detailed comparison of optimal models in the overall test with those in the cross-validation tests, we argue that severe over-fitting is unlikely involved for the modest G of ~ 2 obtained in the overall test. There is thus a reasonable chance that the presently tested type of quiescence will pass the cross-validation test when more target earthquakes become available in the near future. In the meantime, we find that G improves to ~ 5 when target earthquakes are limited to 8 ≤ Mw < 8.5, though we cannot say anything about the possible involvement of over-fitting because we have only three such very large target earthquakes.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Comparison Between Experiments and a Multibody Weakly Nonlinear Potential Flow Approach for Modeling of Marine Operations

2018 ◽  
Author(s):  
Pierre-Yves Wuillaume ◽  
Pierre Ferrant ◽  
Aurélien Babarit ◽  
François Rongère ◽  
Mattias Lynch ◽  
...  
Keyword(s):  
Potential Flow ◽  
Wave Structure ◽  
Body Motion ◽  
Dynamics Modeling ◽  
Flow Solver ◽  
Weakly Nonlinear ◽  
Validation Test ◽  
Nonlinear Potential ◽  
Set Up ◽  
Validation Tests

This paper presents validation tests for a new numerical tool for the numerical simulation of marine operations. It involves multibody dynamics modeling, wave-structure interactions with large amplitude body motion and cable’s dynamic modeling. Hydrodynamic loads are computed using the WS_CN weakly nonlinear potential flow solver, based on the weak-scatterer hypothesis. Large deformation of the wetted body surfaces can be taken into account. Firstly the ECN’s WS_CN solver capabilities are extended to multibody simulations. A first validation test is performed by comparing numerical results to the experimental data of [1]. Then, a second validation test is proposed. It consists in the ballasting operation of a spar. The experimental set-up is described.

scholarly journals Leaf geometric morphometrics among populations of Dalbergia ecastaphyllum (L.) Taub.

2019 ◽  
Vol 35 (6) ◽  
Author(s):  
Daniel Vieira de Morais ◽  
Lorena Andrade Nunes ◽  
Vandira Pereira da Mata ◽  
Maria Angélica Pereira de Carvalho Costa ◽  
Geni da Silva Sodré ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Geometric Morphometrics ◽  
Cross Validation ◽  
Abiotic Factors ◽  
Principal Component ◽  
Canonical Variate ◽  
Validation Test ◽  
Significant Difference ◽  
Variate Analysis ◽  
Geographical Distances

Leaves are plant structures that express important traits of the environment where they live. Leaf description has allowed identification of plant species as well as investigation of abiotic factors effects on their development, such as gases, light, temperature, and herbivory. This study described populations of Dalbergia ecastaphyllum through leaf geometric morphometrics in Brazil. We evaluated 200 leaves from four populations. The principal component analysis (PCA) showed that the first four principal components were responsible for 97.81% of variation. The non-parametric multivariate analysis of variance (NPMANOVA) indicated significant difference between samples (p = 0.0001). The Mentel test showed no correlation between geographical distances and shape. The canonical variate analysis (CVA) indicated that the first two variables were responsible for 96.77 % of total variation, while the cross-validation test showed an average of 83.33%. D. ecastaphyllum leaves are elliptical and ovate.

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

scholarly journals Development of multistage 10-m shuttle run test for VO2max estimation in healthy adults

2021 ◽  
Keyword(s):  
Cross Validation ◽  
Age Groups ◽  
Healthy Adults ◽  
Coefficient Of Determination ◽  
Validation Test ◽  
Uniform Distributions ◽  
Gender And Age ◽  
Run Test ◽  
Shuttle Run ◽  
Spatial Limitation

