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Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts. Highlights: An efficient hybrid ensemble model based on HBA is proposed for predicting tunnel overbreak caused by blasting. An explicit high-accuracy prediction equation for tunnel overbreak is constructed using GEP technology. The model performance is comprehensively evaluated through various metrics and non-parametric statistical tests. The constructed model demonstrates excellent generalization capabilities and interpretability. [ABSTRACT FROM AUTHOR]

Abstract The cast in-situ pile is a widely used type of deep foundations which its execution in civil projects is increasing daily. The use of ordinary concrete in this type of piles causes technical and executive problems, a decrease in the compressive strength (CS) of concrete, and an increase in the permeability under the ground level. But use of the self-compacting concrete in the cast in-situ piles while increasing the CS of concrete ensures proper compaction, increase in the execution speed, and easy placing of concrete. In this article, utilizing the data obtained from the laboratory results and also the application of soft computing techniques, predicting the degree of CS of self-compacting concrete (SCC) in concrete piles was investigated. To estimate the CS of SCC, a total number of 7 inputs were implemented. Then, using gene expression programming (GEP) a model was presented for estimating the CS of SCC in the cast in-situ piles. The results of the neural network showed a precision of 99.98% which exhibits the high accuracy of the model. The use of this model could greatly help persons, companies, and research centers in the preparation and construction of self-compacting concrete with the desired CS.

Abstract Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw1), between 200 and 300 (Mw2), and greater than 300 (Mw3). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

Abstract The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

The degradation of the ecosystem and the loss of natural capital have seriously threatened the sustainable development of human society and economy. Currently, most research on Gross Ecosystem Product (GEP) is based on statistical modeling methods, which face challenges such as high modeling difficulty, high costs, and inaccurate quantitative methods. However, machine learning models are characterized by high efficiency, fewer parameters, and higher accuracy. Despite these advantages, their application in GEP research is not widespread, particularly in the area of combined machine learning models. This paper includes both a GEP combination model and an explanatory analysis model. This paper is the first to propose a combined GEP prediction model called Ada-XGBoost-CatBoost (Ada-XG-CatBoost), which integrates the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) algorithms, and SHapley Additive exPlanations (SHAP) model. This approach overcomes the limitations of single-model evaluations and aims to address the current issues of inaccurate and incomplete GEP assessments. It provides new guidance and methods for enhancing the value of ecosystem services and achieving regional sustainable development. Based on the actual ecological data of a national city, data preprocessing and feature correlation analysis are carried out using XGBoost and CatBoost algorithms, AdaGrad optimization algorithm, and the Bayesian hyperparameter optimization method. By selecting the 11 factors that predominantly influence GEP, training the model using these selected feature datasets, and optimizing the Bayesian parameters, the error gradient is then updated to adjust the weights, achieving a combination model that minimizes errors. This approach reduces the risk of overfitting in individual models and enhances the predictive accuracy and interpretability of the model. The results indicate that the mean squared error (MSE) of the Ada-XG-CatBoost model is reduced by 65% and 70% compared to the XGBoost and CatBoost, respectively. Additionally, the mean absolute error (MAE) is reduced by 4.1% and 42.6%, respectively. Overall, the Ada-XG-CatBoost combination model has a more accurate and stable predictive performance, providing a more accurate, efficient, and reliable reference for the sustainable development of the ecological industry. [ABSTRACT FROM AUTHOR]

Although the incidence of cutaneous melanoma (CM) has been increasing annually, the mortality rate has been decreasing, likely due to better prevention, earlier detection, improved surveillance, and the development of new therapies. Current clinical management guidelines by the National Comprehensive Cancer Network (NCCN) are based on patient risk assignment using staging criteria established by the American Joint Committee on Cancer (AJCC). However, some patients with localized disease (stage I–II), generally considered to have a good prognosis, will develop metastatic disease and die, whereas some patients with later stage disease (stage III–IV) will be cured by surgery, adjuvant therapy, and/or systemic therapy. These results emphasize the importance of identifying patients whose risk may be over or underestimated with standard staging. Gene expression profile (GEP) tests are noninvasive molecular tests that assess the expression levels of a panel of validated genes, providing information about tumor prognosis, including the risk of recurrence, metastasis, and cancer-specific death. GEP tests can provide prognostic information beyond standard staging that may aid clinicians and patients in treatment and surveillance management decisions. This review describes how combining clinicopathologic staging with a robust assessment of tumor biology may provide information that will allow more refined intervention and long-term management. [ABSTRACT FROM AUTHOR]

This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP.

