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Journal articles on the topic 'GEP EQUATION'

Author: Grafiati
Published: 11 September 2023

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1

Salmasi, Farzin, and John Abraham. "Discharge coefficients for ogee weirs including the effects of a sloping upstream face." Water Supply 20, no. 4 (April 17, 2020): 1493–508. http://dx.doi.org/10.2166/ws.2020.064.

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Abstract Discharge coefficients (C0) for ogee weirs are essential factors for predicting the discharge-head relationship. The present study investigates three influences on the C0: effect of approach depth, weir upstream face slope, and the actual head, which may differ from the design head. This study uses experimental data with multiple non-linear regression techniques and Gene Expression Programming (GEP) models that are applied to introduce practical equations that can be used for design. Results show that the GEP method is superior to the regression analysis for predicting the discharge coefficient. Performance criteria for GEP are R2 = 0.995, RMSE = 0.021 and MAE = 0.015. Design examples are presented that show that the proposed GEP equation correlates well with the data and eliminates linear interpolation using existing graphs.
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Wang, Leizhi, Qingfang Hu, Yintang Wang, Yong Liu, Lingjie Li, and Tingting Cui. "Regional Calibration of Hargreaves Equation in the Xiliaohe Basin." Journal of Geoscience and Environment Protection 04, no. 07 (2016): 28–36. http://dx.doi.org/10.4236/gep.2016.47004.

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Jiang, Huanjun, Ahmed Salih Mohammed, Reza Andasht Kazeroon, and Payam Sarir. "Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns." Applied Sciences 11, no. 21 (November 8, 2021): 10468. http://dx.doi.org/10.3390/app112110468.

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The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column.
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Onen, Fevzi. "GEP PREDICTION OF SCOUR AROUND A SIDE WEIR IN CURVED CHANNEL." JOURNAL OF ENVIRONMENTAL ENGINEERING AND LANDSCAPE MANAGEMENT 22, no. 3 (March 17, 2014): 161–70. http://dx.doi.org/10.3846/16486897.2013.865632.

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Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V1/Vc), water head ratio (h1–p)/h1, side-weir length (L/r), side-weir crest height (b/p) and angle of bend θ. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using Gene Expression Programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clearwater scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation.
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Azaiez, Naima. "Improved Modelling of Soil Loss in El Badalah Basin: Comparing the Performance of the Universal Soil Loss Equation, Revised Universal Soil Loss Equation and Modified Universal Soil Loss Equation Models by Using the Magnetic and Gravimetric Prospection Outcomes." Journal of Geoscience and Environment Protection 09, no. 04 (2021): 50–73. http://dx.doi.org/10.4236/gep.2021.94005.

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Ashteyat, Ahmed, Yasmeen T. Obaidat, Yasmin Z. Murad, and Rami Haddad. "COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 26, no. 2 (February 10, 2020): 189–99. http://dx.doi.org/10.3846/jcem.2020.11931.

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The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.
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., Hoang Nguyen, Nam Xuan Bui ., Hieu Quang Tran ., and Giang Huong Thi Le. "A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming." Journal of Mining and Earth Sciences 61, no. 5 (October 10, 2020): 107–16. http://dx.doi.org/10.46326/jmes.ktlt2020.09.

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The efforts of this study are to develop and propose a state - of - the - art model for predicting blast - induced ground vibration in open - pit mines with high accuracy anf ability based on the gene expression programming (GEP) technique. 25 blasts were conducted in the Tan Dong Hiep quarry mines with a total of 83 blasting events that were collected for this study. The GEP method was then applied to develop a non - linear equation for predicting blast - induced ground vibration based on a variety of influential parameters. A traditional empirical equation, namely Sadovski, was also applied to compare with the proposed GEP model. The results indicated that the GEP model can predict blast - induced ground vibration in open - pit mines better than the Sadovski model with an RMSE of 0.986 and R2 of 0.867. Meanwhile, the traditional empirical model (Sadovski) only provided an accuracy with an RMSE of 1.850 và R2 of 0.767.
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8

Hajihassani, Mohsen, Shahrum Shah Abdullah, Panagiotis G. Asteris, and Danial Jahed Armaghani. "A Gene Expression Programming Model for Predicting Tunnel Convergence." Applied Sciences 9, no. 21 (November 1, 2019): 4650. http://dx.doi.org/10.3390/app9214650.

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Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson’s ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model’s output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner.
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Al-Aboodi, Ali H. "ESTIMATION OF MONTHLY MEAN REFERENCE EVAPOTRANSPIRATION USING GENE EXPRESSION PROGRAMMING." Kufa Journal of Engineering 8, no. 1 (February 16, 2017): 37–50. http://dx.doi.org/10.30572/2018/kje/811189.

