Search Results (6,990)

This study presents a structured multi-sensor predictive maintenance framework for CO${}_2$ laser cutting machines based on real-time data acquisition and supervised machine learning. The proposed architecture integrates heterogeneous sensor signals—including vibration, temperature, humidity, and acoustic measurements—through synchronized feature-level fusion to characterize machine operational states. A statistically grounded thresholding strategy, validated using two years of operational observations and controlled experimental perturbations, is employed to distinguish normal and abnormal behavior. Sensor data are processed using a Decision Tree classifier implemented in Python with Scikit-learn, enabling short-horizon probabilistic fault prediction during operational cycles. The system is deployed in a real industrial environment and validated using cross-validation and structured dataset partitioning to assess generalization performance. Results demonstrate reliable fault discrimination capability under controlled operational conditions, highlighting the effectiveness of feature-level sensor integration for early anomaly detection. The modular hardware–software architecture supports adaptability to other CNC platforms with appropriate recalibration and retraining. The proposed framework provides a low-cost, interpretable, and computationally efficient solution for real-time industrial predictive maintenance applications. Full article

Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models (Random Forest, XGBoost, CatBoost, LightGBM, Extra Trees, and Decision Tree) are systematically evaluated using large-scale Spotify data. Among these models, Random Forest achieves the best predictive performance on this dataset (RMSE = 6.79, MAE = 5.10, and $R^2$ = 0.6658), followed by Extra Trees ($R^2$ = 0.6378) and Decision Tree ($R^2$ = 0.6328). Bayesian hyperparameter optimization based on a Tree-structured Parzen Estimator with an Expected Improvement acquisition function is conducted over 50 trials with 5-fold cross-validation to ensure robust model selection. Shapley value decomposition via SHAP analysis reveals that temporal recency dominates feature importance, far surpassing traditional musical attributes, while acoustic intensity (loudness) exhibits a U-shaped contribution pattern with optimal values at moderate intensity levels. Further SHAP dependence analysis uncovers non-linear relationships, indicating substantial popularity advantages for recent releases and optimal loudness levels around $-5$ to 0 dB. These findings suggest that streaming popularity is primarily governed by temporal exposure dynamics and production-related characteristics rather than intrinsic musical structure, offering both theoretical insights for music information retrieval research and suggestive empirical patterns that may inform future investigations into digital music ecosystems. Full article

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources. Full article

Predicting carbon dioxide (CO₂) solubility in brine is critical for carbon capture and storage. This study employs the Ant Colony Optimization (ACO) algorithm to enhance the predictive accuracy of four machine learning models: Neural Network (NN), Decision Tree (DT), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM). The models were trained and validated on a mineral compound dataset. Performance was evaluated using the coefficient of determination (R²) and error metrics including RMSE and MAE. The GBM model achieved the highest test accuracy (R² = 0.986) with low errors (RMSE = 0.0478, MAE = 0.0362), demonstrating superior ability to model complex, non-linear relationships with minimal overfitting. The optimized NN, featuring three layers and fifteen neurons, delivered strong performance (R² = 0.930) with balanced errors across datasets. The DT model offered excellent interpretability and a strong test score (R² = 0.912), while the SVR model provided robust generalization (R² = 0.889). The results indicate that ACO is an effective tool for hyperparameter tuning across diverse model architectures. For maximum accuracy, GBM is recommended, whereas DT is ideal when interpretability is required. The NN presents a strong middle-ground option with competitive accuracy. This comparative framework assists in selecting the optimal model based on specific project priorities of accuracy, transparency, or computational efficiency for geochemical forecasting. Full article

Credit risk prediction is essential for financial institutions to effectively assess the likelihood of borrower defaults and manage associated risks. This study presents a comparative analysis of deep learning architectures and traditional machine learning models on imbalanced credit risk datasets. To address class imbalance, we employ three resampling techniques: Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and the hybrid SMOTE-ENN. We evaluate the performance of various models, including multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), logistic regression, decision tree, support vector machine (SVM), random forest, adaptive boosting, and extreme gradient boosting. The analysis reveals that SMOTE-ENN combined with MLP achieves the highest F1-score of 0.928 (accuracy 95.4%) on the German dataset, while SMOTE-ENN with random forest attains the best F1-score of 0.789 (accuracy 82.1%) on the Taiwanese dataset. SHapley Additive exPlanations (SHAP) are employed to enhance model interpretability, identifying key drivers of credit default. These findings provide actionable guidance for developing transparent, high-performing, and robust credit risk assessment systems. Full article