The aim of this article is to discuss the possibility of using gender equality plans (GEPs) to build a brand, especially the employer branding (EB) of a research institution. The example of the GEP of the Central Mining Institute-National Research Institute (GIG-PIB) has been used in this article. The research methodology includes an analysis of the existing data and a survey involving all the GIG-PIB employees, not only those in research and scientific positions. The computer-assisted web interviewing (CAWI) research covered five key areas associated with gender equality, such as work–life balance, gender balance in management and decision-making bodies, gender equality in recruitment and career development, gender mainstreaming in the research process and measures to combat gender-based violence. This article presents the results of the survey. The analysis of the results shows that the opportunities for professional, scientific and personal development were positively assessed by the staff of GIG-PIB. Such favourable opinions of employees are an important element of building a competitive advantage based on the brand of the research institution and its Employer Branding.

The high temperature flow data of TiB2/2024 aluminum matrix composites (referred to as TiB2/2024 alloy) was investigated using a Gleeble-3500 thermal simulation testing machine. The experiments were conducted at various deformation temperatures (573 K, 623 K, 673 K, and 723 K), strain rates (0.01s−1, 0.1s−1, 1s−1, and 10s−1), and a maximum deformation of 60%. By comprehensively accounting for the deformation conditions, the relationships between the material parameters α, n, S, f of TiB2/2024 alloy and the deformation temperature, strain, and strain rate were determined, leading to the modification of the Arrhenius model. A constitutive model for TiB2/2024 alloy was constructed using the Gene expression programming (GEP) approach. The flow stress of TiB2/2024 alloy during the compression process was predicted using both the modified Arrhenius model and the GEP model. The statistical analysis was performed to evaluate the prediction accuracy of the two models, and the extended stress-strain data was implemented in finite element simulations of the hot compression process. The results indicate that the flow stress of TiB2/2024 alloy is significantly affected by the strain rate and temperature during the deformation process. The flow stress decreases with increasing temperature and increases with increasing strain rate. Both the modified Arrhenius model and the GEP model can effectively predict the alloy's flow stress. However, the modified Arrhenius model exhibits greater prediction accuracy than the GEP model.

Ecosystem carbon storage (ECS) is a critical consideration in reducing the impact of global warming and tackling environmental challenges, positioning it at the forefront of contemporary research. Due to the significant differences in the influence of land usage patterns on ECS in various policy contexts and China's commitment to attaining a carbon-neutral status, a model simulating different scenarios is needed to analyze the spatiotemporal characteristics and evolutionary process of carbon storage in terrestrial ecosystems accurately. To address this challenge, this study established a coupling model of "Geographical analysis -Evolution analysis -Predicting (GEP)" for assessing ecosystem ECS and analyzing its spatial characteristics and evolutionary patterns and projecting the spatial distribution of ECS under various developmental scenarios, which analyzed variations in ECS across different levels of magnitude and delineated the changing areas across a range of varying scenarios in the future additionally. The outcomes suggested that the ECS decreased by 1.17 × 106 t from 1990 to 2020, which pertaining to the utilization transfer of land in the area, whose change in ECS levels with a positive trend. It is predicted that the ECS will grow by 1.15 × 106 t and 1.44 × 106 t, in 2030 and 2060 compared with 2020 within the framework of natural development scenario (NDS), while within the framework of ecological protection scene (EPS), ECS will increase significantly, increasing by 3.06 × 106 t and 4.44 × 106 t. There will be more areas where ECS increases within the framework of EPS, by comparing with the NDS. This study offers a comprehensive analysis of Hanzhong City's carbon storage trends, demonstrating its significant impact on climate change mitigation and serving as a predictive model for similar regions, which underscores the importance of localized carbon management strategies, offering valuable insights for local governments in formulating effective climate adaptation and mitigation policies. [ABSTRACT FROM AUTHOR]

This study presents the development of rainfall forecast models using potential climate indices for the Kimberley region of Western Australia, using 100 years of rainfall and climate indices data for four rainfall stations. Three different modeling techniques: multiple linear regression (MLR), autoregressive moving average with exogenous input (ARIMAX), and gene-expression programming (GEP) were applied to develop prediction models. Preliminary analysis suggests that Western Tropical Indian Ocean (WTIO) and Southern Oscillation Index (SOI) have significant impacts on summer rainfall generation for the region. Developed models' performances were evaluated using Pearson correlation coefficient (r ), root mean square error ( RMSE ), mean absolute error (M A E) , Nash–Sutcliffe efficiency (N S E) , and refined Willmot index of agreement ( d r ). It is found that the GEP model exclusively outperforms the other two alternatives. In the calibration period, the GEP model resulted in a Pearson correlation coefficient (r) values ranging from 0.76 to 0.85, which are significantly higher than that achieved from MLR (0.32 to 0.44) and ARIMAX (0.53 to 0.83) models, while for the validation period, the correlation values for the models ranged from 0.74 to 0.87 for GEP, 0.35 to 0.51 for MLR and 0.59 to 0.77 for ARIMAX models. Considering other statistical error statistics it can be concluded that the GEP model is the best representative seasonal rainfall forecasting model for the region. [ABSTRACT FROM AUTHOR]

