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Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended. [ABSTRACT FROM AUTHOR]

Spontaneous reporting of adverse drug reactions (ADRs) is the cornerstone of pharmacovigilance. However, major underreporting exists. The main objective of this study was to assess the use of a pharmacovigilance simplified reporting tool (PSRT) by general practitioners (GPs) and, secondarily, to describe the quality of ADR reports during this period. The PSRT was proposed on June 1st, 2015, for the 1290 GPs in the Western Normandy Region. The number and quality of ADRs reported monthly by GPs were prospectively collected from June 1st, 2015, to May 31st, 2020 (Period 2), and compared to those reported during a control period (June 1st, 2010, to May 31st, 2015, Period 1). During all the periods, 920 reports were made by 307 GPs (198 reports in Period 1 and 722 reports in Period 2), with 477 reports (51.8%) using the PSRT. During Period 2, the monthly number of reports was multiplied by 3.5 (p < 0.0001), and the number of GPs was 1.4 compared to that in Period 1 (p = 0.01). Our PSRT showed effectiveness in quantitative and qualitative terms. It must now go further and be integrated into GP software to facilitate ADR reporting nationwide. [ABSTRACT FROM AUTHOR]

During gastrulation and neurulation, the chordamesoderm and overlying neuroectoderm of vertebrate embryos converge under the control of a specific genetic programme to the dorsal midline, simultaneously extending along it. However, whether mechanical tensions resulting from these morphogenetic movements play a role in long-range feedback signaling that in turn regulates gene expression in the chordamesoderm and neuroectoderm is unclear. In the present work, by using a model of artificially stretched explants of Xenopus midgastrula embryos and full-transcriptome sequencing, we identified genes with altered expression in response to external mechanical stretching. Importantly, mechanically activated genes appeared to be expressed during normal development in the trunk, i.e., in the stretched region only. By contrast, genes inhibited by mechanical stretching were normally expressed in the anterior neuroectoderm, where mechanical stress is low. These results indicate that mechanical tensions may play the role of a long-range signaling factor that regulates patterning of the embryo, serving as a link coupling morphogenesis and cell differentiation. [ABSTRACT FROM AUTHOR]

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS's unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction. [ABSTRACT FROM AUTHOR]

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts. [ABSTRACT FROM AUTHOR]

Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles. [ABSTRACT FROM AUTHOR]

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency ( R 2 = 0.85, RMSE = 2.2 m , pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R 2 , RMSE, and pRMSE of 0.64, 3.38 m , and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models. [ABSTRACT FROM AUTHOR]

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei's evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia's evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method. [ABSTRACT FROM AUTHOR]

• Probabilistic analysis of soil slope of heavy-haul railway embankment. • Probabilistic analysis of a 12.293 m high soil slope against bearing capacity failure. • Assessment of failure probability of a soil slope in seismic and non-seismic conditions. • Assessment of failure probability for different COVs of soil parameters. This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently. [ABSTRACT FROM AUTHOR]

In this study, sage, chamomile, and tarragon leaves, which are used as spices and consumed as beverages in daily life, were considered as different biosorbents that can be used in water purification by biosorption. At the same time, the effects of the parameters of initial dye concentration (10-200 mg/L), temperature (20-50 °C) and contact time (0-120 min) on biosorption capacity were investigated. The biosorption processes were found to follow Freundlich isotherm and pseudo-second order (PSO) reaction kinetics. In the study, the process was also modeled using multi-tree evolutionary computation based automatic programming (AP) methods. The methods used initial dye concentration, temperature, and contact time as variables. According to the simulation results, these methods obtained nonlinear mathematical models of the processes with R² values as high as 0.99 for each biosorbent. By providing the most accurate models to accurately predict biosorption capacity, this study will make a significant contribution to the field of water treatment using experimental and AP methods. [ABSTRACT FROM AUTHOR]

Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call the traveling salesman problem with bike and robot (TSPBR), where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting. [ABSTRACT FROM AUTHOR]

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET Regex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential. [ABSTRACT FROM AUTHOR]