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Evapotranspiration is a main component of the water cycle and is important in crop growth. Monthly mean reference evapotranspiration (ETo) is estimated using gene expression programming (GEP) in Basrah City, south of Iraq. Various climatic data, such as air temperature, relative humidity, and wind speed are used as inputs of GEP model to estimate the values of reference evapotranspiration (ETo) given by the FAO-56 (Penman-Monteith equation). Nine input combinations tested with GEP are coded as model No. (1-9). Root relative squared error (RRSE) is taken as fitness function in each of GEP models. GEP models with three climatic input variables (temperature, relative humidity, and wind speed) take the highest level in the performance. The GEP technique was successfully employed to estimate ETo in the study area. The explicit formulas obtained can be used as powerful models for estimating the mean monthly ETo in the irrigation practices with limited climatic data.
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Mutakela, Patrick S., Joyce P. Lepetu, Gofaone Rammotokara, Melusi Rampart, and Sarah Assem Ibrahim. "Biomass Prediction Equation for <i>Colophospermum mopane</i> (Mopane) in Botswana." Journal of Geoscience and Environment Protection 11, no. 06 (2023): 1–22. http://dx.doi.org/10.4236/gep.2023.116001.

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11

Chakrabarty, A., and S. N. Yakovenko. "Data-driven turbulence modelling using symbolic regression." Journal of Physics: Conference Series 2099, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2099/1/012020.

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Abstract The study is focused on the performance of machine-learning methods applied to improve the velocity field predictions in canonical turbulent flows by the Reynolds-averaged Navier–Stokes (RANS) equation models. A key issue here is to approximate the unknown term of the Reynolds stress (RS) tensor needed to close the RANS equations. A turbulent channel flow with the curved backward-facing step on the bottom has the high-fidelity LES data set. It is chosen as the test case to examine possibilities of GEP (gene expression programming) of formulating the enhanced RANS approximations. Such a symbolic regression technique allows us to get the new explicit expressions for the RS anisotropy tensor. Results obtained by the new model produced using GEP are compared with those from the LES data (serving as the target benchmark solution during the machine-learning algorithm training) and from the conventional RANS model with the linear gradient Boussinesq hypothesis for the Reynolds stress tensor.
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12

Ali Khan, Mohsin Ali, Adeel Zafar, Arslan Akbar, Muhammad Faisal Javed, and Amir Mosavi. "Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete." Materials 14, no. 5 (February 26, 2021): 1106. http://dx.doi.org/10.3390/ma14051106.

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For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc′ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc′ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume ( %AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc′ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.
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Shah, Kunjan, and Twinkle Singh. "A Solution of the Burger’s Equation Arising in the Longitudinal Dispersion Phenomenon in Fluid Flow through Porous Media by Mixture of New Integral Transform and Homotopy Perturbation Method." Journal of Geoscience and Environment Protection 03, no. 04 (2015): 24–30. http://dx.doi.org/10.4236/gep.2015.34004.

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14

Mozaffari, Sevda, Erfan Amini, Hossein Mehdipour, and Mehdi Neshat. "Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming." Water 14, no. 4 (February 19, 2022): 650. http://dx.doi.org/10.3390/w14040650.

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The significance of spillways is to allow the flood to be safely discharged from downstream. There is a strong correlation between the poor design of spillways and the failures of dams. In order to address this concern, the present study investigates the flow over the Nazloo-ogee spillway using the CFD 3D numerical model and an artificial intelligence method called Gene Expression Programming (GEP). In a physical model, discharge and flow depths were calculated for 21 different total heads. Among different turbulence models, the RNG turbulence model achieved the maximum compatibility in computational fluid dynamic simulation. In addition, GEP was used to estimate Q, in which 70% of collected data was dedicated to training and 30% to testing. R2, RMSE, and MAE were obtained as performance criteria, and the new mathematical equation for the prediction of discharge was obtained using this model. Finally, the numerical model and GEP outputs were compared with the experimental data. According to the results, the numerical model and GEP exhibited a high level of correspondence in simulating flow over an ogee-crested spillway.
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Akin, Oluwatobi O., Amana Ocholi, Olugbenga S. Abejide, and Johnson A. Obari. "Prediction of the Compressive Strength of Concrete Admixed with Metakaolin Using Gene Expression Programming." Advances in Civil Engineering 2020 (November 3, 2020): 1–7. http://dx.doi.org/10.1155/2020/8883412.

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One of the problems of optimization of concrete is to formulate a mathematical equation that shows the relationship between the various constituents of concrete and its properties. In this work, modelling of the compressive strength of concrete admixed with metakaolin was carried out using the Gene Expression Programming (GEP) algorithm. The dataset from laboratory experimentation was used for the analysis. The mixture proportions were made of three different water/binder ratios (0.4, 0.5, and 0.6), and the grades of concrete produced were grade M15 and M20. The compressive strength of the concrete was determined after 28 days of curing. The parameters used in the GEP algorithm are the input variables which include cement content, water, metakaolin content, and fine and coarse aggregate, while the response was designated as the compressive strength. The model was trained and tested using the parameters. The R-square value from the GEP algorithm was compared with the use of conventional stepwise regression analysis. With a coefficient of determination (R-square value) of 0.95, the GEP algorithm has shown to be a good alternative for modelling concrete compressive strength.
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Moghassem, Abdolrasool, Alireza Fallahpour, and Mohsen Shanbeh. "An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming." Journal of Engineered Fibers and Fabrics 7, no. 2 (June 2012): 155892501200700. http://dx.doi.org/10.1177/155892501200700202.