This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP. [ABSTRACT FROM AUTHOR]

This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 ( R 2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model's effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model's potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values. [ABSTRACT FROM AUTHOR]

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Predicting the solubility of hydrogen (H 2) in aqueous solutions is crucial for studying reactions of hydrogen in the formation, which also affects the security and optimal design of hydrogen storage. In this research, five robust machine learning (ML) algorithms, namely adaptive boosting decision tree (AdaBoost-DT), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting decision tree (GB-DT), gradient boosting support vector regression (GB-SVR), and k-nearest neighbors (KNN) and three powerful white-box techniques, namely gene expression programming (GEP), genetic programming (GP), and group method of data handling (GMDH) were developed to accurately predict H 2 solubility in pure and saline water systems. To this aim, a widespread databank containing 427 experimental data points was collected, and temperature, pressure, and salt concentration (mSalt) were considered as input variables. The validity and precision of the developed models were assessed utilizing several statistical and graphical tests. Results demonstrate that the AdaBoost-SVR smart model could obtain a superior performance and provides precise predictions with root mean square error (RMSE) of 0.000115 and determination coefficient (R2) of 0.9973. Among the white-box models, the GEP provided the best results with an RMSE of 0.000362 and an R2 of 0.9542. Although the accuracy of GEP is slightly lower than that of AdaBoost-SVR, it offers explicit and simple mathematical formula for calculating H 2 solubility, which is the main advantage of white box models. The results also demonstrated that AdaBoost-SVR outperforms cubic equations of state (EOSs) such as Peng-Robinson (PR), Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Zudkevitch-Joffe (ZJ). Besides, trend analysis showed that AdaBoost-SVR model could match actual trends of H 2 solubility change versus temperature and pressure. Finally, outlier detection analysis using the Leverage technique indicated that the majority of data points used for modeling (nearly 94 %) are reliable and placed in the valid zone. [Display omitted] • The solubility of hydrogen in aqueous solutions is modeled using a large data bank. • Three white-box and five advanced machine learning models are used for modeling. • The AdaBoost-SVR model could obtain a superior performance among all models. • GEP has the best performance among the white-box models. • The AdaBoost-SVR model also outperformed all cubic equations of state. [ABSTRACT FROM AUTHOR]

In the last few years, nivolumab has become the standard of care for advanced-stage lung cancer patients. Unfortunately, up to 60% of patients do not respond to this treatment. In our study, we identified variations in gene expression related to primary resistance to immunotherapy. Bronchoscopy biopsies were obtained from advanced non-small cell lung cancer (NSCLC) patients previously characterized as responders or non-responders after nivolumab treatment. Ten tumor biopsies (from three responders and seven non-responders) were analyzed by the differential expression of 760 genes using the NanoString nCounter platform. These genes are known to be involved in the response to anti-PD1/PD-L1 therapy. All the patients were treated with nivolumab. Examining the dysregulated expression of 24 genes made it possible to predict the response to nivolumab treatment. Supervised analysis of the gene expression profile (GEP) revealed that responder patients had significantly higher levels of expression of CXCL11, NT5E, KLRK1, CD3G, GZMA, IDO1, LCK, CXCL9, GNLY, ITGAL, HLA-DRB1, CXCR6, IFNG, CD8A, ITK, B2M, HLA-B, and HLA-A than did non-responder patients. In contrast, PNOC, CD19, TP73, ARG1, FCRL2, and PTGER1 genes had significantly lower expression levels than non-responder patients. These findings were validated as predictive biomarkers in an independent series of 201 patients treated with nivolumab (22 hepatocellular carcinomas, 14 non-squamous cell lung carcinomas, 5 head and neck squamous cell carcinomas, 1 ureter/renal pelvis carcinoma, 120 melanomas, 4 bladder carcinomas, 31 renal cell carcinomas, and 4 squamous cell lung carcinomas). ROC curve analysis showed that the expression levels of ITK, NT5E, ITGAL, and CD8A were the best predictors of response to nivolumab. Further, 13/24 genes showed an adverse impact on overall survival (OS) in an independent, large series of patients with NSCLC (2166 cases). In summary, we found a strong association between the global GEP of advanced NSCLC and the response to nivolumab. The classification of NSCLC patients based on GEP enabled us to identify those patients who genuinely benefited from treatment with immune checkpoint inhibitors (ICIs). We also demonstrated that abnormal expression of most of the markers comprising the genomic signature has an adverse influence on OS, making them significant markers for therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these biomarkers. [ABSTRACT FROM AUTHOR]

