Search Results (46)

Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field conditions on clay soil. Specific fuel consumption (SFC), fuel consumption per unit area (FCPA), and overall energy efficiency (OEE) were evaluated at four forward speeds (0.6, 0.95, 1.2 and 1.4 m·s⁻¹) and four tillage depths (15, 19.5, 23 and 26.5 cm). SFC ranged from 0.519 to 1.237 L·kW⁻¹·h⁻¹, while OEE varied between 7.918 and 18.854%. Higher forward speeds significantly reduced fuel consumption and improved energy efficiency, whereas deeper tillage increased fuel use and reduced efficiency. Optimal operation occurred at speeds of 1.2–1.4 m·s⁻¹ and shallow to medium depths. Five machine learning algorithms: Polynomial Regression (PL), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR), were applied to model fuel efficiency parameters. RFR achieved the highest accuracy for predicting SFC, while PL performed best for FCPA and OEE, with the mean absolute percentage error (MAPE) below 2%. Models such as PL and RFR excel in data structures dominated by nonlinear relationships. These results highlight the potential of machine learning to guide data-driven decisions for fuel and energy optimization in tillage, promoting more sustainable mechanization strategies and resource-efficient agricultural production. Full article

The ageing of polymer-modified bitumen (PMB) significantly affects its rheological performance and service life in asphalt pavements. In this study, experimental data PMB 25/55–60 aged under a modified Thin Film Oven Test (TFOT) were restructured into a tidy dataset and analyzed using machine learning techniques. The input variables included temperature, angular frequency, and ageing condition, while the output variable was the complex shear modulus (G*). Two state-of-the-art regression models, Random Forest (RF) and Gradient Boosting Regressor (GBR), were trained and evaluated. Performance assessment revealed that GBR outperformed RF, achieving R² = 0.992, MAE = 1.07 × 10⁶ Pa, and RMSE = 2.04 × 10⁶ Pa, compared to RF with R² = 0.962. Condition-wise analysis further confirmed the robustness of GBR across different TFOT scenarios. Feature importance analysis identified temperature as the dominant factor influencing rheological behavior, followed by frequency and ageing condition. These findings demonstrate the potential of gradient boosting approaches for accurately predicting the rheological properties of aged PMB, providing a reliable tool for performance evaluation and supporting the development of predictive frameworks for pavement materials. Full article

Soft skills remain fundamental for employability and sustainable human development in an increasingly technology-driven society. These interpersonal and cognitive competencies—such as communication, adaptability, and critical thinking—represent uniquely human capabilities that current Artificial Intelligence (AI) systems cannot replicate. However, assessing and developing these skills consistently remains a challenge due to the lack of standardized evaluation frameworks. This study explores the potential of commercial video games as engaging environments for soft skills enhancement and introduces an AI-based assessment methodology to quantify such improvement. Using player data collected from the Steam platform, we designed and validated an AI model based on Gradient Boosting Regressor (GBR) to estimate participants’ soft skill progression. The model achieved high predictive performance (R² ≈ 0.9; MAE/RMSE ≈ 1), demonstrating strong alignment between gameplay behavior and soft skill improvement. The results highlight that video game-based data analysis can provide a reliable, non-intrusive alternative to traditional testing methods, reducing test-related anxiety while maintaining assessment validity. This approach supports the integration of video games into educational and professional training frameworks as a scalable and data-driven tool for soft skills development. Full article

Graphite nodularity is a key indicator for evaluating the microstructure quality of ductile iron and plays a crucial role in ensuring product quality and enhancing manufacturing efficiency. Existing research often only focuses on a single type of feature and fails to utilize multi-source information in a coordinated manner. Single-feature methods are difficult to comprehensively capture microstructures, which limits the accuracy and robustness of the model. This study proposes a hybrid estimation model for the graphite nodularity of ductile cast iron based on multi-source feature extraction. A comprehensive feature engineering pipeline was established, incorporating geometric, color, and texture features extracted via Hue-Saturation-Value color space (HSV) histograms, gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and multi-scale Gabor filters. Dimensionality reduction was performed using Principal Component Analysis (PCA) to mitigate redundancy. An improved watershed algorithm combined with intelligent filtering was used for accurate particle segmentation. Several machine learning algorithms, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), are applied to estimate graphite nodularity based on geometric features (GFs) and feature extraction. Experimental results demonstrate that the CatBoost model trained on fused features achieves high estimation accuracy and stability for geometric parameters, with R-squared (R²) exceeding 0.98. Furthermore, introducing geometric features into the fusion set enhances model generalization and suppresses overfitting. This framework offers an efficient and robust approach for intelligent analysis of metallographic images and provides valuable support for automated quality assessment in casting production. Full article