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Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.
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SHAHRARA, Neda, Tahir ÇELIK, and Amir H. GANDOMI. "GENE EXPRESSION PROGRAMMING APPROACH TO COST ESTIMATION FORMULATION FOR UTILITY PROJECTS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 23, no. 1 (January 19, 2017): 85–95. http://dx.doi.org/10.3846/13923730.2016.1210214.

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This article utilizes gene expression programming (GEP) technique to develop a prediction model in order to automate estimating the construction cost of water and sewer replacement/rehabilitation projects. A database gathered for developing the model was established on the basis of data related to 210 actual water and sewer projects obtained from the City of San Diego, California, USA. To verify the predictability of the GEP model, it was examined to estimate the cost of the projects that were not included in the modelling process. Sensitivity analysis technique and professional experiences were employed to determine the contributions of the qualitative factors and quantifiable parameters affecting the cost estimate. The proposed model with correlation coefficient of 0.8467 is adequately capable of estimating the cost of water and sewer replacement/rehabilitation projects. The GEP-based design equation can easily be used for predesign purposes to help allocate budgets and available limited resources effectively.
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Khan, Sangeen, Mohsin Ali Khan, Adeel Zafar, Muhammad Faisal Javed, Fahid Aslam, Muhammad Ali Musarat, and Nikolai Ivanovich Vatin. "Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence." Materials 15, no. 1 (December 22, 2021): 39. http://dx.doi.org/10.3390/ma15010039.

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The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.
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Amin, Muhammad Nasir, Izaz Ahmad, Mudassir Iqbal, Asim Abbas, Kaffayatullah Khan, Muhammad Iftikhar Faraz, Anas Abdulalim Alabdullah, and Shahid Ullah. "Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete." Materials 15, no. 13 (June 29, 2022): 4573. http://dx.doi.org/10.3390/ma15134573.

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Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.
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Javed, Muhammad Faisal, Furqan Farooq, Shazim Ali Memon, Arslan Akbar, Mohsin Ali Khan, Fahid Aslam, Rayed Alyousef, Hisham Alabduljabbar, and Sardar Kashif Ur Rehman. "New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach." Crystals 10, no. 9 (August 22, 2020): 741. http://dx.doi.org/10.3390/cryst10090741.

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The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been utilized for establishing a prediction model for the axial behavior of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.
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Muhammad, Mohd Khairul Idlan, Shamsuddin Shahid, Mohammed Magdy Hamed, Sobri Harun, Tarmizi Ismail, and Xiaojun Wang. "Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia." Water 14, no. 18 (September 13, 2022): 2858. http://dx.doi.org/10.3390/w14182858.

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Reliable projections of evapotranspiration (ET) are important for agricultural and water resources development, planning, and management. However, ET projections using well established empirical models suffer from uncertainty due to their dependency on many climatic variables. This study aimed to develop temperature-based empirical ET models using Gene Expression Programming (GEP) for the reliable estimation and projection of ET in peninsular Malaysia within the context of global warming. The efficiency of the GEP-generated equation was compared to the existing methods. Finally, the GEP ET formulas were used to project ET from the downscaled and projected temperature of nine global climate models (GCMs) for four Representative Concentration Pathways (RCPs), namely, RCP 2.6, 4.5, 6.0, and 8.5, at ten locations of peninsular Malaysia. The results revealed improved performance of GEP models in all standard statistics. Downscaled temperatures revealed a rise in minimum and maximum temperatures in the range of 2.47–3.30 °C and 2.79–3.24 °C, respectively, during 2010–2099. The ET projections in peninsular Malaysia showed changes from −4.35 to 7.06% for RCP2.6, −1.99 to 16.76% for RCP4.5, −1.66 to 22.14% for RCP6.0 and −0.91 to 39.7% for RCP8.5 during 2010−2099. A higher rise in ET was projected over the northern peninsula than in the other parts.
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22

Ramesh, A., M. Hajihassani, and A. Rashiddel. "Ground Movements Prediction in Shield-Driven Tunnels using Gene Expression Programming." Open Construction & Building Technology Journal 14, no. 1 (September 25, 2020): 286–97. http://dx.doi.org/10.2174/1874836802014010286.

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Introduction: The increase in population and traffic in metropolitan areas has led to the development of underground transportation spaces. Therefore, the estimation of the surface settlement caused by the construction of underground structures should be accurately considered. Several methods have been developed to predict tunneling-induced surface settlement. Among these methods, artificial intelligence-based methods have received much attention in recent years. This paper is aimed to develop a model based on Gene Expression Programming (GEP) algorithm to predict surface settlement induced by mechanized tunneling. Methods: For this purpose, Tehran Metro Line 6 was simulated numerically to investigate the effects of different parameters on the surface settlement, and 85 datasets were prepared from numerical simulations. Subsequently, several GEP models were implemented using the obtained datasets from numerical simulations and finally, a model with 30 chromosomes and 3 genes was selected as the optimum model. Results: A comparison was made between obtained maximum surface settlements by the proposed GEP model and numerical simulation. The results demonstrated that the proposed model could predict surface settlement induced by mechanized tunneling with a high degree of accuracy. Conclusion: Finally, a mathematical equation was derived from the proposed GEP model, which can be easily used for surface settlement prediction.
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Mohammadrezapour, Omolbani, Jamshid Piri, and Ozgur Kisi. "Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)." Water Supply 19, no. 2 (April 30, 2018): 392–403. http://dx.doi.org/10.2166/ws.2018.084.