Gene Expression Programming (GEP) is a powerful evolutionary algorithm with simple, linear and compact chromosomes, which has been applied in many fields to solve a large variety of complex problems such as logistic regression, function finding and time series prediction. Since online signature data are composed of discrete points, it is difficult to represent by functional forms, resulting in a limited amount of information used in calculating feature values. Hausdorff distance is utilized as a similarity measure to compute the maximum distance between two point sets, which reduces computational complexity compared with other distance measures. The main contributions of this work are: (1) In preprocessing stage, GEP is used to make signature curve continuous and control each parameter to obtain a fitting curve. Curve fitting is to find a suitable function that is the best fitting for a given set of data; (2) In feature extraction stage, curvature and torsion are utilized to construct eight feature sets for characterizing each user's signatures, and then Hausdorff distance is proposed to calculate the distances between feature sets of two signatures to form an eight-dimensional feature vector; (3) In verification stage, combined with Feed-Forward BP Neural Network classifier, distance matrices consisting of feature vectors are trained and tested many times. The best performances can be provided with false rejection rate, false acceptance rate, average error rate and standard deviation. The experimental results implemented on three available online signature databases MCYT-100, SVC2004 and SUSIG indicate the effectiveness and robustness of our proposed method. [ABSTRACT FROM AUTHOR]

Sediment discharge in rivers is among the most important water and environmental engineering issues. The present study employed the published reliable field data set and three white-box data-driven methods, including the group method of data handling (GMDH), gene expression programming (GEP), and the multivariate adaptive regression splines (MARS) approach for modeling sediment discharge in rivers (Qs). In addition, the artificial neural network (ANN) model, known as the popular and widely used black-box data-driven model, was employed for modeling sediment discharge. The performance of the proposed methods was explored by statistical measures, scatter plots, and Taylor and Violin plots. The main feature of white-box data-driven models is that they provide explicit mathematical expressions for the prediction of Qs. The outcomes of the proposed methods provide better and more competitive results than the earlier study conducted using the model tree (MT) approach. Statical measurements and graphical plots indicated that all proposed methods have similar results for the prediction of sediment discharge. However, GMDH and GEP were more accurate than the MARS and ANN models. For the overall evaluation of the proposed models, the ranking mean (RM) method was used. This method showed that the GMDH model with RM = 1.86 had better performance in estimating sediment discharge, followed by the GEP with RM = 2.29, MARS with RM = 2.86, and ANN with RM = 3. It is worth mentioning that simple, explicit mathematical expressions generated by GMDH and MARS were more straightforward calculations for estimating sediment discharge compared to GEP and ANN. [ABSTRACT FROM AUTHOR]

Rates and weightings are specified for every data layer to compensate for the data layer's regional fluctuation and relative importance accordingly using simple additive weighting frameworks (SAWFs). To this end, the present article follows three learning steps using artificial intelligence (AI): In Level 1, SFL, ANN and SVM models are used to improve weighting factors from regional and observational data. In Level 2, the AI strategy of gene expression programming (GEP) is applied to understand further. In Level 3, the results from Level 1 are reused as inputs and observational data. Using the anticipated impact measurement and monitoring (AIMM) approach, the study paves the ground to create some frameworks for mapping indices (subsidence susceptibility). The main results of the AIMM analysis are three-fold: First, the NSE coefficient for SAWF obtained through land subsidence values is approximately 0.41. Second, the NSE value is enhanced to 0.88 by learning the weighting factors at Level 1 using SFL. Third, the NSE value is even further enhanced to 0.92 by Level 2 learning of the weighting factors via GEP. With the subsidence vulnerability index map, it is possible to manage underground water resources to prevent excessive subsidence in probable areas. [ABSTRACT FROM AUTHOR]

This study emphasizes the significance of beam–column joints (BCJs) within reinforced concrete (RC) structures and investigates their performance when subjected to seismic forces. Accurately predicting the load-carrying capacity of exterior BCJs under seismic loading poses a significant challenge. The development of a reliable and user-friendly predictive model is of paramount importance for facilitating cost-effective and safe design practices for RC structures. To address this requirement, we propose an artificial intelligence (AI)-based model that utilizes gene expression programming (GEP) to accurately predict the load-carrying capacity of exterior BCJs under monotonic loading conditions. The model is developed using GEP and utilizes a database of 128 joint load-carrying capacity results of exterior BCJs obtained from a validated finite element (FE) model using ABAQUS, which considers the effects of material and geometric factors, which have often been overlooked in prior studies. These factors encompass multiple aspects, including the beam and column dimensions, concrete material properties, longitudinal reinforcements in beams and columns, and axial loads applied to the columns. This study also compared the results of the proposed GEP model with the numerical data obtained from the validated FE model, demonstrating good accuracy and reliability. The proposed model has the potential to improve the accuracy and reliability of joint load-carrying capacity predictions, thereby aiding the design of safe and cost-effective RC structures. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring for graft rejection is a fundamental tenet of post‐transplant follow‐up. In heart transplantation (HT) in particular, rejection has been traditionally assessed with endomyocardial biopsy (EMB). EMB has potential complications and noted limitations, including interobserver variability in interpretation. Additional tests, such as basic cardiac biomarkers, cardiac imaging, gene expression profiling (GEP) scores, donor‐derived cell‐free DNA (dd‐cfDNA) and the novel molecular microscope diagnostic system (MMDx) have become critical tools in rejection surveillance beyond standard EMB. Methods: This paper describes an illustrative case followed by a review of MMDx within the context of other noninvasive screening modalities for rejection. Conclusions: We suggest MMDx be used to assist with early detection of rejection in cases of discordance between EMB and other noninvasive studies. [ABSTRACT FROM AUTHOR]

