Search

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method. [ABSTRACT FROM AUTHOR]

Ordovician limestone water on the floor is a serious threat to the safety of coal mine production in the Weibei coalfield, and prediction of the floor failure depth is the key for evaluating the mining conditions under pressure. This paper combines the hydrogeological conditions of the no. 5 coal seam and uses the FLAC3D program to determine the floor failure depth under two-factor (mining depth and width) and multifactor (mining depth, width, and height) conditions via numerical calculations and analysis. We obtain the fitting formula for the floor failure depth and analyze the influence of various factors on it. The results show that when the mining width does not exceed 200 m, the mining width has the greatest influence on the floor failure depth, followed by mining depth and mining height. In this paper, the working face floor failure depths measured values of 18 flat seams in China are taken as samples for comparison with the values calculated via empirical formulas and fitting formulas, and the maximum and minimum absolute errors and relative errors are analyzed. The nonlinear fitting regression formulas offered in this paper are found to provide strong predictive value, high accuracy, and a relatively small error range. The reliability and rationality of the models are further verified, thus providing a reference for future mining operations under safe aquifer water pressure conditions in the Weibei coalfield. [ABSTRACT FROM AUTHOR]

In order to solve the prewarning problem of South-to-North Water Transfer Project safety, an intelligent cooperative prewarning method based on machine learning was proposed under the framework of intelligent information processing. Driven by the monitoring data of the South-to-North Water Transfer Project, the single sensor in typical scenes was studied, and the security threshold was predicted along the vertical axis of time, firstly. With the support of the data correlation calculation, the sensors in the typical scene were intelligently grouped, and the study objectives were changed into sensor grouping, secondly. Then, the nonlinear regression model between the single sensor and the multisensors was built on the time cross section, and the model was used to dynamically calculate the safety threshold of the current sensor for the second time. Finally, in the framework of intelligent information processing, a double verification mechanism was proposed to support the construction of the intelligent prewarning method for the safety of South-to-North Water Transfer Project. The paper collected the monitoring data from November 2015 to September 2016 in the typical scenarios. The experimental results showed that the methods constructed in the paper can be able to identify the abnormal causes of data sudden jump effectively and give the different level prewarning. The method provides a strong theoretical support for further manual investigation work. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of traditional culture in the intelligent advertising design system in the internet era. The existence form of traditional culture is abstract and vivid. It is extremely charming when used in advertising design. It is unique and subtle and has the beauty of holding a pipa half-covered. The classic colors reveal the splendor of history, which is fascinating, and it seems to want people to integrate into it and appreciate its beauty. This paper, firstly, introduces the background of traditional culture and intelligent advertising design and discusses its significance and innovation. Combined with the research methods and experiments put forward by the related work, the advertising system model is, firstly, proposed. The process of ad request processing, system hardware topology, and ad real-time bidding process are introduced. It will talk about the feature association algorithm in advertising design, explain the linear logistic regression and nonlinear algorithms, and describe the GBDT feature processing process. It combines stochastic gradient descent, decision trees, etc., to calculate ad click-through rates. It also explained the allocation algorithm of traffic and quality and market competition in advertising design. It is guided by a probabilistic filtering mechanism that tests ad spend, bidding, and market competition. Finally, the effect evaluation experiment of intelligent advertising system based on traditional culture fusion is carried out. It displays the application of traditional culture, such as Dunhuang Feitian and blue and white porcelain, Huizhou architecture, and Miao embroidery. It uses pictures to show its beauty, and it compares the advertising situation in the perspective of traditional culture and modern fashion. It is concluded that the introduction of traditional culture into advertisements is more attractive. Through the establishment of an outdoor advertising evaluation system based on face gesture recognition to calculate the pedestrian parking viewing rate, it is concluded that the system has a very high accuracy rate and a very small error, only 3.76%, which can be effectively applied to actual scenarios. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is an important physiological parameter that characterizes the growth of crops. Traditional measurement could not meet the demands of large-scale accurate monitoring. QGA-ELM and LS-SVM algorithm combined with UAV remote sensing images was used to achieve the goal of building large-scale fast inversion modeling of LAI in this paper. Linear and nonlinear models were constructed for comparing the correlation between six spectral indices and LAI by categorizing the nitrogen level. The LS-SVM model was constructed to replace traditional linear model, the determination coefficient of correction set and prediction set (R2C and R2P) were 0.6496 and 0.6814; and the root mean square error of correction set and prediction set (RMSEC and RMSEP) were 0.5702 and 0.6842, respectively. The results showed that the inversion of edge objects in noncrop areas was not so stable. In order to address the problem, an improvement based on the extreme learning machine (ELM) and quantum genetic algorithm (QGA) with probabilistic evolution were used to combine with LS-SVM for overcoming the problem which the hidden layer connection weight and threshold randomly generated and solve the problems of slow regression of nonlinear data and insufficient model generalization ability. Compared with traditional linear and nonlinear regression, the QGA-ELM combined with LS-SVM showed the following: (1) improving the optimization ability greatly and avoid the prematurity of GA (genetic algorithm) effectively. The generalization performance has also been enhanced. (2) R2P of prediction set was 0.6686, and RMSEP was 0.8952 which could reflect the growth and distribution trend of rice in the regional scale. (3) Adapting different fertilization gradients (deficiency to excess) could provide basis for LAI inversion in different varieties and accumulated temperature zone of rice. The results above showed that QGA-ELM combined with LS-SVM could improve the stability of the model greatly and provide reference significance for rice growth inversion. [ABSTRACT FROM AUTHOR]