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Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.
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Bonakdari, Hossein, Azadeh Gholami, Amir Mosavi, Amin Kazemian-Kale-Kale, Isa Ebtehaj, and Amir Hossein Azimi. "A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle." Entropy 22, no. 11 (October 26, 2020): 1218. http://dx.doi.org/10.3390/e22111218.

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This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope (St¯) value comprehensively using a Gene Expression Programming (GEP) by knowing the initial information (discharge (Q) and mean sediment size (d50)) related to the intended problem. An explicit and simple equation of the St¯ of banks and the geometric and hydraulic parameters of flow is introduced based on the GEP in combination with the previous shape profile equation related to previous researchers. Therefore, a reliable numerical hybrid model is designed, namely Entropy-based Design Model of Threshold Channels (EDMTC) based on entropy theory combined with the evolutionary algorithm of the GEP model, for estimating the bank profile shape and also dimensions of threshold channels. A wide range of laboratory and field data are utilized to verify the proposed EDMTC. The results demonstrate that the used Shannon entropy model is accurate with a lower average value of Mean Absolute Relative Error (MARE) equal to 0.317 than a previous model proposed by Cao and Knight (1997) (MARE = 0.98) in estimating the bank profile shape of threshold channels based on entropy for the first time. Furthermore, the EDMTC proposed in this paper has acceptable accuracy in predicting the shape profile and consequently, the dimensions of threshold channel banks with a wide range of laboratory and field data when only the channel hydraulic characteristics (e.g., Q and d50) are known. Thus, EDMTC can be used in threshold channel design and implementation applications in cases when the channel characteristics are unknown. Furthermore, the uncertainty analysis of the EDMTC supports the model’s high reliability with a Width of Uncertainty Bound (WUB) of ±0.03 and standard deviation (Sd) of 0.24.
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Khan, Mohsin Ali, Furqan Farooq, Mohammad Faisal Javed, Adeel Zafar, Krzysztof Adam Ostrowski, Fahid Aslam, Seweryn Malazdrewicz, and Mariusz Maślak. "Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches." Materials 15, no. 1 (December 22, 2021): 58. http://dx.doi.org/10.3390/ma15010058.

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To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.
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Khan, Mohsin Ali, Shazim Ali Memon, Furqan Farooq, Muhammad Faisal Javed, Fahid Aslam, and Rayed Alyousef. "Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest." Advances in Civil Engineering 2021 (January 28, 2021): 1–17. http://dx.doi.org/10.1155/2021/6618407.

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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.
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Amin, Muhammad Nasir, Izaz Ahmad, Asim Abbas, Kaffayatullah Khan, Muhammad Ghulam Qadir, Mudassir Iqbal, Abdullah Mohammad Abu-Arab, and Anas Abdulalim Alabdullah. "Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches." Materials 15, no. 17 (August 26, 2022): 5908. http://dx.doi.org/10.3390/ma15175908.

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This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.
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Achite, Mohammed, Muhammad Jehanzaib, Mohammad Taghi Sattari, Abderrezak Kamel Toubal, Nehal Elshaboury, Andrzej Wałęga, Nir Krakauer, Ji-Young Yoo, and Tae-Woong Kim. "Modern Techniques to Modeling Reference Evapotranspiration in a Semiarid Area Based on ANN and GEP Models." Water 14, no. 8 (April 9, 2022): 1210. http://dx.doi.org/10.3390/w14081210.

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Evapotranspiration (ET) is a significant aspect of the hydrologic cycle, notably in irrigated agriculture. Direct approaches for estimating reference evapotranspiration (ET0) are either difficult or need a large number of inputs that are not always available from meteorological stations. Over a 6-year period (2006–2011), this study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Gene Expression Programming (GEP) machine learning approaches for estimating daily ET0 in a meteorological station in the Lower Cheliff Plain, northwest Algeria. ET0 was estimated using the FAO-56 Penman–Monteith (FAO56PM) equation and observed meteorological data. The estimated ET0 using FAO56PM was then used as the target output for the machine learning models, while the observed meteorological data were used as the model inputs. Based on the coefficient of determination (R2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (EF), the RBFNN and GEP models showed promising performance. However, the FFNN model performed the best during training (R2 = 0.9903, RMSE = 0.2332, and EF = 0.9902) and testing (R2 = 0.9921, RMSE = 0.2342, and EF = 0.9902) phases in forecasting the Penman–Monteith evapotranspiration.
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Ouaer, Hocine, Amir Hossein Hosseini, Menad Nait Amar, Mohamed El Amine Ben Seghier, Mohammed Abdelfetah Ghriga, Narjes Nabipour, Pål Østebø Andersen, Amir Mosavi, and Shahaboddin Shamshirband. "Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids." Applied Sciences 10, no. 1 (December 31, 2019): 304. http://dx.doi.org/10.3390/app10010304.