The Montado, a traditional Mediterranean agro-silvopastoral system, has historically sustained ecological and economic functions through the integration of trees, livestock, and crops. Today, its multifunctionality is increasingly threatened by climate variability, market volatility, and evolving policy frameworks. While previous research has examined Montado dynamics at landscape or plot scales, less attention has been paid to sustainability trajectories at the farm level, where management decisions are made. This study bridges that gap by assessing the sustainability dynamics of farms through a participatory, typology based, scenario approach grounded in a regional typology. We characterized three representative farm archetypes (forestry-focused, mixed agro-silvopastoral, and livestock-focused) and evaluated their trajectories under plausible future scenarios driven by climate, market, and policy pressures. Scenario outcomes were assessed using expert-based scoring (five-point scale), revealing score differences of up to two points across sustainability dimensions between farm archetypes and scenarios. Findings reveal marked trade-offs: Tree-focused farms maintain high environmental value but remain vulnerable to market and labor constraints, while livestock-specialized farms achieve higher economic output at the expense of ecological integrity. Mixed systems demonstrate greater resilience through diversification but face significant labor intensity challenges. We conclude that current “one-size-fits-all” policies generate contradictory incentives. Therefore, adaptive governance frameworks (e.g., results-based payment schemes) are essential to realign farm economics with ecological stewardship. Beyond the Montado, the approach provides insights relevant to other Mediterranean agroforestry systems facing similar sustainability challenges. Full article

This study investigates the effects of extraction technique, solvent type, and geographical origin on the recovery of bioactive compounds from Arbutus unedo L. leaves collected from two Croatian islands (Vis and Mali Lošinj) and extracted using conventional, Soxhlet, and ultrasound-assisted extraction (UAE) with green solvents (distilled water, 70% ethanol, and ethyl acetate). Extracts were purified and characterized by thin-layer chromatography, column chromatography, and FTIR spectroscopy. Total phenols, hydroxycinnamic acids, flavonols, condensed tannins, and antioxidant capacity were quantified spectrophotometrically. Solvent type had the greatest influence, with 70% ethanol yielding the highest levels of bioactives and antioxidant capacity. Geographical origin significantly affected total phenolics and condensed tannins, with leaves from Vis outperforming those from Mali Lošinj. UAE was slightly more efficient than conventional and Soxhlet methods, particularly for thermolabile phenolics. Machine learning algorithms were applied as exploratory tools, using total phenols as a proxy variable to estimate selected bioactive compounds and antioxidant capacity based on extraction parameters. Decision Tree and Gradient Boosting models showed high goodness of fit within the experimental dataset (R² > 0.91). These results support the potential of green extraction strategies combined with data-driven screening for the valorization of A. unedo leaf extracts, while highlighting the need for further validation prior to industrial application. Full article

To address the challenges of strong tectonic stress anisotropy, multi-scale pore networks, and complex seepage pathways in the tight sandstone reservoirs of the Shaximiao Formation, southern Sichuan Basin, this study integrates petrophysical analysis with machine learning techniques to develop an intelligent flow unit identification methodology applicable to complex structural settings. Based on core petrophysical properties, mercury injection capillary pressure (MICP) data, and production dynamics, the reservoirs were classified into a fracture-type plus four conventional-type (I–IV) flow unit system. Quantitative identification of flow units was achieved using conventional well-logging curves (Gamma Ray, Spontaneous Potential, Caliper, etc.—eight curves total) using the Gradient Boosting Decision Tree (GBDT), Backpropagation Neural Network (BPANN), and Supervised Self-Organizing Map (SSOM) algorithms. Key findings include the following: The SSOM algorithm delivered optimal performance, achieving a 90.1% average accuracy on the test set, significantly outperforming GBDT (87.8%) and BPANN (85.5%), particularly in capturing nonlinear responses of fracture-type reservoirs and class-overlapping samples. Flow unit spatial distribution exhibits dual sedimentary-structural control: High-quality units (Types I/II) are enriched at the base of distributary channels in deltaic plain facies (J₂S1²), while fracture-type units cluster near fault peripheries. Strong planar heterogeneity is observed in the J₂S1³ sub-member: Near-source areas (south/southwest) develop banded Type I/II units, whereas distal regions are dominated by Type IV units. This methodology provides a theoretical foundation and intelligent technological pathway for the efficient development of highly heterogeneous tight sandstone reservoirs. Full article