Conventional breeding involves considerable genetic recombination. To maximize breeding efforts, it is necessary to increase seed yield. This study proposes to improve seed yield by using tetraploid bananas as part of a genetic improvement programme for cooking varieties. To this end, the female fertility (seeding rate and embryo sac maturity) of tetraploid banana plants was characterised. The pollen potential of tetraploids was then assessed at anthesis and at the end of meiosis, as well as during crosses with seminiferous diploids, leading to a phenotypic characterisation of the value of an offspring. This study highlighted the low seed production rate of tetraploid genitors. At best, 7% of seeds are produced per fruit, but 57% of the ovules per fruit have mature embryo sacs. The use of tetraploids as male parents, free from any source of sterility associated with the domestication process, did not generally improve the seed rate, except in the case of FHIA 21 (2930 seeds). Its pollen potential was used in a cross with Banksii 0623 (♀). This resulted in progeny with interesting vigour and phenotypic characteristics. This latter combination has the potential to transfer its "cooking" genetic structure. [ABSTRACT FROM AUTHOR]

Mocking is an essential unit testing technique for isolating the class under test from its dependencies. Developers often leverage mocking frameworks to develop stub code that specifies the behaviors of mock objects. However, developing and maintaining stub code is labor-intensive and error-prone. In this article, we present StubCoder to automatically generate and repair stub code for regression testing. StubCoder implements a novel evolutionary algorithm that synthesizes test-passing stub code guided by the runtime behavior of test cases. We evaluated our proposed approach on 59 test cases from 13 open source projects. Our evaluation results show that StubCoder can effectively generate stub code for incomplete test cases without stub code and repair obsolete test cases with broken stub code. [ABSTRACT FROM AUTHOR]

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingeniería (0121-750X) is the property of Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively. [ABSTRACT FROM AUTHOR]

This study presents a generic methodology for modelling and optimizing a reactor with complex and poorly understood kinetics. Here, a photoreactor is considered which performs photodegradation of sodium oxalate salt in spent Bayer liquor. Multiple data‐driven modelling methods, including artificial neural networks (ANN), genetic programming (GP), hybrid genetic programming‐grey wolf optimization (GP‐GWO), and multi‐gene genetic programming (MGGP), were used to model the reactor's performance based on experimental data. The input parameters considered for modelling were initial solution pH, power of the lamp, total organic carbon, and catalyst loading. The models were evaluated based on their predictability, explainability, complexity, and adherence to the reactor's phenomenology. The MGGP model was found to be the most effective and was used to generate surface plots showing the parity between experimental results and model predictions. Additionally, the MGGP model was optimized using GWO to determine the process conditions that maximize the reaction rate. [ABSTRACT FROM AUTHOR]

Several AutoML tools aim to facilitate the usability of machine learning algorithms, automatically recommending algorithms using techniques such as meta-learning, grid search, and genetic programming. However, the preprocessing step is usually not well handled by those tools. Thus, in this work, we present a systematic review of preprocessing algorithms selection with meta-learning, aiming to find the state of the art in this field. To perform this task, we acquired 450 references, of which we selected 37 to be evaluated and analyzed according to a set of questions earlier defined. Thus, we managed to identify information such as what was published on the subject; the topics more often presented in those works; the most frequently recommended preprocessing algorithms; the most used features selected to extract information for the meta-learning; the machine learning algorithms employed as meta-learners and base-learners in those works; and the performance metrics that are chosen as the target of the applications. [ABSTRACT FROM AUTHOR]

Background: Cardiac Myxoma is a primary tumor of heart. Its origins, rarity of the occurrence of primary cardiac tumors and how it may be related to limited cardiac regenerative potential, are not yet entirely known. This study investigates the key cardiac genes/ transcription factors (TFs) and signaling pathways to understand these important questions. Methods: Databases including PubMed, MEDLINE, and Google Scholar were searched for published articles without any date restrictions, involving cardiac myxoma, cardiac genes/TFs/signaling pathways and their roles in cardiogenesis, proliferation, differentiation, key interactions and tumorigenesis, with focus on cardiomyocytes. Results: The cardiac genetic landscape is governed by a very tight control between proliferation and differentiation-related genes/TFs/pathways. Cardiac myxoma originates possibly as a consequence of dysregulations in the gene expression of differentiation regulators including Tbx5, GATA4, HAND1/2, MYOCD, HOPX, BMPs. Such dysregulations switch the expression of cardiomyocytes into progenitor-like state in cardiac myxoma development by dysregulating Isl1, Baf60 complex, Wnt, FGF, Notch, Mef2c and others. The Nkx2–5 and MSX2 contribute predominantly to both proliferation and differentiation of Cardiac Progenitor Cells (CPCs), may possibly serve roles based on the microenvironment and the direction of cell circuitry in cardiac tumorigenesis. The Nkx2–5 in cardiac myxoma may serve to limit progression of tumorigenesis as it has massive control over the proliferation of CPCs. The cardiac cell type-specific genetic programming plays governing role in controlling the tumorigenesis and regenerative potential. Conclusion: The cardiomyocytes have very limited proliferative and regenerative potential. They survive for long periods of time and tightly maintain the gene expression of differentiation genes such as Tbx5, GATA4 that interact with tumor suppressors (TS) and exert TS like effect. The total effect such gene expression exerts is responsible for the rare occurrence and benign nature of primary cardiac tumors. This prevents the progression of tumorigenesis. But this also limits the regenerative and proliferative potential of cardiomyocytes. Cardiac Myxoma develops as a consequence of dysregulations in these key genes which revert the cells towards progenitor-like state, hallmark of CM. The CM development in carney complex also signifies the role of TS in cardiac cells. [ABSTRACT FROM AUTHOR]