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Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.
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30

Najafzadeh, Mohammad, Mohammad Rezaie Balf, and Esmat Rashedi. "Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models." Journal of Hydroinformatics 18, no. 5 (March 19, 2016): 867–84. http://dx.doi.org/10.2166/hydro.2016.212.

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Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models are investigated. Performances of the testing results for these models are compared with the traditional approaches based on regression methods. The uncertainty prediction of the MT was quantified and compared with those of existing models. Also, sensitivity analysis was performed to assign effective parameters on the scour depth prediction.
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31

Mohammadzadeh S., Danial, Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati, and Joseph H. M. Tah. "Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model." Infrastructures 4, no. 2 (May 14, 2019): 26. http://dx.doi.org/10.3390/infrastructures4020026.

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In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
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32

Jamali Nazari, Ali, Dariush Sardari, Ahmad Reza Vali, and Keivan Maghooli. "Computer Implementation of a New Therapeutic Model for GBM Tumor." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/481935.

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Modeling the tumor behavior in the host organ as function of time and radiation dose has been a major study in the previous decades. Here the effort in estimation of cancerous and normal cell proliferation and growth in glioblastoma multiform (GBM) tumor is presented. This paper introduces a new mathematical model in the form of differential equation of tumor growth. The model contains dose delivery amount in the treatment scheme as an input term. It also can be utilized to optimize the treatment process in order to increase the patient survival period. Gene expression programming (GEP) as a new concept is used for estimating this model. The LQ model has also been applied to GEP as an initial value, causing acceleration and improvement of the algorithm estimation. The model shows the number of the tumor and normal brain cells during the treatment process using the status of normal and cancerous cells in the initiation of treatment, the timing and amount of dose delivery to the patient, and a coefficient that describes the brain condition. A critical level is defined for normal cell when the patient’s death occurs. In the end the model has been verified by clinical data obtained from previous accepted formulae and some of our experimental resources. The proposed model helps to predict tumor growth during treatment process in which further treatment processes can be controlled.
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33

Ghasemi, S., S. Vaghar, M. Pourzafar, H. Dehghani, and A. Heidarpour. "A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming." Journal of Mining and Metallurgy, Section B: Metallurgy 56, no. 2 (2020): 237–45. http://dx.doi.org/10.2298/jmmb190924012g.

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Regarding the high corrosion resistance of brass in sulfuric acid, its leaching process is the most important step in hydrometallurgical recovery of brass scraps. In this study, the electrochemical dissolution of brass chips in sulfuric acid has been investigated. The electrochemical cell voltage depends on various parameters. Regarding the complexity of electrochemical dissolution, the system voltage could not be easily predicted based on the operational parameters of the cell. So, it is necessary to use modeling techniques to predict cell voltage. In this study, 139 leaching experiments were conducted under different conditions. Using the experimental results and gene expression programming (GEP), parameters such as acid concentration, current density, temperature and anode-cathode distance were entered as the inputs and the voltage of the electrochemical dissolution was predicted as the output. The results showed that GEP-based model was capable of predicting the voltage of electrochemical dissolution of brass alloy with correlation coefficient of 0.929 and root square mean error (RSME) of 0.052. Based on the sensitivity analysis on the input and output parameters, acid concentration and anode-cathode distance were the most and least effective parameters, respectively. The modeling results confirmed that the proposed model is a powerful tool in designing a mathematical equation between the parameters of electrochemical dissolution and the voltage induced by variation of these parameters.
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34

Mitra, Amit Kumar, Sanjoy Dey, Andrew Hangsleben, Michael Steinbach, Vipin Kumar, and Brian Van Ness. "Generation of a Predictive Score for Ixazomib Response in Multiple Myeloma." Blood 124, no. 21 (December 6, 2014): 5695. http://dx.doi.org/10.1182/blood.v124.21.5695.5695.

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Abstract Multiple myeloma (MM) is the second-most common hematopoietic malignancy in the United States accounting for 1% of all cancers and 10% of all hematologic malignancies. Despite recent improvements in treatment strategies including the emergence of proteasome inhibitors (PIs) as effective chemotherapeutic agents, MM still remains difficult to cure with median survival rate of around 7 years, primarily due to wide inter-individual variation in response to treatment. We believe such heterogeneity in response to PIs is governed by the underlying molecular characteristics of the tumor including alterations in gene expression profile (GEP). In the current study, we used a panel of Human Myeloma Cell Lines (HMCLs) representing the gamut of biological and genetic heterogeneity in MM to evaluate the gene expression signatures associated with response to the second-generation PI Ixazomib and produced a predictive score (PI score) for Ix response. HMCLs (n=45) were treated with increasing concentrations of Ixazomib used as single agent and half maximal inhibitory concentration (IC50) values were determined using cell viability equation. Gene expression profiling data was obtained as publicly available data from the Keats lab website at TGen (http://www.keatslab.org/myeloma-cell-lines). Genes with high expression value and high standard deviation beyond the median values were pre-filtered and log expression values were normalized by subtracting mean expression of individual genes across all the samples and the housekeeping genes (GAPDH). Subsequently, analysis of correlation between Ix IC50 data and GEP data and the False Discovery Rate (FDR) based on 1000 random permutations were performed to identify true patterns of genes that are highly predictive of Ix response and to look for the top genes that could discriminate between the top sensitive and top resistant cell lines. Gene clusters were identified that correlated with response and will be presented. Our results will demonstrate in vitro modeling of response using GEP approaches that may provide predictive scoring algorithms of a defined set of genes that will be useful in clinical evaluation of drug choice in treating individual patients. Disclosures No relevant conflicts of interest to declare.
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35

Amin, Muhammad Nasir, Mudassir Iqbal, Arshad Jamal, Shahid Ullah, Kaffayatullah Khan, Abdullah M. Abu-Arab, Qasem M. S. Al-Ahmad, and Sikandar Khan. "GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism." Polymers 14, no. 10 (May 16, 2022): 2016. http://dx.doi.org/10.3390/polym14102016.

