Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.

Characterizing the transduction behavior of ionic polymer-metal composite actuators and sensors via dimensional analysis

Ionic polymer-metal composites (IPMCs) are functional smart materials that exhibit both electromechanical and mechanoelectrical transduction properties, and the physical phenomenon underlying the transduction mechanisms have been studied across the literature extensively. Here we use a new modeling framework to conduct the most comprehensive dimensional analysis of IPMC transduction phenomena, characterizing the IPMC actuator displacement, actuator blocking force, short-circuit sensing current, and open-circuit sensing voltage under static and dynamic loading. The information obtained in this analysis is used to construct nonlinear regression models for the transduction response as univariant and multivariant functions. Automatic differentiation techniques are leveraged to linearize the nonlinear regression models in the vicinity of a typical IPMC description and derive the sensitivity of the transduction response with respect to the driving independent variables. Further, the multiphysics model is validated using experimental data collected for the dynamic IPMC actuator and voltage sensor. With data collected from physical samples of IPMC materials in-lab, the regression models developed under the new computational framework are verified.

Early LOC Estimation of Web Apps Created Using Yii Framework by Nonlinear Regression Models

We have performed early LOC estimation of Web applications (apps) created using the Yii framework by three nonlinear regression models with three predictors based on the normalizing transformations. We used two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation for constructing nonlinear regression models. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results compared to other regression ones based on the univariate transformations.

A Review of Well-Known Robust Line Search and Trust Region Numerical Optimization Algorithms for Solving Nonlinear Least-Squares Problems

The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares estimation involve minimizing either the conditional or the unconditional residual sum of squares. The maximum likelihood estimation (MLE) approach and the nonlinear least squares (NLS) procedure involve an iterative search technique for obtaining global rather than local optimal estimates. Several authors have presented brief overviews of algorithms for solving NLS problems. Snezana S. Djordjevic (2019) presented a review of some unconstrained optimization methods based on the line search techniques. Mahaboob et al. (2017) proposed a different approach to estimate nonlinear regression models using numerical methods also based on the line search techniques. Mohammad, Waziri, and Santos (2019) have briefly reviewed methods for solving NLS problems, paying special attention to the structured quasi-Newton methods which are the family of the search line techniques. Ya-Xiang Yuan (2011) reviewed some recent results on numerical methods for nonlinear equations and NLS problems based on online searches and trust regions techniques, particularly on Levenberg-Marquardt type methods, quasi-Newton type methods, and trust regions algorithms. The purpose of this paper is to review some online searches and trust region's more well-known robust numerical optimization algorithms and the most used in practice for the estimation of time series models and other nonlinear regression models. The line searches algorithms considered are: Gradient algorithm, Steepest Descent (SD) algorithm, Newton-Raphson (NR) algorithm, Murray’s algorithm, Quasi-Newton (QN) algorithm, Gauss-Newton (GN) algorithm, Fletcher and Powell algorithm (FP), Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. While the only trust-region algorithm considered is the Levenberg-Marquardt (LM) algorithm. We also give some main advantages and disadvantages of these different algorithms.

Erosive Degradation Study of Concrete Augmented by Mussel Shells for Marine Construction

This work proposes a green material for artificial reefs to be placed in Galicia (northwest Spain) taking into account the principles of circular economy and sustainability of the ecosystem. New concrete formulations for marine applications, based on cement and/or sand replacement by mussel shells, are analyzed in terms of resistance to abrasion. The interest lies in the importance of the canning industry of Galicia, which generates important quantities of shell residues with negative environmental consequences. Currently, the tests to determine the abrasion erosion resistance of concrete on hydraulic structures involve large and complex devices. According to this, an experimental test has been proposed to estimate and compare the wear resistance of these concretes and, consequently, to analyze the environmental performance of these structures. First, a numerical analysis validated with experimental data was conducted to design the test. Subsequently, experimental tests were performed using a slurry tank in which samples with conventional cement and sand were partially replaced by mussel shell. The abrasive erosion effect of concrete components was analyzed by monitoring the mass loss. It shows an asymptotic trend with respect to time that has been modeled by Generalized Additive Model (GAM) and nonlinear regression models. The results were compared to concrete containing only conventional cement and sand. Replacing sand and/or cement by different proportions of mussel shells has not significantly reduced the resistance of concrete against erosive degradation, except for the case where a high amount of sand (20 wt.%) is replaced. Its resistance against the erosive abrasion is increased, losing between 0.1072 and 0.0310 wt.% lower than common concrete. In all the remaining cases (replacements of the 5–10 wt.% of sand and cement), the effect of mussel replacement on erosive degradation is not significant. These results encourage the use of mussel shells in the composition of concrete, taking into account that we obtain the same degradation properties, even more so considering an important residue in the canning industry (and part of the seabed) that can be valorized.