Background and objective: The disadvantage of the traditional 20-m multistage shuttle run test (MST) is that it requires a long space for measurements and does not include various age groups to develop the test. Therefore, we developed a new MST to improve the spatial limitation by reducing the measurement to a 10-m distance and to resolve the bias via uniform distributions of gender and age. Material and methods: Study subjects included 120 healthy adults (60 males and 60 females) aged 20 to 50 years. All subjects performed a graded maximal exercise test (GXT) and a 10-m MST at five-day intervals. We developed a regression model using 70% of the subject's data and performed a cross-validation test using 30% of the data. Results: The male regression model's coefficient of determination (R2) was 58.8%, and the standard error of estimation (SEE) was 4.17 mL/kg/min. The female regression model's R2 was 69.2%, and the SEE was 3.39 mL/kg/min. The 10-m MST showed a high correlation with GXT on the VO2max (males: 0.816; females: 0.821). In the cross-validation test for the developed regression models, the male's SEE was 4.38 mL/kg/min, and the female's SEE was 4.56 mL/kg/min. Conclusion: Thus, the 10-m MST is an accurate and valid method for estimating the VO2max. Therefore, the 10-m MST developed by us can be used when the existing 20-m MST cannot be used due to spatial limitations and can be applied to both men and women in their 20s and 50s.

Artificial Intelligence–Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors

2021 ◽  
pp. 459-468
Author(s):  
Fatma Güntürkün ◽  
Oguz Akbilgic ◽  
Robert L. Davis ◽  
Gregory T. Armstrong ◽  
Rebecca M. Howell ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Clinical Features ◽  
Cross Validation ◽  
Childhood Cancer Survivors ◽  
Model Based ◽  
Extreme Gradient Boosting ◽  
The Cross

PURPOSE Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline–recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

Development of a dual-energy X-ray absorptiometry-derived body volume equation in Hispanic adults for administering a four-compartment model

2020 ◽  
Vol 123 (12) ◽  
pp. 1373-1381 ◽  
Author(s):  
Brett S. Nickerson ◽  
Michael V. Fedewa ◽  
Cherilyn N. McLester ◽  
John R. McLester ◽  
Michael R. Esco
Keyword(s):  
Cross Validation ◽  
Dual Energy ◽  
Body Volume ◽  
Main Body ◽  
Validation Sample ◽  
Body Composition Assessment ◽  
X Ray ◽  
Hispanic Females ◽  
Hispanic Adults ◽  
The Cross

AbstractThe purpose of the present study was: (1) to develop a new dual-energy X-ray absorptiometry (DXA)-derived body volume (BV) equation with the GE-Lunar prodigy while utilising underwater weighing (UWW) as a criterion and (2) to cross-validate the novel DXA-derived BV equation (4C-DXANickerson), Wilson DXA-derived BV equation (4C-DXAWilson) and air displacement plethysmography (ADP)-derived BV (4C-ADP) in Hispanic adults. A total of 191 Hispanic adults (18–45 years) participated in the present study. The development sample consisted of 120 females and males (50 % females), whereas the cross-validation sample comprised of forty-one females and thirty males (n 71). Criterion body fat percentage (BF %) and fat-free mass (FFM) were determined using a four-compartment (4C) model with UWW as a criterion for BV (4C-UWW). 4C-DXANickerson, 4C-DXAWilson and 4C-ADP were compared against 4C-UWW in the cross-validation sample. 4C-DXANickerson, 4C-DXAWilson and 4C-ADP all produced similar validity statistics when compared with 4C-UWW in Hispanic males (all P > 0·05). 4C-DXANickerson also yielded similar BF % and FFM values as 4C-UWW when evaluating the mean differences (constant error (CE)) in Hispanic females (CE = –0·79 % and 0·38 kg; P = 0·060 and 0·174, respectively). However, 4C-DXAWilson produced significantly different BF % and FFM values (CE = 3·22 % and –2·20 kg, respectively; both P < 0·001). Additionally, 4C-DXAWilson yielded significant proportional bias when estimating BF % (P < 0·001), whereas 4C-ADP produced significant proportional bias for BF % and FFM (both P < 0·05) when evaluated in Hispanic females. The present study findings demonstrate that 4C-DXANickerson is a valid measure of BV in Hispanics and is recommended for use in clinics, where DXA is the main body composition assessment technique.

Sign in / Sign up

Export Citation Format

Share Document