A Weighted EFOR Algorithm for Dynamic Parametrical Model Identification of the Nonlinear System

In this paper, the Nonlinear Auto-Regressive with exogenous inputs (NARX) model with parameters of interest for design (NARX-M-for-D), where the design parameter of the system is connected to the coefficients of the NARX model by a predefined polynomial function is studied. For the NARX-M-for-D of nonlinear systems, in practice, to predict the output by design parameter values are often difficult due to the uncertain relationship between the design parameter and the coefficients of the NARX model. To solve this issue and conduct the analysis and design, an improved algorithm, defined as the Weighted Extended Forward Orthogonal Regression (WEFOR), is proposed. Firstly, the initial NARX-M-for-D is obtained through the traditional Extended Forward Orthogonal Regression (EFOR) algorithm. Then a weight matrix is introduced to modify the polynomial functions with respect to the design parameter, and then an improved model, which is referred to as the final NARX-M-for-D is established. The genetic algorithm (GA) is used for deriving the weight matrix by minimizing the normalized mean square error (NMSE) over the data sets corresponding to the design parameter values used for modeling and first prediction. Finally, both the numerical and experimental studies are conducted to demonstrate the application of the WEFOR algorithm. The results indicate that the final NARX-M-for-D can accurately predict the system output of a nonlinear system. The new algorithm is expected to provide a reliable model for dynamic analysis and design of the nonlinear system.

A homogeneous earthquake catalogue for Turkey

A new homogenized earthquake catalogue for Turkey is compiled for the period 1900–2018. The earthquake parameters are obtained from the Bulletin of International Seismological Centre that was fully updated in 2020. New conversion equations between moment magnitude and the other scales (md, ML, mb, Ms, and M) are determined using the general orthogonal regression method to build up a homogeneous catalogue, which is the essential database for seismic hazard studies. The 95 % confidence intervals are estimated using the bootstrap method with 1000 samples. The equivalent moment magnitudes (Mw*) for the entire catalogue are calculated using the magnitude relations to homogenize the catalogue. The magnitude of completeness is 2.7 Mw*. The final catalogue is not declustered or truncated using a threshold magnitude in order to be a widely usable catalogue. It contains not only Mw* but also the average and median of the observed magnitudes for each event. Contrary to the limited earthquake parameters in the previous catalogues for Turkey, the 45 parameters of ∼378 000 events are presented in this study.

A comparison of alternative curve fitting techniques for different earthquake fault parameters of Iranian earthquakes

In this study, we tried to estimate the optimum linear equations among the parameters associated with different earthquake fault mechanisms for Iranian earthquakes. For this purpose, we tested different curve fitting methods in order to present the most proper empirical relationships between several seismic parameters for different fault systems. In the present paper, 46 large and destructive Iranian earthquakes whose magnitudes change between 5.8 and 7.8 from 1900 to 2014 were used for the analyses. A comparison was made by using four types of curve fitting techniques. The estimation procedures are considered as (1) L2 or Least Squares Regression, (2) L1 or Least Sum of Absolute Deviations Regression, (3) Robust Regression and, (4) Orthogonal Regression. Confidence intervals were selected as 95% for all types of regression relationships. In the selection of the best probability distribution, we considered the correlation coefficients of the linear regressions as a powerful and conceptually simple method. Correlation coefficients of all relationships change between 0.299 and 0.986 with Orthogonal regression, between 0.168 and 0.792 with L1 regression, between 0.059 and 0.829 with Robust regression. For Iranian earthquakes, the most suitable and reliable empirical relationships between moment magnitude (Mw) and surface wave magnitude (Ms), Mw and surface rupture length (SRL), Mw and maximum displacement (MD), and SRL and MD were obtained by Orthogonal regression since it supplies stronger correlation coefficients than those of the other regression techniques in most estimates. The results show that estimated empirical relationships among the different fault parameters by using the Orthogonal regression method can be accepted as more up-to-date and more appropriate in comparison with the other regression norms. Consequently, these equations were suggested as more reliable in the estimation of the maximum surface displacement, maximum surface rupture length and associated with the maximum credible earthquakes for different areas of Iran. Furthermore, obtained relationships can be statistically significant for the assessment of seismic, tectonic and geologic activities, and they can be used to evaluate the rupture hazard of the Iranian Plateau.

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.

A Homogeneous Earthquake Catalogue for Turkey and Surrounding Region

A new earthquake catalogue for Turkey and surrounding region (32°–47° N, 20°–52° E) is compiled for the period 1900–2017. The earthquake parameters are obtained from the Bulletin of International Seismological Centre that is fully updated in 2020. New conversion equations between moment magnitude and the other scales (md, ML, mb, Ms and M) are determined using in the General Orthogonal Regression method to build up a homogeneous catalogue, which is the essential data for seismic hazard studies. The 95 % confidence intervals are estimated using the bootstrap method with 1000 samples. The equivalent moment magnitudes (Mw*) for the entire catalogue are calculated using the magnitude relations to homogenise the catalogue. The magnitude of completeness is 2.9 Mw* and 3.0–3.2 Mw* for Turkey and Greece generally. The final dataset is not declustered or truncated using a threshold magnitude because of motivation for generating a widely usable catalogue. It contains not only Mw*, but also the average and median of the observed magnitudes for each event. Contrary to the limited earthquake parameters in the previous catalogues, the 45 parameters of approximately 700 k events occurred in a wide area from the Balkans to the Caucasus are presented.

Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (∆CO:∆NOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NOx but not cross-road gradient ∆CO:∆NOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.