With the rapid development of air transportation, the complexity, importance, and severity of civil aviation safety have gradually become prominent. It is essential to use various data to analyze and predict the level of aviation safety. This paper used a combined prediction model based on Induced Ordered Weighted Averaging (IOWA) operator to forecast the civil aviation incident rate. We compiled and calculated civil aviation incident data and total flight hours from 2008 to 2019 in China and took the civil aviation incident rate (incident numbers per ten thousand flight hours) as the prediction object. First, this paper used the nonlinear regression model, Grey Verhulst model, and Holt-Winters exponential smoothing model to forecast the civil aviation incident rate individually. Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. Finally, this paper forecasted the civil aviation incident rate in 2020 and 2021. The results showed that the predicted rates are 0.524 and 0.551. Most notably the incident rate will increase significantly compared with 2019. [ABSTRACT FROM AUTHOR]

A large number of engineering problems involve several conflicting objectives, which today are often solved through expensive simulation calculations. Methods based on meta-models are one of the approaches to solving this group of problems. In this paper, multiobjective optimization in the extraction system of a copper open-pit mine complex is presented by the modified-NBI optimization method and regression meta-model. For this purpose, two objective functions of maximizing the amount of total extraction, which is the sum of the extraction of sulfide, oxide, low-grade ores, and waste in this mine, and minimizing the transport time of haulage according to the limitation of its storage capacity, transport equipment, and budget are considered. The Central Composite Design (CCD) method is used to build the Design of Experiments (DOE) for the design variables. The considered design variables are the number of trucks of 120 tons, 240 tons, 35 tons, and 100 tons. The number of targets considered in each design combination is considered the response surface. The suitable meta-model to maximize the total extraction rate and minimize the transport time of the haulage, two modified functions of nonlinear regression have been determined. The accuracy of the models for selection has been done using PRESS and R 2 statistics. The most common PRESS error has also been used to validate the meta-models. Then the multiobjective optimization problem was solved using the modified-NBI method. Finally, Pareto and optimal solutions using the proposed approach were presented and discussed. [ABSTRACT FROM AUTHOR]

The wear behaviour of hot pressed AA 2618 aluminium alloy matrix composites reinforced through nano Si3N4 elements (1 percent and 2 percent) has been investigated in this paper. Temperatures of 50°C, 150°C, and 250°C were used to examine the tribological characteristics of the models under a range of loads and pressures. The best wear performance was found in AA 2618/2wt percent Si3N4. Under a load of 30 N and temperature of 250°C, it was discovered that Si3N4-enriched AA 2618 alloy was 35.7% more wear-resistant than unreinforced AA 2618 alloy. Metal flow and plain delamination were the most common wear mechanisms at higher temperatures. Delamination is the most common wear mechanism at temperatures between 50 and 250 degrees Celsius. In the analysis of variance, the wear rate was influenced by temperature, load, and the presence of Si3N4 by 47.2%. In order to predict the wear rate, regression equations (linear and nonlinear) were developed by Taguchi method. Using a high determination coefficient, the nonlinear regression was the preeminent success rate (92.8 percent). [ABSTRACT FROM AUTHOR]

In this paper, we present a mathematical model that describes agree-disagree opinions during polls. We first present the different compartments of the model. Then, using the next-generation matrix method, we derive thresholds of the stability of equilibria. We consider two sets of data from the Reuters polling system regarding the approval rating of the U.S presidential in two terms. These two weekly polls data track the percentage of Americans who approve and disapprove of the way the President manages his work. To validate the reality of the underlying model, we use nonlinear least-squares regression to fit the model to actual data. In the first poll, we consider only 31 weeks to estimate the parameters of the model, and then, we compare the rest of the data with the outcome of the model over the remaining 21 weeks. We show that our model fits correctly the real data. The second poll data is collected for 115 weeks. We estimate again the parameters of the model, and we show that our model can predict the poll outcome in the next weeks, thus, whether the need for some control strategies or not. Finally, we also perform several computational and statistical experiments to validate the proposed model in this paper. To study the influence of various parameters on these thresholds and to identify the most influential parameters, sensitivity analysis is carried out to investigate the effect of the small perturbation near a parameter value on the value of the threshold. An uncertainty analysis is performed to evaluate the forecast inaccuracy in the outcome variable due to uncertainty in the estimation of the parameters. [ABSTRACT FROM AUTHOR]

The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper. [ABSTRACT FROM AUTHOR]