BACKGROUND: Endomyocardial biopsy (EMBx) is considered the gold standard for rejection monitoring after heart transplantation; however, it is invasive and histologic interpretation has limitations. Sensitive blood biomarkers, including donor-derived cell-free DNA (dd-cfDNA), have emerged to decrease EMBx frequency. METHODS: We retrospectively reviewed data on 237 patients who underwent heart transplantation at our institution. Of these, 125 patients underwent monitoring using dd-cfDNA, combined with a fewer number of EMBx, and 112 patients underwent monitoring using EMBx only. We compared rates of rejection, graft dysfunction, and survival at 1 year. RESULTS: Median age at time of transplant was 59.8 years, and 77.6% were men. In the dd-cfDNA group, there were significantly fewer episodes of EMBx defined acute cellular rejection (ACR) (2.5% vs 18.8%, p

This paper investigates the effect of jointed rock mass properties on the Minimum Required Caving Span (MRCS) in the block caving method using numeric and heuristic approaches. To do so, the effects of five parameters of jointed rock mass, namely joint set number, joint spacing, joint inclination angle, joint surface friction angle, and undercut depth on MRCS, were investigated using a discrete element code. For this purpose, many numerical models were generated with various rock mass parameters. Moreover, Gene Expression Programming and Artificial Neural Networks were employed to create a heuristic model for MRCS. The model parameters were subjected to sensitivity analysis. All model input parameters showed sensitivity to the model. There are several effective parameters on MRCS, but joint dip and joint set numbers are the most important and the smallest.

Urban landslides have brought challenges to developing countries undergoing urbanization. Rapid approaches to assess ground deformation are required when facing the challenge of insufficient geological survey methods. Additionally, it is indeed a challenge to map landslide-affected areas, especially precipitation-induced landslides, through optical remote sensing methods. This study applied SAR change detection methods to map the slope failure event of the San Jorge Kantutani landfill site in La Paz, Bolivia, which occurred in April 2019, and Multi-Temporal Synthetic Aperture Radar Interferometry (MTInSAR) methods to assess pre- and post-failure ground stability related to this event. We found that the amplitude information of high-resolution COSMO-SkyMed SAR imagery and its texture information can be very useful in landslide mapping, especially in situations in which optical images are not available because of complex meteorological conditions and the similar spectral characteristics between the original land cover and landslide deposits. The MTInSAR analyses found that there was already significant deformation of more than 50 mm/year along the slope direction over this site before the landslide, and such deformation could be clearly discriminated from the surrounding environment. After the landslide event and the remobilization of the landslide deposit, the slope still shows a deformation velocity of more than 30 mm/year. The SAR amplitude change detection and MTInSAR fully exploited the SAR data in landslide studies and were useful in back analyzing the occurred landslides; this could be a good method for monitoring the ground stability of La Paz or even on a national scale over the long term for reducing the catastrophic effects of geological hazards in this landslide-prone city. [ABSTRACT FROM AUTHOR]

It is known that bond strength is affected by the application conditions and methods of adhesive connections. Knowing which method can increase the joint strength more and choosing those methods is important in terms of preventing time loss and financial losses. This study aims to experimentally examine the effects of aluminum alloy and carbon fiber reinforced polymer (CFRP) composite panels on the mechanical properties of adhesive thickness, different temperatures, pressure and filler in adhesive joints and model data. Experiments were carried out by using AA7075-T6/CFRP panels with single lap joint bonding joints, unfilled, 1% and 2% by weight carbon fiber filled, at 19, 50 and 100 °C and also by pressing under 2 MPa pressure without applying pressure. It is understood from the results that the parameters that affect the bond strength the most are the adhesive thickness and the amount of pressing applied on it. The effect of the experiments carried out under these conditions on the tensile failure load values was examined, and also artificial neural network (ANN) and gene expression programming (GEP) models were presented for failure load estimation. The R2 values of the ANN model are 0.9456. The R2 values of the GEP model are 0.9029–0.9572 for training and validation, respectively. High accuracy rates were obtained with both models. It is seen that there is a good agreement between the observed and predicted values in the errors of the training and validation sets. As a result, it was concluded that ANN and GEP models can be used to select the optimum value in similar applications. [ABSTRACT FROM AUTHOR]