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Reinforced concrete structures are subjected to frequent maintenance and repairs due to steel reinforcement corrosion. Fiber-reinforced polymer (FRP) laminates are widely used for retrofitting beams, columns, joints, and slabs. This study investigated the non-linear capability of artificial intelligence (AI)-based gene expression programming (GEP) modelling to develop a mathematical relationship for estimating the interfacial bond strength (IBS) of FRP laminates on a concrete prism with grooves. The model was based on five input parameters, namely axial stiffness (Eftf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of the groove (hg), and IBS was considered the target variable. Ten trials were conducted based on varying genetic parameters, namely the number of chromosomes, head size, and number of genes. The performance of the models was evaluated using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The genetic variation revealed that optimum performance was obtained for 30 chromosomes, 11 head sizes, and 4 genes. The values of R, MAE, and RMSE were observed as 0.967, 0.782 kN, and 1.049 kN for training and 0.961, 1.027 kN, and 1.354 kN. The developed model reflected close agreement between experimental and predicted results. This implies that the developed mathematical equation was reliable in estimating IBS based on the available properties of FRPs. The sensitivity and parametric analysis showed that the axial stiffness and width of FRP are the most influential parameters in contributing to IBS.
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Babaeian, Mohammad, Farhang Sereshki, Mohammad Ataei, Micah Nehring, and Sadjad Mohammadi. "Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining." Mining 3, no. 2 (May 27, 2023): 304–33. http://dx.doi.org/10.3390/mining3020019.

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Blasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no models that can predict the flyrock distance that may be applicable across mining operations in general. In this regard, the Jajarm bauxite mine complex was used as a case study. The purpose of this study was to develop and evaluate different methods that can predict flyrock distance. For this purpose, soft computing models were developed using generalized regression neural network (GRNN), gene expression programming (GEP) and genetic-algorithm-based GRNN (GA-GRNN) methods. To obtain statistical models, multivariable regression was applied in the form of linear and nonlinear equations. A flyrock index was introduced using a classification system developed by incorporating fuzzy decision-making trial and evaluation methods (fuzzy DEMATEL). In order to achieve this goal, the data of 118 blasts in eight mines of the Jajarm bauxite complex were collected and used. Following this, four performance benchmarks were applied: the coefficient of determination (R2), variance accounted for (VAF), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The performance of the models was evaluated, and they were compared with each other as well as with the most common previous empirical models. The obtained results indicate that the GA-GRNN model has a higher performance in predicting the flyrock distance in actual cases compared to the other models. At first, data on factors that were the main cause of flyrock (and had a direct impact on it) were collected and classified from different blasts. Then, using the collected data, 19 different combinations were established, which can be used to provide the appropriate predictive equation. The purpose of this work is to more accurately predict flyrock and prevent heavy damage to buildings and mining machines across the mining complex.
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37

Khaskheli, Muhammad Bilawal, Shumin Wang, Xiaoshan Yan, and Yuehan He. "Innovation of the Social Security, Legal Risks, Sustainable Management Practices and Employee Environmental Awareness in The China–Pakistan Economic Corridor." Sustainability 15, no. 2 (January 5, 2023): 1021. http://dx.doi.org/10.3390/su15021021.

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This research is about the China–Pakistan Economic Corridor (CPEC), which is an important and first project of the “Belt and Road” initiative (BRI). BRI is the framework and manifesto for the wide-ranging, fundamental collaboration signed by China and Pakistan in 2013. The CPEC vision and mission were initiated to develop economic growth and facilitate free trade, the people’s living standards of Pakistan and China through bilateral investments, trade, cultural exchanges, and economic activities between both countries. The initial investment for the project was $46 billion, with a tentative duration of fifteen years. This research aimed to inquire into the effects of legal risks (LR), social security (SS), and employee environmental awareness (EEA) on the project performance (PP) of the CPEC. It further investigates the significance of gender empowerment perspectives (GEP). A research framework consisting of this quantitative analysis and the bilateral impacts of the study were explored through several policies scenarios into 2025. The results of the risk analysis were rated on a Likert scale. A questionnaire survey was used in order to collect data and test the research framework and hypotheses. An empirical test was conducted using a dataset with partial least square structural equation modeling (PLS-SEM) to validate the study.
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38

Peng, Jie, Jianwen Du, and Liping Wang. "The study of ionic conductivity of the Li10GeP2S12 type Solid State Electrolyte by an extrapolation method and a deep-learning method." Journal of Physics: Conference Series 2557, no. 1 (July 1, 2023): 012034. http://dx.doi.org/10.1088/1742-6596/2557/1/012034.