Traditional CCRMs (Constrained Center-and-Range Methods) in solving the problem of interval regression could hardly make tradeoffs between the overall fitting accuracy and the coincidence degree between the observed and predicted intervals and could also hardly reduce the number of disjoint elements between the observed and predicted intervals, as well as raise the average ratio of all predicted intervals contained within their observed intervals. This paper constructed a nonlinear regression model based on center-and-range method, in which the maximization of coincidence degree for the sample with the worst coincidence degree between the observed and predicted interval was incorporated into the traditional CCRM model's objective. This novel nonlinear programming model was proven to be a convex one that satisfied K-T condition. Monte Carlo simulation shows that the model is degenerated to the compared CCRM+ model as the objective only contains the minimization of the overall fitting accuracy for both center and range sample series. In this situation, it could obtain a better solution than the use of the compared CCRM model. In addition, when the proposed model only takes into account the maximization of coincidence degree for the sample with the worst coincidence degree between the observed and predicted interval, the model shows a better performance than the CCRM+ model in terms of the average ratio of all predicted intervals contained within their observed intervals, as well as the average number of forecasts with 0% accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation on the properties of hydraulic conductivity and permeability of conglomerates under different temperatures and confining pressures with integrated samples and samples with shear failure. Constant head tests were carried out in a temperature-controlled triaxial cell with samples obtained from the Zhuxianzhuang Coal Mine. Five levels of temperatures (10°C, 20°C, 28°C, 35°C, and 50°C) and three levels of confining pressures (3 MPa, 5 MPa, and 7 MPa) were chosen for the tests. The results show that there is a negative relationship between hydraulic conductivity and confining pressure with both original and shear failure samples. An inflection point of 35°C is found in the relationship between the flow rate and temperature. However, with increasing temperature conditions, hydraulic conductivity first increases and then decreases at 50°C with the intact sample, while hydraulic conductivity first decreases from 20°C and then increases with the shear failure sample. Finally, nonlinear regression equations of hydraulic conductivity and temperature were obtained under different confining pressures. [ABSTRACT FROM AUTHOR]

This paper aims to understand the dynamics of the spread of COVID-19 for Nepal. It is carried out with the help of multivariate statistics techniques. Direct relationships among variables are obvious, as they are easily seen and measured. But, hidden variables and their interrelationships also have a significant effect on the spread of a pandemic. Multinomial logistic regression, odds ratio, linear mixed-effect models, and principal component analysis are used here to analyze these hidden variables and their interrelationships. Also, such studies are very important for countries with limited and scarce data. These countries do not have a backbone of good-quality official records. Understanding the spread of a disease in a developing country also helps in management and eradication of that disease. The multivariate daily data of new cases, deaths, recovered, total cases, total deaths, total recovered, and total infected (isolated) are used here. The daily incidence of new cases is also modeled here using nonlinear regression. Two best nonlinear models are discussed here. ARIMA models are used for analyzing and forecasting the progression of the variables for two months into the future. The impact of government restriction in the form of strict lockdown 1, partially relaxed lockdown 1, completely relaxed lockdown 1, and strict lockdown 2 is minutely analyzed. These controls were exercised to curtail the spread of the pandemic. The role of these controls in curbing the spread of the pandemic is also studied here. The results obtained from this study can be applied to other countries of South Asia and Africa. [ABSTRACT FROM AUTHOR]

Multivariate noises in the learning process are most of the time supposed to follow a standard multivariate normal distribution. This hypothesis does not often hold in many real-world situations. In this paper, we consider an approach based on multivariate skew-normal distribution. It allows for a multiple continuous variation from normality to nonnormality. We give an extension of the generalized least squares error function in a context of multivariate nonlinear regression to learn imprecise data. The simulation study and application case on real datasets conducted and based on multilayer perceptron neural networks (MLP) with bivariate continuous response and asymmetric revealed a significant gain in precision using the new quadratic error function for these types of data rather than using a classical generalized least squares error function having any covariance matrix. [ABSTRACT FROM AUTHOR]

In order to optimize the wall thickness distribution of medical balloon, kyphoplasty balloon was chosen as the research object, the uniformity of wall thickness distribution was taken as the evaluation index, and the influence of stretch blow molding process on the uniformity of kyphoplasty balloon was investigated. In this paper, 16 sets of orthogonal test schemes were studied by selecting four main parameters such as forming temperature, forming pressure, stretching distance, and holding time of stretch blow molding process based on the L16(44) Taguchi method orthogonal table. The statistical analysis showed that the forming temperature was an utmost parameter on the uniformity, while an optimal scheme was obtained and an optimal balloon with the uniformity of 95.86% was formed under the scheme. To further quantify the relationship between the uniformity and the parameters, artificial neural network (ANN) and nonlinear regression (NLR) models were developed to predict the uniformity of the balloon based on orthogonal test results. A feed-forward neural network based on backpropagation (BP) was made up of 4 input neurons, 11 hidden neurons, and one output neuron, an objective function of the NLR model was developed using second-order polynomial, and the BFGS method was used to solve the function. Adequacy of models was tested using hypothesis tests, and their performances were evaluated using the R2 value. Results show that both predictive models can be used for predicting the uniformity of the balloon with a higher reliability. However, the NLR model showed a slightly better performance than the ANN model. [ABSTRACT FROM AUTHOR]