The integration of renewable energy and power electronics has intensified the occurrence of complex power quality disturbances (PQDs), which increasingly threaten grid stability. To address the challenges of multi-class PQD identification under noisy conditions, this paper proposes a novel framework that combines an enhanced time–frequency analysis method with an optimized AdaBoost decision tree. The main contributions are three-fold: (1) We develop an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) equipped with a custom adaptive operator that aligns closely with the frequency modulation patterns in PQD signals, thereby improving time–frequency energy localization. (2) The IFAFSST outputs are decomposed into low-frequency and high-frequency components, from each of which a set of 16 discriminative features is extracted. (3) An improved AdaBoost classifier is introduced, incorporating forward feature selection and Hyperband-based hyperparameter optimization to enhance classification performance. Hyperband accelerates the optimization process by dynamically allocating computing resources and iteratively eliminating suboptimal configurations, thereby enabling efficient determination of the optimal hyperparameters. The method proposed in this paper achieved an accuracy rate of 99.50% on simulated data containing 30 dB white noise and 98.30% on hardware platform data. This framework can effectively handle 23 types of interference, including seven types of single interference, 12 types of double compound interference, three types of triple compound interference, and one type of quadruple compound interference. It performs particularly well in identifying composite interference scenarios. This research has made a significant contribution to power quality analysis, providing a powerful solution with high accuracy and practical applicability, and offering great potential for the implementation of smart grid monitoring systems and the integration of renewable energy. Full article

Perovskite oxides show excellent catalytic performance for thermochemical CO₂ splitting, with A/B-site cation substitution further enhancing redox activity. While traditional first-principles methods are computationally expensive, machine learning (ML) provides an efficient approach to perovskite optimization. In this paper, machine learning is employed to investigate and predict the performance of perovskite catalysts in CO₂ decomposition reactions. Based on 227 perovskite compositions (A₁A₂)(B₁B₂)O₃ curated from experimental literature, a total of five ML models are used, including Decision Tree, Bagging, Random Forest, Extra Trees, and Gradient Boosting Regression (GBR). The Random Forest model performed best. After hyperparameter optimization, the Random Forest model achieved an R² of 0.910 and an MAE of 41.528 on an independent test set. SHAP analysis indicated that the thermal reduction temperature (T₁) and the B1-site stoichiometric fraction (C_b₁) are the most influential features governing the predicted CO yield. A higher CO yield is predicted when C_b₁ ranges from 0.6 to 0.8, and T₁ exceeds 1300 °C. This behavior can be attributed to the enhanced formation of oxygen vacancies at elevated temperatures and the optimized electronic structure induced by appropriate B-site stoichiometry. Full article

Precise timing of rice harvesting is critical for ensuring grain yield and quality. Traditional manual evaluation methods are highly subjective and time-consuming, highlighting the critical demand for rapid, non-destructive approaches to estimate rice maturity. This study focused on cold-region japonica rice grown in Heilongjiang Province, aiming to develop and validate dual-scale (pot and field) maturity estimation models. For model development, canopy spectral data were collected using two complementary acquisition tools: a ground-based active sensor (CGMD402) and UAV-borne multispectral imagery. Four modeling algorithms—Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were utilized, with input variables comprising single spectral indices (Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI) and composite spectral indices (Normalized Difference Maturity Ratio Vegetation Index, NDMRVI; Normalized Difference Pigment Ratio Vegetation Index, NDPRVI). At the pot scale, composite spectral indices showed stronger correlations with rice maturity than single indices. Among the four algorithms, the DT model with combined NDVI + RVI input yielded the optimal comprehensive performance, with a coefficient of determination (R²) of 0.957, a root mean square error (RMSE) of 0.064, and a relative error (RE) of 4.8% in the test set. At the field scale, NDVI and RVI both exhibited strong negative correlations with maturity (Spearman’s correlation coefficients of −0.76 and −0.79, respectively). While the RF model performed best in the training set (R² = 0.752), it was prone to overfitting; in contrast, Multiple Linear Regression (MLR, Ridge Regression) with NDVI + RVI combination demonstrated greater stability in the test set (R² = 0.515, RMSE = 0.116). Notably, composite spectral indices consistently outperformed single indices across all modeling algorithms, but their accuracy was comparable to the optimal single index combination model. To tackle the challenge of scaling models from pot to field conditions, this research developed a “modeling–validation–evaluation–scaling” framework and a four-indicator combined judgment criterion (ΔR²–ΔRMSE–ΔRE–SF). Quantitative analysis showed that the optimal pot-scale model suffered significant accuracy loss during cross-scale transfer: ΔR² = 0.447, ΔRMSE = 0.120, ΔRE = 22.84%, and Scale Transfer Factor (SF) = 2.875. A “regional calibration + residual correction” scheme was proposed, which is expected to reduce the transferred RMSE to below 0.12 and SF to 1.8–2.0. Overall, this research offers a reliable technical method for large-scale, non-destructive monitoring of rice maturity, which can facilitate data-driven precision harvesting decisions. Full article