This study presents a comprehensive Quantitative Microbial Risk Assessment (QMRA) for Escherichia coli in northeastern Greece’s riverine and deltaic aquatic systems, evaluating potential human health risks from recreational water exposure. The analysis integrates seasonal microbiological monitoring data—E. coli, total coliforms, enterococci, Salmonella spp., Clostridium perfringens (spores and vegetative forms), and physicochemical parameters (e.g., pH, temperature, BOD₅)—across multiple sites. A beta-Poisson dose–response model within a Monte Carlo simulation framework (10,000 iterations) was applied to five exposure scenarios, simulating varying ingestion volumes for different population groups. Median annual infection risks ranged from negligible to high, with several locations (e.g., Mandra River, Konsynthos South, and Delta Evros) surpassing the World Health Organization (WHO)’s benchmark of 10⁻⁴ infections per person per year. A Gradient Boosting Regressor (GBR) model was developed to enhance predictive capacity, demonstrating superior accuracy metrics. Permutation Importance analysis identified enterococci, total coliforms, BOD₅, temperature, pH, and seasons as critical predictors of E. coli concentrations. Additionally, sensitivity analysis highlighted the dominant role of ingestion volume and E. coli levels across all scenarios and sites. These findings support the integration of ML-based tools and probabilistic modelling in water quality risk governance, enabling proactive public health strategies in vulnerable or high-use recreational zones. Full article

Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than the time to reach the design flood peak. Therefore, estimating the temporal variation in scour depth is necessary. This study evaluates multiple machine learning (ML) models to identify the most accurate method for predicting scour depth (Ds) over time using experimental data. The dataset of 3275 records, including flow depth (Y), abutment length (L), channel width (B), velocity (V), time (t), sediment size (d₅₀), and Ds, was used to train and test Linear Regression (LR), Random Forest Regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, LightGBM, and KNN models. Results demonstrated the superior performance of AI-based models over conventional regression. The RFR model achieved the highest accuracy (R² = 0.9956, Accuracy = 99.73%), followed by KNN and GBR. In contrast, the conventional LR model performed poorly (R² = 0.4547, Accuracy = 57.39%). This study confirms the significant potential of ML, particularly ensemble methods, to provide highly reliable scour predictions, offering a robust tool for enhancing bridge design and safety. Full article

This study explores the integration of copper oxide (Cu₂O) into bitumen and leverages Artificial Intelligence (AI) to evaluate and optimize the binder’s performance across multiple scales. Comprehensive laboratory tests, including conventional binder properties, rheological analysis, aging simulations, low-temperature cracking, and moisture susceptibility, were conducted on base and Cu₂O modified asphalt binders. The results were used to train predictive models using gradient boosting regressors for each performance category. Optimization identified ideal Cu₂O ratios for different engineering goals, offering practical recommendations. Based on this integrated cost-performance analysis, a Cu₂O concentration of 2.3% was recommended as the most efficient trade-off point. AI modeling using Gradient Boosting Regressor (GBR) achieved high predictive performance, with R² values reaching 0.98 for BBR prediction and 0.78 for rheology, and mean absolute error (MAE) values as low as 4.21. This demonstrates the model’s robustness in capturing complex nonlinear binder behaviors. Full article