Implementing intelligent irrigation and adjusting the irrigation system is essential in today's agricultural system to control the amount of water required for the plant. This study focuses on data obtained from sensors measuring (soil temperature and humidity, temperature and humidity of ambient and light) and image processing of plant leaves. To analyze the data, two models were implemented including a regression model in SPSS software and another model by genetic programming in MATLAB 2018 software. The optimal model was a combined model of sensors and images in genetic programming with higher R2 and lower standard error of 0.88 and 0.03, respectively. This model was superior to the regression model which had an R2 and standard error of 0.86 and 0.21, respectively, so this optimal model was selected to adjust the microcontroller for the intelligent irrigation system. The following year by replanting the crop, the intelligent irrigation system was presented as the superior system with 11% water saving compared to the previous year (irrigation by the user). Also, no changes were observed in the yield and color indicators of the plant at the level of 5%, which indicates the superiority of the intelligent irrigation system and its high accuracy of this system. [ABSTRACT FROM AUTHOR]

The existing gap in the ability to quantify the impacts of resistive losses on the performance of anion‐exchange membrane fuel cells (AEMFCs) during the lifetime of their operation is a serious concern for the technology. In this paper, we analyzed the ohmic region of an operating AEMFC fed with pure oxygen followed by CO2‐free air at various operating currents, using a combination of electrochemical impedance spectroscopy (EIS) and a novel technique called impedance spectroscopy genetic programming (ISGP). Presented here for the first time in this work, we isolated and quantified the individual effective resistance (Reff) values occurring in the AEMFC and their influence on performance as operating conditions change. We believe that this first work is vital to help distinguish the influence of the individual catalytic and mass‐transfer processes in this technology thereby providing valuable data to the AEMFC community, with potentially wider applicability to other electrochemical devices where individual physical processes occur simultaneously and need to be sequestered for deeper understanding. [ABSTRACT FROM AUTHOR]

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP's functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modeling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behavior or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. Aims: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. Methods: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t‐test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). Results: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI‐based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74–0.85) versus 0.61 (95% CI: 0.54–0.68), respectively (p <.001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. Conclusion: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models. [ABSTRACT FROM AUTHOR]

The integrity and security of medical data have become a big challenge for healthcare services applications. Images of hidden text represent steganography forms in which the image is exploited as an object to cover information and data. So, the data masking ability and image quality of the cover object are significant elements in image masking. In this study, the patient's personal information and message generated by the doctor's comment are stored in the images. Image pixels and message bits are exchanged sequentially. The best cluster is randomly selected using the genetic algorithm (GA) and standard deviation (STD) methods. The method, depending on optimizing and taking benefits of the similarities between pixels, has been proposed. High image quality can be achieved by using stego-image and increasing the data amount to be hidden. Analysis metrics of visual quality such as peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and bit error rate (BER) are adopted to assess the performance of the method proposed. The suggested and proposed model has proven its capability to mask the secrete data of patients into a cover image transmitted with high ability, imperceptibility, and minimal future degradation. [ABSTRACT FROM AUTHOR]