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions. [ABSTRACT FROM AUTHOR]

In the construction sector, there remains a continual drive for sustainable development. Due to the negative environmental impact of cement manufacture, self-compacting geopolymer concrete (SGC) is utilized as an environmentally safe alternative of the same. This work primarily focuses on developing mathematical models for predicting the mechanical strength such as compressive, flexural and split strength of SGC prepared using fly ash-blast furnace slag (GGSB). A detailed comprehensive database of the mechanical strength of fly ash-GGSB-based SGC was formulated via an experimental process. The database consists of 396 specimens obtained from 132 different mixes, where the minimum and maximum compressive strength value is 2.48 and 69.33 Mpa, respectively. Geopolymer binder content, specimen age and the ratio of alkaline solution are used as modeling input parameters, as they are the most significant parameters for obtaining the output. The mechanical strength of fly ash-GGSB-based SGC was predicted using simplified and practical mathematical equations based on the gene-based expression programming (GBEP) model. The average co-relation coefficient (R) value for GBEP is 0.9960, 0.9934 and 0.9913 for compressive, split and flexural strength in the training stage and the same is 0.9962, 0.9927 and 0.9951 at the testing phase, respectively. Also, the root mean square error, mean square error, mean absolute error, relative absolute error, root relative squared error, relative squared error and R-square predicted the experimental data results very precisely and with very few errors, it is also preferable over other models as they generate simple equations from the process and hence simplify the whole model. [ABSTRACT FROM AUTHOR]

According to the advanced domestic and global experience in ecosystem value quantitative research, on the deep analysis and conclusion of other recent GEP accounting cases, we established an accounting index system of urban GEP and presented accounting techniques on the basis of characteristic analysis of urban ecological system. We conducted application of urban GEP accounting in Yantian district of Shenzhen, as a typical area, and evaluated its natural ecosystem value and living environment ecosystem value quantitatively. Our results indicated that urban GEP of Yantian district in 2013 was 102.976 billion yuan, while in 2017 was 109.203 billion yuan. Compared with 2013, urban GEP of Yantian district increased by 6.227 billion yuan in 2017. The annual growth rate was 1.51%. The various functions provided by the urban ecosystem were valued through urban GEP accounting research. So that managers and public could more directly recognize the value of ecological resources, in the meanwhile, it could also provide technical support for the ecological audit, ecological compensation and ecological environment damage assessment. The study can do contribute to regional environmental management and decision-making. [ABSTRACT FROM AUTHOR]

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Scour depth prediction is an important aspect of designing a bridge pier structure in a river. Proper modeling of scour depth ensures the sustainability of the structure. An attempt is made to develop a scour depth model for the bridge pier using an adaptive network-based fuzzy inference system (ANFIS) and gene expression programming (GEP). The scour depth is found to be influenced by various independent parameters such as pier diameter, flow depth, approach mean velocity, critical velocity, Froude number, bed sediment, and geometric standard deviation of bed particle size. Gamma tests are performed to identify the best input parameter combinations to predict scour depth. In the present study, two separate models have been developed for clear-water scouring (CWS) and live-bed scouring (LBS). For different ranges of input parameters, the scour depth ratio is computed and error analysis is performed. Results indicate that the ANFIS model (R2CWS = 0.95, MAPECWS = 9.39% and R2LBS = 0.95, MAPELBS = 5.29%) is the most accurate predictive model in both scour conditions as compared to the GEP model and existing models of previous researchers. However, for the low value of pier diameter (b) to flow depth (y) ratio (

This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 (R2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

Ecological restoration can significantly improve ecosystem services and human well-being and provide a basis for regional coordinated development and ecological security. To guide restoration efforts, information on the potential benefits of restoration was required to efficiently target investments. Although the number of studies focusing on ecosystem restoration has increased in recent decades, priority areas that integrate ecological and economic benefits have yet to be identified. We developed and applied a prioritization approach to identify potential priority sites in Beijing. We used the historical environmental data on Beijing to identify areas of degradation and to assess the feasibility of restoration. Ecosystem service and quality degradation, low ecosystem quality, and soil erosion were integrated into one index to indicate the restoration importance. Potential restoration benefits were mapped using the monetary value of six ecosystem services. Based on the importance and benefits of restoration, three scenarios were developed to identify priority restoration areas. In Beijing, restoring 30% of the degraded area (1531 km2) in priority areas by 2050 could increase the annual ecological benefit by more than 5 billion yuan, or approximately 787 million USD, and could decrease the ecological degradation index by 50%. By integrating explicit spatial information on restoration importance and restoration benefits, this methodology provides a feasible way to identify restoration priority areas and assess restoration benefits. [ABSTRACT FROM AUTHOR]