NONLINEAR MODELS BASED ON QUANTILES IN THE FITTING OF GROWTH CURVES OF PEPPER GENOTYPES

This study aimed to fit nonlinear regression models to model the growth of the characters fruit length (FL) and fruit width (FW) of pepper genotypes (Capsicum annuum L.) over time using the method of ordinary least squares (OLS); and identify the model with the best fit and compare it to the model obtained via nonlinear quantile regression (QR) in the 0.25, 0.5, and 0.75 quantiles. Three regression models (Logistic, Gompertz, and von Bertalanffy) and four fit quality evaluators were adopted: Akaike information criterion, residual mean absolute deviation, and parametric and intrinsic curvature measurements. Five commercial genotypes of pepper were evaluated. Characters FL and FW were evaluated weekly from seven days after flowering, totaling ten measurements. In the estimation by OLS, the Logistic and von Bertalanffy models were considered adequate according to the quality evaluators. In the comparison between the models above by OLS and QR, the superiority of models obtained by QR was verified for the character FL. For the character FW, QR was efficient in three out of the five genotypes, being a valuable alternative in the study of fruit growth.

Methods for Developing Models in a Fuzzy Environment of Reactor and Hydrotreating Furnace of a Catalytic Reforming Unit

Methods for the development of fuzzy and linguistic models of technological objects, which are characterized by the fuzzy output parameters and linguistic values of the input and output parameters of the object are proposed. The hydrotreating unit of the catalytic reforming unit was investigated and described. On the basis of experimental and statistical data and fuzzy information from experts and using the proposed methods, mathematical models of a hydrotreating reactor and a hydrotreating furnace were developed. To determine the volume of production from the outlet of the reactor and furnace, nonlinear regression models were built, and fuzzy models were developed in the form of fuzzy regression equations to determine the quality indicators of the hydrotreating unit—the hydrogenated product. To identify the structure of the models, the ideas of sequential inclusion regressors are used, and for parametric identification, a modified method of least squares is used, adapted to work in a fuzzy environment. To determine the optimal temperature of the hydrotreating process on the basis of expert information and logical rules of conditional conclusions, rule bases are built. The constructed rule bases for determining the optimal temperature of the hydrotreating process depending on the thermal stability of the feedstock and the pressure in the hydrotreating furnace are implemented using the Fuzzy Logic Toolbox application of the MatLab package. Comparison results of data obtained with the known models, developed models and real, experimental data from the hydrotreating unit of the reforming unit are presented and the effectiveness of the proposed approach to modeling is shown.

Estimation of phosphorus requirements of sows based on 24-h urinary phosphorus excretion during gestation and lactation

Phosphorus requirements of reproducing sows were estimated using 24-h urinary P excretion. Thirty-six multiparous sows were fed one of six maize–soybean meal-based diets with total P ranging from 0·40 to 0·80 % in 0·08 % increments with a constant Ca:total P ratio (1·25:1). Diets were fed from day 7·5 ± 1 after breeding until the end of lactation (day 26 ± 1). Urine samples were collected in mid and late gestation (days 77·1 ± 2 and 112·4 ± 1) and early and late lactation (days 4·5 ± 1 and 18·2 ± 1). Phosphorus requirements were estimated using linear and nonlinear regression models. Based on a single 24-h urinary P excretion, estimated daily dietary total P requirements in mid and late gestation were 10·3 g (6·0 g standardised total tract digestible P, STTD P), and estimates for early and late lactation were 31·1 g (16·6 g STTD P) and 40·3 g (22·1 g STTD P), respectively. Plasma P and Ca concentrations were maintained within normal ranges at the estimated levels of P requirements. No differences among treatments were observed for plasma parathyroid hormone (P ≥ 0·06) and bone formation marker (P ≥ 0·16). In lactation, bone resorption marker decreased (P ≤ 0·001) as sows consumed more P. Among the analysed variables, urinary P was the most sensitive response to changes in dietary P intake. Urinary P excretion offers a practical method to estimate P requirements in sows. Our recommended daily total P requirements are 10·3 g for gestation and 35·7 g for lactation.

Application and Evaluation of Mathematical Models for Prediction of the Electric Energy Demand Using Plant Data of Five Industrial-Size EAFs

The electric arc furnace (EAF) represents the most important process route for recycling of steel and the second most productive steelmaking process overall. Considering the large production quantities, the EAF process is subject to continuous optimization, and even small improvements can lead to a significant reduction in resource consumption and operating cost. A common way to investigate the furnace operation is through the application of mathematical models. In this study the applicability of three different statistical modeling approaches for prediction of the electric energy demand is investigated by using more than 21,000 heats from five industrial-size EAFs. In this context, particular consideration is given to the difference between linear and nonlinear regression models. Detailed information on the treatment of the process data is provided and the applied methods for regression are described in short, including information on the choice of hyperparameters. Subsequently, the results of the models are compared. Gaussian process regression (GPR) was found to yield the best overall accuracy; however, the benefit of applying nonlinear models varied between the investigated furnaces. In this regard, possible reasons for the inconsistent performance of the methods are discussed.