The Travelling Salesman Problem (TSP) is a well-known optimisation problem that has been widely studied over the last century. As a result, a variety of exact and approximate algorithms have been proposed in the literature. When it comes to solving large instances in real-time, greedy algorithms guided by priority rules represent the most common approach, being the nearest neighbour (NN) heuristic one of the most popular rules. NN is quite general but it is too simple and so it may not be the best choice in some cases. Alternatively, we may design more sophisticated heuristics considering the particular features of families of instances. To do that, we have to consider problem attributes other than the proximity of the next city to build priority rules. However, this process may not be easy for humans and so it is often addressed by some learning procedure. In this regard, hyper-heuristics as Genetic Programming (GP) stands as one of the most popular approaches. Furthermore, a single heuristic, even being good in average, may not be good for a number of instances of a given set. For this reason, the use of ensembles of heuristics is often a good alternative, which raises the problem of building ensembles from a given set of heuristic rules. In this paper, we study the application of two kinds of ensembles to the TSP. Given a set of TSP instances having similar characteristics, we firstly exploit a GP to build a set of heuristics involving a number of problem attributes, and then we build ensembles combining these heuristics by means of a Genetic Algorithm (GA). The experimental study provided valuable insights into the construction and utilisation of single rules and ensembles. It clearly demonstrated that the performance of ensembles justifies the time invested when compared to using individual heuristics. [ABSTRACT FROM AUTHOR]

This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/non-insolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction models. [ABSTRACT FROM AUTHOR]

Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimization techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40% on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance. [ABSTRACT FROM AUTHOR]

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a micro-RNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications. [ABSTRACT FROM AUTHOR]

This study investigates the impact of shear reinforcement amount and arrangement on the shear capacity of shallow/wide RC beams. Seven specimens of shallow/wide beams with different ultimate shear reinforcement stress (µ.Fys), longitudinal spacing to depth ratio (S/d), and transversal spacing to depth ratio (S'/d) were tested under a monotonic three-point bending test. All the specimens were designed to fail at shearing. The results showed that the shear reinforcement was fully functioning until it yielded; also, the amount of shear reinforcement had the major impact on the shear capacity; in addition, the transverse spacing had more influence on the shear capacity than the longitudinal spacing. The measured shear capacities were compared to six design codes, in which the results ranged from 95% to 110%, with the Japanese code (JSCE) being the closest to the experimental results. Two AI-based predicting equations, "Genetic Programming" (GP) and "Evolutionary Polynomial Regression" (EPR), were also compared to the experimental, with accuracies of 78% and 86% of the measured capacities, respectively. Initial stiffness, final stiffness, dissipated energy, and ductility were all discussed for the seven specimens, with ultimate shear reinforcement stress being the most impactful on the total shear capacity of the wide beams. [ABSTRACT FROM AUTHOR]

In January 2022 final electromagnetic stirrers were installed to improve billets' macrostructure and to reduce defects in ŠTORE STEEL, Slovenia. From January to September 2022 the material from 1121 batches, out of 2284 batches produced, was examined using ultrasonic testing. Among these 1121 batches, 270 batches were detected with defects larger than 2.5 mm. Based on various metallurgical reports made from samples taken after an ultrasonic inspection, the root causes were found to be shrinkage porosity and segregations. Based on the gathered parameters from this period (chemical composition, casting parameters, cooling or heating parameters of billets), the occurrence of inner defects was predicted using logistic regression and genetic programming. The genetic programming proved to be better than logistic regression. Based on modeling results, optimization of the final electromagnetic stirrers position for 51CrV4 spring steel was conducted. Consequently, out of 41 examined batches, only 2 were detected with defects larger than 2.5 mm. [ABSTRACT FROM AUTHOR]

Emerging Proliferated Low Earth Orbit (P-LEO) constellations may be susceptible to attacks launched by malevolent actors capable of compromising orbiting satellites. We introduce and investigate an adversarial ground station transit time game as a proxy to study the ability to rapidly detect and respond to satellite attacks. Investigation of this problem allows us to study more complex real-world dynamics involving threats to satellite systems. Unfortunately, the problem proves to be daunting to solve due to the high-dimensionality of the solution space and the difficulty of predicting action consequences in dynamic adversarial settings. For this reason, an effective method to identify successful strategies as solutions to the problem is necessary. In this work, an artificial intelligence approach called competitive coevolution is employed to solve scenarios featuring an attacker evolving strategic locations to degrade the performance of a constellation, while a defender evolves intelligent strategies to counter the attacker’s action. The proposed solution outperforms both minimal and complex strategies, while showing versatility and robustness over a variety of scenarios. [ABSTRACT FROM AUTHOR]