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Abstract An extrapolation method is usually applied when Ab initio molecular dynamics (AIMD) is applied to studying ionic conductivity in solid-state electrolytes in lithium-ion batteries. As the ions move slowly in solid-state electrolytes, the first-principles method typically involves computationally intensive calculations, and it can take significant time to obtain accurate results. First-principles method is too expensive for the time scale required at room temperature. The classical molecular dynamics method is typically applied to systems containing thousands of atoms and can simulate the system’s behavior over nanoseconds. During the simulation, the positions and velocities of the atoms are updated at discrete time intervals, allowing the system’s behavior to be studied over time. However, its accuracy depends on the empirical force-field libraries. Limited by the computational resource, the previous studies applied the extrapolation method to obtain the room temperature ionic conductivity, which was not accurate because the linear relationship in the Arrhenius equation was not valid in a wide range of temperatures. Deepmd-Kit is a tool that integrates these two different computational approaches. The extrapolation and Deepmd methods were applied to the materials Li10GeP2S12, Li10SiP2S12, Li10GePS12Cl, and Li10SiPS12Cl, respectively. Both methods showed that the lithium ions favor the c direction when diffusing in the LGPS-type solid-state electrolytes. The ionic conductivity is more accurate with the dependent method compared with experiments.
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39

Nee, Janpou. "Nonlinear integral equation from the BCS gap equations of superconductivity." Nonlinear Analysis: Real World Applications 11, no. 1 (February 2010): 190–97. http://dx.doi.org/10.1016/j.nonrwa.2008.10.047.

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40

Mende, Tamás, and András Roósz. "Calculation of the Immiscibility Gap by ESTPHAD Method." Materials Science Forum 659 (September 2010): 423–28. http://dx.doi.org/10.4028/www.scientific.net/msf.659.423.

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By using the thermodynamic equations, the ESTPHAD (Estimation of Phase Diagrams) method developed by us has been made suitable for calculating the immiscibility gap of monotectic systems. 2 special parameters were introduced in the basic equation of ESTPHAD which ensure the description of the characteristics of monotectic curve on the basis of the boundary conditions created by us. By means of the monotectic ESTPHAD equation, the equations of separation temperatures of monotectic parts in the Zn-Bi and Al-Bi systems were calculated (with an accuracy of +/- 10K) on the basis of the reference data.
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41

GUPTA, H. C. "ELECTRON-PHONON INTERACTION FOR AN ANALYTIC SOLUTION TO THE BCS EQUATION FOR THE HIGH TEMPERATURE SUPERCONDUCTORS." Modern Physics Letters B 05, no. 20 (August 30, 1991): 1349–53. http://dx.doi.org/10.1142/s0217984991001647.

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An energy dependent electron-phonon interaction has been used in the BCS equation. This provides an exactly solvable analytic solution to the BCS equation for the superconducting transition temperature and the gap parameter at absolute zero. These analytically obtained equations reduce to standard BCS form when temperature is small. These equations are applicable to low as well as high temperature superconductors successfully for their superconducting transition temperature and the energy gap parameter.
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42

El-Tantawy, S. A., Alvaro H. Salas, and Castillo H. Jairo E. "Stability analysis and novel solutions to the generalized Degasperis Procesi equation: An application to plasma physics." PLOS ONE 16, no. 9 (September 28, 2021): e0254816. http://dx.doi.org/10.1371/journal.pone.0254816.

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In this work two kinds of smooth (compactons or cnoidal waves and solitons) and nonsmooth (peakons) solutions to the general Degasperis-Procesi (gDP) equation and its family (Degasperis-Procesi (DP) equation, modified DP equation, Camassa-Holm (CH) equation, modified CH equation, Benjamin-Bona-Mahony (BBM) equation, etc.) are reported in detail using different techniques. The single and periodic peakons are investigated by studying the stability analysis of the gDP equation. The novel compacton solutions to the equations under consideration are derived in the form of Weierstrass elliptic function. Also, the periodicity of these solutions is obtained. The cnoidal wave solutions are obtained in the form of Jacobi elliptic functions. Moreover, both soliton and trigonometric solutions are covered as a special case for the cnoidal wave solutions. Finally, a new form for the peakon solution is derived in details. As an application to this study, the fluid basic equations of a collisionless unmagnetized non-Maxwellian plasma is reduced to the equation under consideration for studying several nonlinear structures in the plasma model.
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43

Brezhnev, Yurii V. "What does integrability of finite-gap or soliton potentials mean?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1867 (June 27, 2007): 923–45. http://dx.doi.org/10.1098/rsta.2007.2056.

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In the example of the Schrödinger/KdV equation, we treat the theory as equivalence of two concepts of Liouvillian integrability: quadrature integrability of linear differential equations with a parameter (spectral problem) and Liouville's integrability of finite-dimensional Hamiltonian systems (stationary KdV equations). Three key objects in this field—new explicit Ψ -function, trace formula and the Jacobi problem—provide a complete solution. The Θ -function language is derivable from these objects and used for ultimate representation of a solution to the inversion problem. Relations with non-integrable equations are also discussed.
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44

Samdarshi, S. K., and S. C. Mullick. "Generalized Analytical Equation for the Top Heat Loss Factor of a Flat-Plate Solar Collector With N Glass Covers." Journal of Solar Energy Engineering 116, no. 1 (February 1, 1994): 43–46. http://dx.doi.org/10.1115/1.2930064.