Background: Multiple Endocrine Neoplasia type 1 is a rare genetic syndrome mainly caused by mutations of MEN1 gene and characterized by a combination of several endocrine and non-endocrine manifestations. The objective of this study was to describe cutaneous lesions and other non-endocrine manifestations of MEN1 in a cohort of patients with familial (F) and sporadic (S) MEN1, compare the prevalence of these manifestations between the two cohorts, and investigate the correlation with MEN1 mutation status. Methods: We collected phenotypic and genotypic data of 185 patients with F-MEN1 and S-MEN1 followed from 1997 to 2022. The associations between F-MEN1 and S-MEN1 or MEN1 mutation-positive and mutation-negative patients and non-endocrine manifestations were determined using chi-square or Fisher's exact tests or multivariate exact logistic regression analyses. Results: The prevalence of angiofibromas was significantly higher in F-MEN1 than in S-MEN1 in both the whole (p < 0.001) and index case (p = 0.003) cohorts. The prevalence of lipomas was also significantly higher in F-MEN1 than in SMEN1 (p = 0.009) and in MEN1 mutation-positive than in MEN1 mutationnegative (p = 0.01) index cases. In the whole cohort, the prevalence of lipomas was significantly higher in MEN1 mutation-positive compared to MEN1 mutation-negative patients (OR = 2.7, p = 0.02) and in F-MEN1 than in SMEN1 (p = 0.03), only after adjustment for age. No significant differences were observed for the other non-endocrine manifestations between the two cohorts. Hibernoma and collagenoma were each present in one patient (0.5%) and meningioma and neuroblastoma in 2.7% and 0.5%, respectively. Gastric leiomyoma was present in 1.1% of the patients and uterine leiomyoma in 14% of women. Thyroid cancer, breast cancer, lung cancer, basal cell carcinoma, melanoma, and colorectal cancer were present in 4.9%, 2.7%, 1.6%, 1.6%, 2.2%, and 0.5% of the whole series, respectively. Conclusions: We found a significantly higher prevalence of angiofibromas and lipomas in F-MEN1 compared with S-MEN1 and in MEN1 mutation-positive compared to MEN1 mutation-negative patients. In patients with one major endocrine manifestation of MEN1, the presence of cutaneous lesions might suggest the diagnosis of MEN1 and a possible indication for genetic screening. [ABSTRACT FROM AUTHOR]

Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network's performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation. [ABSTRACT FROM AUTHOR]

The Muskingham method uses two formulas to describe the translation of flow surges in a river bed. The continuity formula is the first formula, while the relationship between the reach's storage, inflow, and outflow is the second formula (the discharge storage formula); these formulas are applied to a portion of the river between two river cross sections. Several methods can be utilized to estimate the model's parameters. This section contrasts the conventional graphic approach with three numerical methods: Genetic algorithm, Exponential regression, and Classical fourth-order Runge--Kutta. This application's most noticeable plus point was the need to employ a few hydrological variables, such as intake, output, and duration. The location of the Euphrates entrance to the Iraqi territory in Husaybah city was chosen with its hydrological data during the period (1993-2017) to conduct this study. The goal function is established by accuracy criterion approaches (Sum of squares error and sum of squared deviations). Depending on the simulation findings, the suggested predictive flood routing ideawas highly acceptable with the prospect of adopting the Genetic Expression Programming model as a suitable and more accurate replacement to existing methods such as the Muskingum model and other numerical models, where this method gave results (R² = 0.9984, SSQ = 1.06, SSSD = 80.75), These results achieved a hydrograph that is largely identical to what was given by the hydrological method called Muskingham. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of jointed rock mass properties on the Minimum Required Caving Span (MRCS) in the block caving method using numeric and heuristic approaches. To do so, the effects of five parameters of jointed rock mass, namely joint set number, joint spacing, joint inclination angle, joint surface friction angle, and undercut depth on MRCS, were investigated using a discrete element code. For this purpose, many numerical models were generated with various rock mass parameters. Moreover, Gene Expression Programming and Artificial Neural Networks were employed to create a heuristic model for MRCS. The model parameters were subjected to sensitivity analysis. All model input parameters showed sensitivity to the model. There are several effective parameters on MRCS, but joint dip and joint set numbers are the most important and the smallest. [ABSTRACT FROM AUTHOR]

