scholarly journals ROUGHNESS FACTOR IN OVERTOPPING ESTIMATION

2017 ◽  
pp. 7
Author(s):  
JOSEP RAMON MEDINA ◽  
JORGE MOLINES
Keyword(s):  
Neural Network ◽  
Confidence Interval ◽  
Roughness Factor ◽  
Empirical Formulas ◽  
Wave Overtopping ◽  
Explanatory Variables ◽  
Factor I ◽  
Interval Width ◽  
Confidence Interval Width

The roughness factor (γf) is a key variable to estimate wave overtopping discharge on mound breakwaters. In this study, the γf is re-calibrated using a dataset extracted from the CLASH database. Compared to previous roughness factors calibrated using less restrictive data, overtopping estimators with a few explanatory variables showed variations up to 15% in the 50% percentile of γf. On the contrary, the CLASH neural network overtopping predictor showed insignificant variations in the roughness factor, since it is less sensitive to the variability in the data used for calibration. The confidence interval width of the CLASH neural network was narrow compared to simple explicit overtopping estimators, given that it is less sensitive to the number of data used for calibration. The γf values used to estimate wave overtopping discharge should be carefully calibrated, especially when using simple empirical formulas.

scholarly journals Meta-analysis of the clinical performance of commercial SARS-CoV-2 nucleic acid, antigen and antibody tests up to 22 August 2020

2020 ◽  
Author(s):  
Ivo Van Walle ◽  
Katrin Leitmeyer ◽  
Eeva K Broberg ◽  
Keyword(s):  
Nucleic Acid ◽  
Confidence Interval ◽  
Clinical Performance ◽  
Point Of Care ◽  
Test Results ◽  
Interval Width ◽  
Point Of Care Tests ◽  
Antibody Tests ◽  
Confidence Interval Width ◽  
Nucleic Acid Tests

We reviewed the clinical performance of SARS-CoV-2 nucleic acid, viral antigen and antibody tests based on 94739 test results from 157 published studies and 20205 new test results from 12 EU/EEA Member States. Pooling the results and considering only results with 95% confidence interval width ≤5%, we found 4 nucleic acid tests, among which 1 point of care test, and 3 antibody tests with a clinical sensitivity ≤95% for at least one target population (hospitalised, mild or asymptomatic, or unknown). Analogously, 9 nucleic acid tests and 25 antibody tests, among which 12 point of care tests, had a clinical specificity of ≤98%. Three antibody tests achieved both thresholds. Evidence for nucleic acid and antigen point of care tests remains scarce at present, and sensitivity varied substantially. Study heterogeneity was low for 8/14 (57.1%) sensitivity and 68/84 (81.0%) specificity results with confidence interval width ≤5%, and lower for nucleic acid tests than antibody tests. Manufacturer reported clinical performance was significantly higher than independently assessed in 11/32 (34.4%) and 4/34 (11.8%) cases for sensitivity and specificity respectively, indicating a need for improvement in this area. Continuous monitoring of clinical performance within more clearly defined target populations is needed.

Sample size adjustment to reduce the probability of exceeding a specified confidence interval width

1992 ◽  
Vol 68 (6) ◽  
pp. 747-751
Author(s):  
Peter L. Marshall ◽  
Valerie M. LeMay ◽  
Albert Nussbaum
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Sampling Error ◽  
Simple Random Sampling ◽  
Significance Level ◽  
Interval Width ◽  
Population Dispersion ◽  
Size Adjustment ◽  
Sample Size Adjustment ◽  
Confidence Interval Width

The maximum confidence interval width desired by a sampler will be exceeded about half the time if sample size is determined using a formula that does not account for variability in the estimate of population dispersion. This probability can be decreased by increasing sample size; however, determining how much to increase sample size can be tedious. A series of graphs is presented that can be used to quickly determine the percentage adjustment for unadjusted sample sizes less than 250 and a significance level of 0.05, assuming simple random sampling with replacement. The benefit of acquiring precise estimates of population dispersion, when it is important not to exceed a specified sampling error level, is clearly demonstrated by comparing the graphs.

scholarly journals Meta-analysis of the clinical performance of commercial SARS-CoV-2 nucleic acid and antibody tests up to 22 August 2020

2021 ◽  
Vol 26 (45) ◽  
Author(s):  
Ivo Van Walle ◽  
Katrin Leitmeyer ◽  
Eeva K Broberg ◽  
Keyword(s):  
Nucleic Acid ◽  
Confidence Interval ◽  
Clinical Performance ◽  
Point Of Care ◽  
Test Results ◽  
Interval Width ◽  
Point Of Care Tests ◽  
Antibody Tests ◽  
Confidence Interval Width ◽  
Nucleic Acid Tests

Background Reliable testing for SARS-CoV-2 is key for the management of the COVID-19 pandemic. Aim We estimate diagnostic accuracy for nucleic acid and antibody tests 5 months into the COVID-19 pandemic, and compare with manufacturer-reported accuracy. Methods We reviewed the clinical performance of SARS-CoV-2 nucleic acid and antibody tests based on 93,757 test results from 151 published studies and 20,205 new test results from 12 countries in the European Union and European Economic Area (EU/EEA). Results Pooling the results and considering only results with 95% confidence interval width ≤ 5%, we found four nucleic acid tests, including one point-of-care test and three antibody tests, with a clinical sensitivity ≥ 95% for at least one target population (hospitalised, mild or asymptomatic, or unknown). Nine nucleic acid tests and 25 antibody tests, 12 of them point-of-care tests, had a clinical specificity of ≥ 98%. Three antibody tests achieved both thresholds. Evidence for nucleic acid point-of-care tests remains scarce at present, and sensitivity varied substantially. Study heterogeneity was low for eight of 14 sensitivity and 68 of 84 specificity results with confidence interval width ≤ 5%, and lower for nucleic acid tests than antibody tests. Manufacturer-reported clinical performance was significantly higher than independently assessed in 11 of 32 and four of 34 cases, respectively, for sensitivity and specificity, indicating a need for improvement in this area. Conclusion Continuous monitoring of clinical performance within more clearly defined target populations is needed.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

scholarly journals Wave Overtopping Database As the Starting Point for a Neural Network Prediction Method

2004 ◽  
Author(s):  
H. Verhaeghe ◽  
J. W. van der Meer ◽  
G.-J. Steendam ◽  
P. Besley ◽  
L. Franco ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wave Overtopping ◽  
Starting Point ◽  
Neural Network Prediction ◽  
Network Prediction

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

2018 ◽  
Vol 233 (10) ◽  
pp. 2467-2478 ◽  
Author(s):  
Radhika Raveendran ◽  
Apoorva Suresh ◽  
Vignesh Rajaram ◽  
Shankar C Subramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confidence Interval ◽  
Prediction Error ◽  
Performance Degradation ◽  
Dynamics Simulation ◽  
Brake System ◽  
Road Vehicles ◽  
Prediction Scheme ◽  
Artificial Neural

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

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