Aiming at the influence of information, we investigate and analyze the sequential route choice behavior under dynamic reference points based on cumulative prospect theory in this paper. An experiment platform collecting the sequential route choices based on C/S structure is designed and four types of information are released to participants, respectively. Real-time travel time prediction methods are then proposed for travelers' decision-making. Using nonlinear regression method, the parameters of the value function and weight function of cumulative prospect theory are estimated under different types of information, respectively. It is found that travelers' behavior showed obvious characteristic of risk pursuit under the circumstance where real-time travel time information is released. Instead, when they have access to descriptive information, they tend to be more conservative. [ABSTRACT FROM AUTHOR]

Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322 e -08 and an R -squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion. [ABSTRACT FROM AUTHOR]

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft. [ABSTRACT FROM AUTHOR]

With the increase in shaft depth, the problem of cracks and leakage in single-layer concrete lining in porous water-rich stable rock strata has become increasingly clear, in which case the mechanism of fracturing in shaft lining remains unclear. Considering that the increase in pore water pressure can cause rock mass expansion, this paper presents the concept of hydraulic expansion coefficient. First, a cubic model containing spherical pores is established for studying hydraulic expansion, and the ANSYS numerical simulation, a finite element numerical method, was used for calculating the volume change of the model under the pore water pressure. By means of the multivariate nonlinear regression method, the regression equation of the hydraulic expansion coefficient is obtained. Second, based on the hydraulic expansion effect on the rock mass, an interaction model of pore water pressure–porous rock–shaft lining is established and further solved. Consequently, the mechanism of fracturing in shaft lining caused by high-pressure pore water is revealed. The results show that the hydraulic expansion effect on the surrounding rock increases with its porosity and decreases with its elastic modulus and Poisson's ratio; the surrounding rock expansion caused by the change in pore water pressure can result in the outer edge of the lining peeling off from the surrounding rock and tensile fracturing at the inner edge. Therefore, the results have a considerable guiding significance for designing shaft lining through porous water-rich rock strata. [ABSTRACT FROM AUTHOR]

This paper investigates dynamic response, rutting resistance, and fatigue behavior of three stone mastic asphalt (SMA) concrete mixtures selected on basis of nominal maximum aggregate size (NMAS): 25 mm, 19 mm, and 12.5 mm using cellulose fiber added as 0.3% of the total weight of aggregate. Superpave gyratory specimens were fabricated and subjected to the dynamic modulus (E∗) and flow tests (flow number and flow time) using an asphalt mixture performance tester. The E∗ test results were employed to develop stress-dependent master curves for each mixture, indicating that the mixture with the NMAS of 25 mm is relatively stiffer than other tested mixtures; this mixture also exhibits excellent strength against rutting failure. In addition, fatigue parameter, which is derived from dynamic response and phase angle, is determined, and results reveal that 12.5 mm NMAS mix has relatively better resistance to fatigue than other selected mixtures. Furthermore, nonlinear regression model specifications were utilized to predict accumulated strains as a function of loading cycles. Also, a flow number model is developed that predicts the rutting behavior of mixtures, and results suggest that model predicted and observed outputs of 25 mm SMA mix are found to be very close. The results of this study help in understanding the performance and behavior of cellulose fiber-added stone mastic asphalt concrete mixtures under varying simulated temperature and stress levels, which can be used in areas where the premature failure of flexible pavements is often observed. The testing protocol employed in this study will also help in evaluating pavement performance using Mechanistic-Empirical Pavement Design Guide. [ABSTRACT FROM AUTHOR]

The special topic of this paper is to build a nonlinear measuring model between PPP project risk and multiple variables and test it through case analysis. Nonlinear regression method was used in this research to study the risk mutation of public-private partnership (PPP) projects without any significant events. Risk evolution is influenced by three factors which include (1) macroscopic environment, (2) micro environment, and (3) subject’s capacity and their cooperative relationship. First, it reveals three main drive variables of project risk evolution through case analysis. Second, it builds a nonlinear time-varying measurement model which is then transformed to a classical regression model. Lastly, it estimates and tests the model parameter by an example. The study shows that there is an integral negative correlation among the three independent variables within a certain threshold range, revealing macro environment as the most significant factor of project risk. Analyzing the complex relationship between multiple impact variables and risk evolution of PPP projects can provide a basic tool for forecasting and control of risk. [ABSTRACT FROM AUTHOR]

An automated flower thinning system, when combined with machine vision, has the potential to reduce the labor force, improve efficiency, and lower costs. This combination represents the future of agricultural machinery development. The primary objective of automatic flower thinning is to determine the flowering density of fruit trees under natural light conditions. In this study, we introduce a flower recognition algorithm that uses pixel values as an independent variable to recognize flower categories by constructing a nonlinear regression model. Initially, the RGB pixel values of elements in the training set are extracted. Similar pixel values are clustered together to reduce the amount of computation, and representative elements are selected to construct a nonlinear classification function, known as the regression function. The coefficients in the classifier are determined by transforming the problem into an unconstrained optimization problem using the least square method. The optimal solution is then found as the coefficient value in the classifier. The classification function calculates the function value of the RGB pixel value for each input entity to determine whether it belongs to the flower entity. Finally, the developed algorithm is used to classify the flower graphic elements of the measured pictures, and the efficiency of the algorithm is verified. [ABSTRACT FROM AUTHOR]