In this article, a new method for establishing creep predictive model using gene expression programming (GEP) is proposed. The three-point bending tests under constant load are carried out to determine time-dependent creep curves of fiber reinforced polymer materials with different lay-up styles, a modeling program is developed to predict creep behavior of composite materials. The creep of fiber is much smaller than that of resin matrix, various fiber layups play a role in constraining the deformation of resin, resulting in differences in creep performance of composites. The mathematical model satisfies the variation law that creep strain monotonically increases with time and tends to be stable. Based on 0 ~ 1000 h experimental data, the creep model is established by GEP, and then utilized to predict creep ranging from 1000 to 2000 h, the predicted values are in good agreement with experimental values. The fitting efficiency and prediction accuracy of GEP model are demonstrated by R2, RMSE, MAE and RRSE metrics. Moreover, taking R2 as a statistical metric, the validity of developed model is verified by comparison with Burgers model, Findley model and HKK model. Creep factor calculated by GEP model is lower than standard specified value, and the relative errors δ of creep deflection are very low, all within about 10%, indicating that GEP model can accurately predict the long-term creep performance of composites. [ABSTRACT FROM AUTHOR]

Abstract This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R 2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R 2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

The year 2022 will herald approximately 100,000 new cases of cutaneous melanoma (CM), and over 7000 deaths from CM. Over the past 40 years, CM incidence has increased nearly six-fold; however, annual mortality has remained relatively constant. These trends encapsulate the phenomenon of overdiagnosis. Increased recognition of indolent lesions that appear histologically malignant may be leading to a melanoma epidemic. Enhanced melanoma awareness, screening efforts, physician uncertainty, medical-legal pressures, and diagnostic scrutiny using tools like immunohistochemical staining, mole mapping, dermoscopy, confocal microscopy, and molecular diagnostics contribute to increased CM diagnosis. As a result, current melanoma staging and treatment guidelines are being challenged. Existing standards fail to accurately identify histologically benign lesions that are lethal or, conversely, histologically malignant lesions that are innocuous. Healthcare systems and, more importantly, patients suffer from this diagnostic ambiguity that leads to the over-treatment of innocuous melanomas and under-treatment of aggressive melanomas. As dermatology continues to experience a shift towards earlier diagnosis of melanoma, management strategies must adapt. Herein, we review factors that may contribute to the increased incidence of melanoma, emphasize deficiencies in current staging systems, and provide insights into the future of melanoma management via precision medicine.

In the analysis of vapor compression refrigeration systems, simple and effective mathematical formulas are required to determine the thermodynamic properties of refrigerants. This study aims to determine the thermodynamic properties such as enthalpy, entropy, and specific volume of environmentally friendly new-generation refrigerants (R515B and R450A) with low global warming potential using gene expression programming (GEP). The thermodynamic properties calculated using the formulations obtained from the GEP model and the actual thermodynamic properties obtained from the REFROP software were compared. Performance evaluation criteria such as R2, root mean square error, and mean absolute percent error are in the range of 0.86 to 0.999, 0.0000285–6.489, and 0.0009–0.35, respectively, and these values are acceptable. This study offers simple and efficient formulations to calculate the thermodynamic properties of new-generation refrigerants without the need for any software. So, the simulation of cooling and heat pump systems will be greatly facilitated. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Some melanocytic neoplasms suspicious for melanoma require additional workup to arrive at a final diagnosis. Within the last eight years, gene expression profiling (GEP) has become an important ancillary tool to aid in the diagnosis of melanocytic neoplasms with uncertain malignant potential. As the usage of two commercially available tests (23-GEP and 35-GEP) evolves, it is important to answer key questions about optimal utilization and their impact on patient care. METHODS: Recent and relevant articles answering the following questions were included in the review. First, how do dermatopathologists synthesize the available literature, the latest guidelines, and their clinical experience to determine which cases would be most likely to benefit from GEP testing? Second, how best can a dermatologist convey to their dermatopathologist that the use of GEP in the diagnostic process could provide a more clearly defined result and thereby help empower the dermatologist to provide higher- quality patient care when making specific patient management decisions for otherwise pathologically ambiguous lesions? RESULTS: When interpreted in the context of the clinical, pathologic, and laboratory information, GEP results can facilitate the rendering of timely, accurate, and definitive diagnoses for melanocytic lesions with otherwise uncertain malignant potential to inform personalized treatment and management plans. LIMITATIONS: This was a narrative review focused on clinical use of GEP compared to other ancillary diagnostic tests performed postbiopsy. CONCLUSION: Open communication between dermatopathologists and dermatologists, especially regarding GEP testing, can be a vital component to achieve appropriate clinicopathologic correlation for otherwise ambiguous melanocytic lesions. [ABSTRACT FROM AUTHOR]

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture). [ABSTRACT FROM AUTHOR]