The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming Symbolic Regression (GPSR) method on a publicly available dataset. The primary aim is to assess if the resulting Mathematical Equations (MEs) from GPSR can accurately estimate calculated atomic coordinates obtained through DFT. Given the numerous hyperparameters in GPSR, a Random Hyperparameter Value Search (RHVS) method is devised to pinpoint the optimal combination of hyperparameter values, maximizing estimation accuracy. Two distinct approaches are considered. The first involves applying GPSR to estimate calculated coordinates ( u c , v c , w c ) using all input variables (initial atomic coordinates u, v, w, and integers n, m specifying the chiral vector). The second approach applies GPSR to estimate each calculated atomic coordinate using integers n and m alongside the corresponding initial atomic coordinates. This results in the creation of six different dataset variations. The GPSR algorithm undergoes training via a 5-fold cross-validation process. The evaluation metrics include the coefficient of determination ( R 2 ), mean absolute error ( M A E ), root mean squared error ( R M S E ), and the depth and length of generated MEs. The findings from this approach demonstrate that GPSR can effectively estimate CNT atomic coordinates with high accuracy, as indicated by an impressive R 2 ≈ 1.0 . This study not only contributes to the advancement of accurate estimation techniques for atomic coordinates but also introduces a systematic approach for optimizing hyperparameters in GPSR, showcasing its potential for broader applications in materials science and computational chemistry. [ABSTRACT FROM AUTHOR]

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection. [ABSTRACT FROM AUTHOR]

In the present study, the density and viscosity of the CO2-loaded and –unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.%), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.%), and super activated carbon (SAC) (0.1 wt.%) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.%) +Sulfolane (SFL), 30 wt.%) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15–315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and γ-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 % in high CO2 loading, ∼0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results.

Artificial intelligence (AI) and machine learning (ML) methods have gained notable recognition for their innovative problem-solving approaches, which notably do not require understanding the problem’s physical underpinnings. AI applications in medicine herald a new era of digital health, assisting physicians in delivering optimal patient care. The experience and knowledge of physicians are undeniably crucial in diagnosing diseases and treating patients. In this context, AI models facilitate the rapid learning and analysis of large datasets. Consequently, with the growing volume of data collection and the refinement of AI models, AI technologies can assist physicians and health policy-makers make more precise evidence-based clinical decisions. In cancer research, AI methodologies are extensively utilized for prognostic predictions and risk assessments. Specifically, accurately categorizing cancer patients into risk groups and forecasting individual prognoses are vital for therapeutic decision-making. Like other AI techniques, genetic programming (GP) has been employed for prognostic predictions and the classification of cancer patients.Additionally, AI-assisted classification of cancer types may provide more precise criteria for distinguishing malignant and benign lesions. Preliminary studies in breast cancer utilizing GP have yielded significant diagnostic criteria for the classification of malignant lesions in screening mammography. Early cancer diagnosis and identifying individuals at risk for specialized screening programs are undoubtedly life-saving advancements in cancer research. In light of this, further investigations utilizing GP are recommended.

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%.

For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions.

Geotechnical engineering relies heavily on predicting soil strength to ensure safe and efficient construction projects. This paper presents a study on the accurate prediction of soil strength properties, focusing on hydrated-lime activated rice husk ash (HARHA) treated soil. To achieve precise predictions, the researchers employed two grey-box machine learning models—classification and regression trees (CART) and genetic programming (GP). These models introduce innovative equations and trees that readers can readily apply to new databases. The models were trained and tested using a comprehensive laboratory database consisting of seven input parameters and three output variables. The results indicate that both the proposed CART trees and GP equations exhibited excellent predictive capabilities across all three output variables—California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (Rvalue) (according to the in-situ cone penetrometer test). The GP proposed equations, in particular, demonstrated a superior performance in predicting the UCS and Rvalue parameters, while remaining comparable to CART in predicting the CBR. This research highlights the potential of integrating grey-box machine learning models with geotechnical engineering, providing valuable insights to enhance decision-making processes and safety measures in future infrastructural development projects.