This paper analyzes the utility calculation principle of travelers from the perspective of mental accounting and proposes a travel choice behavior model that considers travel time and cost (MA-TC model). Then, a questionnaire is designed to analyze the results of the travel choice under different decision-making scenarios. Model parameters are estimated using nonlinear regression, and the utility calculation principles are developed under different hypothetical scenarios. Then, new expressions for the utility function under deterministic and risky conditions are presented. For verification, the nonlinear correlation coefficient and hit rate are used to compare the proposed MA-TC model with the other two models: (1) the classical prospect theory with travel time and cost (PT-TC model) and (2) mental accounting based on the original hedonic editing criterion (MA-HE model). The results show that model parameters under deterministic and risky conditions are pretty different. In the deterministic case, travelers have similar sensitivity to the change in gain and loss of travel time and cost. The prediction accuracy of the MA-TC model is 3% lower than the PT-TC model and 6% higher than the MA-HE model. Under risky conditions, travelers are more sensitive to the change in loss than to the change in gain. Additionally, travelers tend to overestimate small probabilities and underestimate high probabilities when losing more than when gaining. The prediction accuracy of the MA-TC model is 2% higher than the PT-TC model and 6% higher than the MA-HE model. [ABSTRACT FROM AUTHOR]

There are plenty of uncertainties and enormous challenges in deep water drilling due to complicated shallow flow and deep strata of high temperature and pressure. This paper investigates density of dynamic kill fluid and optimum density during the kill operation process in which dynamic kill process can be divided into two stages, that is, dynamic stable stage and static stable stage. The dynamic kill fluid consists of a single liquid phase and different solid phases. In addition, liquid phase is a mixture of water and oil. Therefore, a new method in calculating the temperature and pressure field of deep water wellbore is proposed. The paper calculates the changing trend of kill fluid density under different temperature and pressure by means of superposition method, nonlinear regression, and segment processing technique. By employing the improved model of kill fluid density, deep water kill operation in a well is investigated. By comparison, the calculated density results are in line with the field data. The model proposed in this paper proves to be satisfactory in optimizing dynamic kill operations to ensure the safety in deep water. [ABSTRACT FROM AUTHOR]

Many developing countries including China are implementing increasingly stringent environmental regulations to achieve sustainable development. However, we have limited understanding about whether environmental regulations promote enterprise innovation. To address this research gap, this study empirically analyzes the impact of environmental regulations which is represented by the China Environmental Protection Law (2015) on enterprise innovation and it explores the moderating effects of official communication on environmental regulations and corporate innovation. The Super-Slacks-Based Measure (Super-SBM) model and Multiple Nonlinear Regression model are used to analyze 34,074 samples in China's A-share market during the 2015–2017 period. Our results show that, in general, a higher intensity of environmental regulations is more beneficial to incentivize enterprises to implement innovation. Meanwhile, there is an inverted U-type relationship between official communication and innovation of enterprises. Furthermore, at present, the average level of official communication in China is too high, which will weaken the role of environmental regulation in promoting enterprise innovation. Overall, this study can help us better understand the politics behind enterprise innovation in countries. [ABSTRACT FROM AUTHOR]

The main focus of this paper is to extend fuzzy regression and its application. Therefore, we used nonlinear modeling to investigate the impact of information and communication technology (ICT) on the Gini coefficient. Hence, we calculated the three borders upside, medium, and downside of the Gini coefficient. We found no evidence of reduced income inequality when decreasing the use of ICT goods. However, with the increased use of ICTs, income inequality decreases over time. Also, the results showed that the effect of ICT commodities on the Gini coefficient was the inverted U situation. Understanding this impact is very important from the perspective of planning and building infrastructure for poverty reduction. [ABSTRACT FROM AUTHOR]

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke' performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players' Forehand strokes racket's movement and orientation measurements; besides, the strokes' evaluation scores were assigned by the three coaches. The authors investigated the ML models' behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study. [ABSTRACT FROM AUTHOR]

This study proposes a trajectory planning method for an automated lane change maneuver. We consider that the spatiotemporal trajectory of a controlled vehicle can be represented by a polynomial function and estimate the parameters of the polynomial function through nonlinear programming that does not rely on the assumed time horizon of lane change and the assumed final state of the controlled vehicle. When the controlled vehicle achieves its target lane, the state of this vehicle should be constrained by both the position and the speed of the forward vehicle. We integrate a car-following model into the nonlinear programming to constrain the state of the controlled vehicle at the end of the lane change. Notably, the interaction factors are taken into consideration: the motion of the vehicle that follows the controlled vehicle in the target lane can be influenced by the trajectory planning results of the controlled vehicle. The proposed trajectory planning method can simultaneously estimate the motion of the following vehicle and plan the trajectory for the controlled vehicle. Our proposed model can also work for curved road sections. We represent the curve information in the nonlinear programming by a regression model. We solve the nonlinear programming through the sequential quadratic program (SQP) algorithm and design a method to specify an initial guess for the algorithm. This paper finally demonstrates the effect of the proposed trajectory planning method under different scenarios. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN) distribution, which is proposed within a general framework of flexible skew- symmetric (FSS) distributions by combining with skew-t-normal (STN) distribution. In comparison with the common skewed distributions such as skew normal (SN), and skew-t (ST) as well as scale mixtures of skew normal (SMSN), the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach. [ABSTRACT FROM AUTHOR]