This study explored the use of Borassus fruit fiber as reinforcement for earthen matrices (BFRC). The experimental results of the testing carried out on the structural properties were used to generate a primary dataset for training and testing machine learning (ML) models. Linear regression (LR), Decision tree regressor (DTR), and gradient boosting regression (GBR) were used to build an ensemble learning (EL) model during the prediction of the hygroscopic properties, Young’s modulus, and compressive strength of the BFRC. Fiber content, activation concentration, curing days, dry weight, saturated weight, mass, flexural vibration, longitudinal vibration, correction factor, maximum load, and cross-sectional area were the various inputs considered in the structural properties prediction. The performance of both EL and single models (SMs) was appraised via three performance metrics—mean square error (MSE), root mean square (RMSE), and the coefficient of determination (R²)—to comparatively ascertain the model’s efficiency. Results showed that all models exhibited high accuracy in predicting Young’s modulus and compressive strength. Ensemble learning outperformed single models in predicting these properties, with MSE, RMSE, and R² of 0.01 MPa, 0.1 MPa, and 99% and 3,923,262.5 MPa, 1980.7 Pa, and 99% for compressive strength and Young’s modulus, respectively. However, for hygroscopic behavior, linear regression (LR) demonstrated superior performance compared to other models, with MSE, RMSE, and R² values of 0.13%, 0.36%, and 99%. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Predicting wave run-up on seawalls is essential for assessing coastal flood risk and guiding resilient design. In this study, we combine physical model experiments with a hybrid data driven method to forecast wave run-up distance. Laboratory tests generated a nonlinear data set spanning a wide range of wave amplitudes, wavelengths, Froude numbers. To capture the underlying physical regimes, the records were first classified using a Gaussian Mixture Model (GMM), which automatically grouped waves of similar hydrodynamic character. Within each cluster a Gradient Boosting Regressor (GBR) was then trained, allowing the model to learn tailored input–output relationships instead of forcing a single global fit. Results demonstrate that the GMM-GBR combined model achieves a coefficient of determination $R^2$ greater than 0.91, outperforming a conventional, non-clustered GBR model. This approach offers a reliable tool for predicting seawall performance under varying wave conditions, contributing to better coastal management and resilience strategies. Full article

This study focuses on optimizing machining parameters in the micro-milling of AlSi10Mg aluminum alloy produced via the powder bed fusion additive manufacturing process. Although additive manufacturing enables complex geometries and minimizes material waste, challenges remain in reducing surface roughness and cutting forces during post-processing. Micro-milling experiments were conducted using spindle speeds up to 60,000 rpm, with varied feed rates and cutting depths. Cutting forces (Fx, Fy, and Fz) were measured using a Kistler-9119AA1 mini dynamometer, while surface roughness (Ra) was evaluated with a Nanovea-ST400 3D optical profilometer. Five advanced machine learning models, random forest regressor (RFR), gradient boosting regressor (GBR), LightGBM, CatBoost, and k-nearest neighbors (KNN), were employed to predict cutting forces and surface roughness, with CatBoost achieving the highest predictive accuracy (R² > 0.96). Among all models, CatBoost achieved the best predictive performance, with test R² values exceeding 0.96 for both force and Ra estimations. Experimental and ML-based results demonstrated that higher feed rates and depths of cut increased cutting forces, particularly in the Fx direction, while elevated spindle speeds reduced forces due to thermal softening. Surface roughness was minimized at lower feed rates and higher spindle speeds. The optimal machining conditions for achieving Ra < 1 µm were identified as ap = 50 µm, n = 30,000 rpm, and fz = 0.25 µm/tooth. This integrated approach supports precision machining of AM aluminum alloys. Full article

Water temperature is a fundamental parameter influencing a range of biotic and abiotic processes occurring within various components of the hydrosphere. This study presents a multi-step, data-driven predictive modeling framework to estimate water temperatures for the period 2021–2100 in three aquatic environments in Central Europe: the Odra River, the Szczecin Lagoon, and the Baltic Sea. The framework integrates Bayesian Model Averaging (BMA), Random Sample Consensus (RANSAC) regression, Gradient Boosting Regressor (GBR), and Random Forest (RF) machine learning models. To assess the performance of the models, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used. The results showed that the application of statistical downscaling methods improved the prediction of air temperatures with respect to the BMA. Moreover, the RF method was used to predict water temperature. The best model performance was obtained for the Baltic Sea and the lowest for the Odra River. Under the SSP2-4.5 and SSP5-8.5 scenario-based simulations, projected air temperature increases in the period 2021–2100 could range from 1.5 °C to 1.7 °C and 4.7 to 5.1 °C. In contrast, the increase in water temperatures by 2100 will be between 1.2 °C and 1.6 °C (SSP2-4.5 scenario) and between 3.5 °C and 4.9 °C (SSP5-8.5). Full article

Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R² values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives. Full article

Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R² = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings. Full article