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A generalized analytical equation for the top heat loss factor of a flat-plate collector with one or more glass covers has been developed. The maximum computational errors resulting from the use of the analytical equation with several simplifications are ± 5 percent compared to numerical solution of the set of heat balance equations. The analytical equation is considerably more accurate than the available semi-empirical equations over the entire range of variables covered. An additional advantage of the proposed technique over the semi-empirical equations is that results can be obtained for different values of sky temperature, using any given correlation for convective heat transfer in the air gap spacings, and for any given values of fluid (air in the present case) properties.
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45

Khilji, Nasir M., and Akhtar Mahmood. "Military Expenditures and Economic Growth in Pakistan." Pakistan Development Review 36, no. 4II (December 1, 1997): 791–808. http://dx.doi.org/10.30541/v36i4iipp.791-808.

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This paper explores the impacts of defence expenditures on economic growth and other major economic variables in the Pakistan economy over the period 1972-1995. The results of Granger-causality tests show that there is bi-directional feedback between the defence burden and GDP growth. We test four different single equation models that are widely used in the defence literature. In these frameworks we generally find the defence burden to be negatively related to GDP growth. Finally, we specify a three-equation model which explains GDP growth, average propensity to save, and the defence ratio. In single equation estimations of the savings ratio and the defence burden, we uncover some interesting relationships. The savings ratio is affected positively by the defence ratio, and negatively by the inflation rate. The Pakistani defence burden is impacted negatively by the Indian defence burden and positively by the government budget. When all three equations are estimated as a system to account for feedback and covariance between these equations, these effects are diminished and go down in statistical significance.
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46

Perdue, Mitzi. "The Schadt Equation." Genetic Engineering & Biotechnology News 33, no. 2 (January 15, 2013): 30–31. http://dx.doi.org/10.1089/gen.33.2.20.

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47

Ahmed, Mahmoud M., Ayman G. Awadallah, Nabil A. Awadallah, and Wael T. Ahmed. "Assessment of Various Empirical Soil Loss Estimation Equations in Arid Regions." Journal of Geoscience and Environment Protection 10, no. 01 (2022): 109–22. http://dx.doi.org/10.4236/gep.2022.101008.

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48

SHI, ZHU-PEI, GUOXING HUANG, and RUIBAO TAO. "SOLITON-LIKE PHONON LOCALIZED MODES IN DIATOMIC NONLINEAR LATTICE CHAIN." International Journal of Modern Physics B 05, no. 13 (August 10, 1991): 2237–52. http://dx.doi.org/10.1142/s0217979291000869.

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We have studied the localization of multivibrational states in diatomic nonlinear lattice chain. A simple model Hamiltonian presented here describes a system containing two kinds of phonons. The equations of motion for these boson operators are two partial differential equations with nonlinear coupling in long-wave approximation. With the help of the method of multiple scales, these equations are reduced to the nonlinear Schrödinger equation. It is shown that soliton-like phonon localized modes, multi-phonon localized modes can exist. The possibility of observing the gap solitons (phonon localized modes in the frequency gap) in diatomic nonlinear lattice chain is predicted.
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49

DUNNE, GERALD V. "SYMMETRIES IN THE GROSS-NEVEU PHASE DIAGRAM." International Journal of Modern Physics A 25, no. 02n03 (January 30, 2010): 616–26. http://dx.doi.org/10.1142/s0217751x10048901.

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The existence of crystalline condensates in the temperature and chemical potential phase diagram of the Gross-Neveu models can be traced to intricate symmetries of the associated inhomogeneous gap equation. The gap equation based on the Ginzburg-Landau expansion is precisely the mKdV or AKNS hierarchy of integrable nonlinear equations for the Gross-Neveu model with discrete or continuous chiral symmetry, respectively. The former model also has a dense-dilute symmetry that is due to the energy-reflection duality of the underlying quasi-exactly soluble spectral operators.
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50

Banerjea, Sudeshna, and B. N. Mandal. "Scattering of water waves by a submerged thin vertical wall with a gap." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 39, no. 3 (January 1998): 318–31. http://dx.doi.org/10.1017/s0334270000009425.

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AbstractA train of surface water waves normally incident on a thin vertical wall completely submerged in deep water and having a gap, experiences reflection by the wall and transmission through the gaps above and in the wall. Using Havelock's expansion of water wave potential, two different integral equation formulations of the problem are presented. While the first formulation involves multiple integral equations which are solved here by reducing them to a singular integral equation with Cauchy kernel in a double interval, the second formulation involves a first-kind singular integral equation in a double interval with a combination of logarithmic and Cauchy kernel, the solution of which is obtained by utilizing the solution of a singular integral equation with Cauchy kernel in (0, ∞) and also in a double interval. The reflection coefficient is evaluated by both the methods.
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