Owing to its mathematical elegance and empirical accuracy, the speed-density model is critical in solving macroscopic traffic problems. This study developed an improved general-logistic-based speed-density model, which is a new method in macroscopic traffic flow theory. This article extensively discusses the properties of the general-logistic-based speed-density model. The physical meanings and values of all the parameters were determined based on the effect of heavy vehicles and the method for the linear and nonlinear regression analysis. The accuracy and versatility of the developed model were also found to be excellent based on the field data and relative error. [ABSTRACT FROM AUTHOR]

The need to design low-cost adsorbents for the detoxification of industrial effluents has been a growing concern for most environmental researchers. So modelling of experimental data from adsorption processes is a very important means of predicting the mechanisms of various adsorption systems. Therefore, this paper presents an overall review of the applications of adsorption isotherms, the use of linear regression analysis, nonlinear regression analysis, and error functions for optimum adsorption data analysis. [ABSTRACT FROM AUTHOR]

Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1) extracted from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax. [ABSTRACT FROM AUTHOR]

The excessive use of chemical fertilizers causes many problems for which solutions are being sought in a variety of agricultural production systems. From the perspective of paying attention to the health requirements of aquatic animals in terms of water quality, this study investigated the impact of chemical fertilizer application on water quality in a rice (Oryza sativa L.; Cultivar "Qingxiangruangeng," accession number: 2014004)–eel (Monopterus albus) coculture system in Shanghai, China. Chemical fertilizer was applied four times—as base fertilizer, rejuvenated fertilizer, tiller fertilizer, and ear granule fertilizer—during the production process of a rice–eel coculture system (June to October 2020). Changes in the water quality parameters of rice field's surface water and ring ditch water in the regular chemical fertilizer group (RF) and no chemical fertilizer group (NF, the control) were compared before and at 24 h, 48 h, and 72 h after the chemical fertilizer application. The results for the analyzed physical and chemical indexes of each water area before and after four fertilizations revealed several consistent trends. First, the pH, dissolved oxygen (DO), water temperature (T), and chemical oxygen demand (COD) of either water area were similar between the NF and RF groups, whereas their total nitrogen (TN), total ammonia nitrogen (TAN), and total phosphorus (TP) levels differed significantly. After adding the above fertilizer containing nitrogen and phosphorus, 24 h later, the TN, TAN, and TP content had already increased significantly in comparison with the control. The maximum average content of TAN and nitrite nitrogen (NO2−-N) in the ring ditch water reached 12.30 mg/L and 0.37 mg/L, respectively, at 24 h after the chemical fertilizer application. Nonlinear regression analysis results showed that there was a significant positive relationship TN (δTN) and TAN (δTAN) vis-à-vis the nitrogen content of the fertilizer. The results of this study provide a timely empirical reference and data support for improving fertilizer management in rice–eel coculture systems. [ABSTRACT FROM AUTHOR]

The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user's subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types. [ABSTRACT FROM AUTHOR]

Drying processes including solar, oven, and refractance window were studied to determine their influence on the drying behavior of jackfruit slices and properties of resultant jackfruit powders. The loss of sample mass, converted to the ratio between the water content at time t and the initial water content (moisture ratio), was used as the experimental parameter for modelling drying processes. Fifteen thin layer drying models were fitted to the experimental data using nonlinear regression analysis. Based on the highest R 2 and lowest SEE values, the models that best fit the observed data were Modified Henderson and Pabis, Verma et al., and Hii et al. for RWD, oven, and solar drying, respectively. The effective moisture diffusivity coefficients were 5.11 × 10 − 9 , 3.28 × 10 − 10 , and 2.55 × 10 − 10 for RWD, oven and, solar drying, respectively. The solubility of freeze-dried jackfruit powder (75.7%) was not significantly different from the refractance window dried powder (73.2%) and was higher than oven-dried jackfruit powder (66.1%). Oven-dried jackfruit powder had a lower rehydration ratio and porosity. Differences in rehydration ratio and porosity under different drying methods could be explained by the microstructure. Fractal dimension (FD) and lacunarity were applied to study the structure and irregularities of jackfruit dried with the different methods. FD was significantly (P < 0.05) affected by the drying method. FD ranged from 1.809 to 1.837, while lacunarity ranged between 0.258 and 0.404. [ABSTRACT FROM AUTHOR]