Machine learning (ML) is commonly used to develop survival-predictive radiomic models in non-small cell lung cancer (NSCLC) patients, which helps assist treatment decision making. Radiomic features derived from computer tomography (CT) lung images aim to capture quantitative tumor characteristics. However, these features are determined by humans, which poses a risk of including irrelevant or redundant variables, thus reducing the model’s generalization. To address this issue, we propose using genetic programming (GP) to automatically construct new features with higher discriminant power than the original radiomic features. To achieve this goal, we introduce a fitness function that measures the classification performance ratio of output to input. The constructed features are then input for various classifiers to predict the two-year survival of NSCLC patients from two public CT datasets. Our approach is compared against two popular feature selection methods in radiomics to choose relevant radiomic features, and two GP-based feature construction methods whose fitness functions are based on measuring the constructed features’ quality. The experimental results show that survival prediction models trained on GP-based constructed features outperform feature selection methods. Also, maximizing the classification performance gain output-to-input ratio produces features with higher discriminative power than only maximizing the classification accuracy from constructed features. Furthermore, a survival analysis demonstrated statistically significant differences between survival and non-survival groups in the Kaplan–Meier curves. Therefore, the proposed approach can be used as a complementary method for oncologists in determining the clinical management of NSCLC patients.

Artificial Intelligence (AI) methods, including Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs), Support Vector Machines (SVMs), Deep Learning (DL), Genetic Programming (GP) and Hybrid Algorithms, have proven to be important tools for accurate groundwater level (GWL) modelling. Through an analysis of the results obtained in numerous articles published in high-impact journals during 2001–2023, this comprehensive review examines each method’s capabilities, their combinations, and critical considerations about selecting appropriate input parameters, using optimisation algorithms, and considering the natural physical conditions of the territories under investigation to improve the models’ accuracy. For example, ANN takes advantage of its ability to recognise complex patterns and non-linear relationships between input and output variables. In addition, ANFIS shows potential in processing diverse environmental data and offers higher accuracy than alternative methods such as ANN, SVM, and GP. SVM excels at efficiently modelling complex relationships and heterogeneous data. Meanwhile, DL methods, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), are crucial in improving prediction accuracy at different temporal and spatial scales. GP methods have also shown promise in modelling complex and nonlinear relationships in groundwater data, providing more accurate and reliable predictions when combined with optimisation techniques and uncertainty analysis. Therefore, integrating these methods and optimisation techniques (Hybrid Algorithms), tailored to specific hydrological and hydrogeological conditions, can significantly increase the predictive capability of GWL models and improve the planning and management of water resources. These findings emphasise the importance of thoroughly understanding (a priori) the functionalities and capabilities of each potentially beneficial AI-based methodology, along with the knowledge of the physical characteristics of the territory under investigation, to optimise GWL predictive models.

Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering.

This study employs friction stir processing to create a surface alloy using Al1050 aluminum as the base material, with Cu powder applied to enhance surface properties. Various parameters, including tool rotation speed, feed rate, and the number of passes, are investigated for their effects on the microstructure and mechanical properties of the resulting surface alloy. The evaluation methods include tensile testing, microhardness measurements, and metallographic examinations. The initial friction stir alloying pass produced a non-uniform stir zone, which was subsequently homogenized with additional passes. Through the plasticization of Al1050, initial agglomerates of copper particles were compacted into larger ones and saturated with aluminum. The alloyed samples exhibited up to an 80% increase in the strength of the base metal. This significant enhancement is attributed to the Cu content and grain size refinement post-alloying. Additionally, machine learning techniques, specifically Genetic Programming, were used to model the relationship between processing parameters and the mechanical properties of the alloy, providing predictive insights for optimizing the surface alloying process.

ŠTORE STEEL Ltd. is one of the three steel plants in Slovenia. Continuous cast 180 mm × 180 mm billets can undergo cooling to room temperature using a turnover cooling bed. They can also be cooled down under hoods or heat treated to reduce residual stresses. Additional operations of heat treatment from 36 h up to 72 h and cooling of the billets for 24 h, with limited capacities (with only two heat treatment furnaces and only six hoods), drastically influence productivity. Accordingly, the casting must be carefully planned (i.e., the main thing is casting in sequences), while the internal quality of the billets (i.e., the occurrence of inner defects) may be compromised. Also, the stock of billets can increase dramatically. As a result, it was necessary to consider the abandoning of cooling under hoods and heat treatment of billets. Based on the collected scrap data after ultrasonic examination of rolled bars, linear regression and genetic programming were used for prediction of the occurrence of inner defects. Based on modeling results, cooling under hoods and heat treatment of billets were abandoned at the casting of several steel grades. Accordingly, the casting sequences increased, and the stock of billets decreased drastically while the internal quality of the rolled bars remained the same.

This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.