Crushing and caving of roof strata in goaf is an important factor affecting surface subsidence. In order to study the influence law of mining under thick loose layer, overburden size parameters, and initial mechanical parameters of collapse zone on surface subsidence, the law of surface subsidence was analyzed through measured data. On this basis, with the help of FLAC3D numerical simulation software, the surface subsidence was simulated by changing the above factors, and the evolution characteristics of the maximum surface subsidence under the influence of various factors were analyzed. Based on the grey relational analysis, the relational degree between each factor and the maximum surface subsidence value is analyzed, and through multiple nonlinear regression, the prediction model of mining subsidence deformation under thick loose layer considering the influence of seven factors is established. The results show that the mining thickness has the greatest influence on the surface subsidence, and cohesion has the least influence on surface subsidence. The rationality of the subsidence prediction model is verified by an example. [ABSTRACT FROM AUTHOR]

Sound speed profile (SSP) inversion is usually performed by linear statistical regression, such as the single empirical orthogonal function regression (sEOF-r) model. However, due to the complex dynamic activities of the ocean, the relationship between parameters is not strictly linear, often resulting in an unsatisfactory inversion result. In this study, an algorithm based on the random forest (RF) integrated learning model, for SSP inversion, was proposed. Using the sea surface temperature anomaly (SSTA) and sea surface height anomaly (SSHA) data, the sound speed profile of the upper 1000 m layer in the South China Sea was reconstructed, and its accuracy was evaluated through the root mean square error (RMSE). The accuracy of the evaluation demonstrated that the RF model proposed here could reconstruct the SSP in the upper 1000 m layer better than the sEOF-r can. Compared with the latter, the average reconstruction accuracy of the RF model was improved by 0.56 m/s. The linear regression of the sEOF-r model fell short of expectations in the regression between surface and subsurface parameters. By removing the constraints of linear inversion, the nonlinear regression of the RF model showed a smaller RMSE and better robustness in the reconstruction process and was superior to the sEOF-r model at all depths. As a result, it provided an effective integrated learning model for SSP reconstruction. [ABSTRACT FROM AUTHOR]

The compaction and consolidation process of a regenerated roof can be divided into two stages, namely, secondary fragmentation of crushed rock masses and pore structure adjustment. Therefore, the mechanical properties of the regenerated roof differs significantly from those of the primary roof. We performed confined compression tests of crushed mudstones and sandy mudstones using a self-developed compression testing apparatus. We studied the influence of the moisture content, compression ratio, particle size, and volume-based degradation on the compression and consolidation characteristics based on the microscopic morphology of the crushed rock masses. Further, the degrees of influence of these factors on the stability of the consolidated bodies were determined. Therefore, the influence mechanism of each factor on the compressive and shear strengths of the consolidated bodies was revealed. The results indicated that the compaction and consolidation of the crushed rock masses involved a constant adjustment of the contact and stress states of the rock masses. Under uniaxial compression and shearing, the consolidated bodies presented the features of ductile failure. The compression ratio and particle size were the most important influencing factors in the compressive and shear strengths of the consolidated bodies. Further, we carried out an orthogonal experiment and built a multivariate nonlinear regression model to quantitatively characterize the combined influence of these factors on the compaction characteristics of the crushed rock masses and the mechanical behavior of the consolidated bodies. [ABSTRACT FROM AUTHOR]

Generalized method of moments (GMM) has been widely applied for estimation of nonlinear models in economics and finance. Although generalized method of moments has good asymptotic properties under fairly moderate regularity conditions, its finite sample performance is not very well. In order to improve the finite sample performance of generalized method of moments estimators, this paper studies higher-order mean squared error of two-step efficient generalized method of moments estimators for nonlinear models. Specially, we consider a general nonlinear regression model with endogeneity and derive the higher-order asymptotic mean square error for two-step efficient generalized method of moments estimator for this model using iterative techniques and higher-order asymptotic theories. Our theoretical results allow the number of moments to grow with sample size, and are suitable for general moment restriction models, which contains conditional moment restriction models as special cases. The higher-order mean square error can be used to compare different estimators and to construct the selection criteria for improving estimator's finite sample performance. [ABSTRACT FROM AUTHOR]

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizesmodels. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likeli hood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice. [ABSTRACT FROM AUTHOR]

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-drivenmodeling technique comparisons have beenmade, looking for the bestmodel to yield prediction.However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model.Multiple linear regression, stepwise linear regression, M5?? regression trees, and artificial neural networks (ANN) were ranked.The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate themodels, three accuracy metrics were used: the root relative square error (RRSE), relativemean absolute error (RMAE), and correlation factor (??). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63). [ABSTRACT FROM AUTHOR]

Efficient and economical drilling and blasting method is widely used in tunnel excavation projects, but the vibration problem caused cannot be ignored. The blasting vibration in the near area directly damages the surrounding rock and supporting structure, and the vibration in the middle and far area affects the stability of the surrounding rock, the primary support, and even the secondary lining. Based on the blasting excavation of Xiaoxiku tunnel, blasting vibration tests were carried out. In addition, the measured data were analyzed by using nonlinear regression and Fourier transform of Sadowski formula. According to the interference vibration reduction method, the blasting vibration of group holes is analyzed, and the results provide a reference for blasting design optimization of Xiaoxiku tunnel or similar soft rock tunnel. The results showed that the blasting vibration test data showed that the peak vibration velocity generated by the blasting of cut hole is the largest. Based on the measured vibration waveform of single-hole blasting, MATLAB software is used to carry out superposition operation, so as to determine the best delay time of single-hole continuous blasting vibration reduction, and the value is calculated as 7 ms. According to the superposition operation results, the experiments of the cut area under different delay intervals were carried out. The measurement vibration waveform showed that the peak vibration velocity is the minimum when the delay interval is 7 ms. The actual measurement results are the same as the calculation results. The measured blasting waveform distribution is relatively uniform, and the vibration duration is moderate. The vibration speed decreases, while the main frequency increases. [ABSTRACT FROM AUTHOR]

Composite steel beams are commonly used element in multistorey steel buildings to enhance floor economy and serviceability and provide more clear height. Due to the low level of stress in the webs of such beams, hybrid sections are used where the flanges have higher strength than the webs. A lot of earlier research was carried out to optimize the design of the hybrid and nonhybrid composite steel beams under both static loading and dynamic behavior. However, there is still a need to develop a more practical optimization method. The aim of this research is to develop simple and practical equations to determine the optimum cross section dimensions for both shored and unshored, simply supported, hybrid and nonhybrid, composite steel beam under static loads. To achieve that goal, a research program of two phases was carried out. The first phase was generating a database of 504 composite beams with different steel grades for flanges and webs, subjected to different values of bending moment. The cross section of each beam in the database was optimized using GRG technique to minimize the cost considering the unit price of each steel grade. In the second phase, the generated database was divided into training and validation subsets and used to develop two predictive models using Nonlinear Regression (NLR) technique and Artificial Neural Network (ANN) technique to predict the optimum cross section dimensions and hence the optimum weight and cost. The accuracies of the developed models were measured in terms of average error percent. NLR and ANN models showed average error percent of 16% and 11%, respectively. [ABSTRACT FROM AUTHOR]

Sight distance during driving may be limited by side factors such as mountain slopes or trees and buildings in horizontal curves and by the dome of the arc in vertical curves, and the night vision also can be limited in the sag vertical curves by the vehicle's light. Analyzing driver's sight distance in the road is very important for traffic safety. In this regard, in order to help the designer, the current rules and guidelines propose the two-dimensional analysis model for the sight distance. In this analysis, the sight distance is calculated separately in the combination of horizontal and vertical curves, and the smallest amount is considered as the sight distance. While, after constructing and operating the road, drivers control their vehicle according to the conditions in their 3D space. Nowadays, given the remarkable advances in computer science, there are many possibilities for 3D modeling of the route. In this research, the goal is to calculate the three-dimensional stopping sight distance at each spatial point by computer modeling the existing roads. The speed of various drivers with conventional riding vehicles under free traffic conditions was obtained by a GPS device. The results showed that, in places such as curves, given the provision of sufficient stopping sight distance, driver's free-flow speed reduces. Thus, another factor affecting the speed of the drivers is the curvature change rate. Finally, using nonlinear regression modeling a logical relationship was determined and extracted between the three factors of driver's free-flow speed, 3D stopping sight distance, and curvature change rate of the path. [ABSTRACT FROM AUTHOR]

In-flight ice accretion on typical pitot-static systems is numerically investigated to reveal their performance deterioration under both rime and glaze icing. Coupled with the open source computational fluid dynamics (CFD) platform, OpenFOAM, the numerical strategy integrates the airflow determination by the Reynolds-averaged Navier-Stokes equations, droplet collection evaluation by Eulerian representation, and ice accumulation by mass and energy conservation. Under varying inflow conditions and wall temperatures, the calculated ice accretion performance indicates that the ambient temperature has the most significant effect on the icing-induced failure time, leading to an almost exponential growth. Meanwhile, the blocking time is found to be linearly proportional to the increase in wall temperature. With the increase in inflow velocity, the failure time follows a parabolic variation with glaze ice accretion while shows a monotonic reduction under rime icing conditions. In addition, when the angle of attack increases, failure accelerates under both the glaze and rime icing scenarios. These findings provide guidance for the protection design of pitot tubes. A nonlinear regression analysis is further conducted to estimate the failure performance. The predicated failure times show reliable consistency with numerical results, demonstrating the capability of the obtained empirical functions for convenient predictions of failure times within the applicable range. [ABSTRACT FROM AUTHOR]

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T , curing time t , age of the specimen A , the molarity of NaOH solution M , percent SiO2 solids to water ratio % S / W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate ( % A G ), fine aggregate to the total aggregate ratio F / A G , sodium oxide (Na2O) to water ratio N / W in Na2SiO3 solution, alkali or activator to the FA ratio A L / F A , Na2SiO3 to NaOH ratio N s / N o , percent plasticizer (% P), and extra water added as percent FA E W % . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions. [ABSTRACT FROM AUTHOR]