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article

Cutaneous melanoma is one of the most aggressive skin cancers, and early diagnosis remains essential to reduce mortality. Reflectance Confocal Microscopy (RCM) provides non-invasive, quasi-histological images of the epidermis, dermoepidermal junction (DEJ), and dermis, enabling real-time assessment of melanocytic lesions. However, interpretation still relies on expert visual evaluation, which is time-consuming and subjective. In this context, Artificial Intelligence (AI) and Computer-Assisted Detection (CAD) systems are emerging as valuable tools to improve diagnostic accuracy and reproducibility. This review summarizes research on AI applications in RCM imaging for melanoma, focusing on three major areas: delineation of skin strata, segmentation of tissues and morphological patterns, and classification of benign versus malignant lesions. Early approaches included Bayesian classifiers, wavelet-based decision trees, and logistic regression, while recent studies have employed support vector machines, random forests, and increasingly deep learning architectures such as convolutional and recurrent neural networks. The results demonstrate encouraging accuracy in DEJ localization, the segmentation of diagnostically relevant patterns, and the discrimination of melanoma from benign nevi. We distinguish the maturity of dermoscopy-based AI (AUC (ROC) > 0.80 on large multicenter cohorts) from the still-exploratory evidence for RCM-based AI. Nonetheless, current studies are often limited by small datasets, heterogeneous protocols, and a lack of multicenter validation. Overall, progress in AI applied to RCM supports the development of CAD systems that could assist clinicians during acquisition and diagnosis, reducing unnecessary biopsies and improving early melanoma detection. Future work should address standardization, dataset expansion, and the integration of advanced AI methods to move closer to clinical implementation. Full article

Assessing the investment attractiveness of companies is essential for effective capital allocation under conditions of uncertainty and heterogeneous risk–return profiles. Investors typically face multiple financing alternatives, making comparative evaluation impossible without robust and specialized assessment methodologies. This study proposes a refined conceptual model for assessing the investment attractiveness of production companies, with a specific focus on the manufacturing sector of Kazakhstan. The research is based on a modeling-oriented methodological framework that integrates a modified discounted cash flow (DCF) approach with elements of environmental controlling. The proposed model incorporates sector-specific characteristics, including resource utilization patterns, regulatory requirements and the potential “green” premium observed in capital markets. To capture investment-related uncertainty and risk, the study employs material flow cost accounting, scenario-based modeling and probabilistic decision tree analysis. Particular attention is given to improving the determination of the discount rate, recognizing its critical influence on present value-based investment assessments. The model accounts for macroeconomic and sectoral factors specific to Kazakhstan’s production industry and offers alternative discount rate estimation scenarios under different initial conditions. The study contributes to the literature on investment attractiveness assessment by integrating financial, environmental and risk dimensions into a unified framework. The proposed model enhances transparency in investment decision-making and provides new insights into investment evaluation practices in emerging industrial economies. Full article

The tensile capacity of a connection is predicted through the use of established models, among which the bond behavior between CFRP layers and concrete is always considered. In structures reinforced with CFRP, the prediction of the bond force between concrete and CFRP is essential, as the connection must be designed to withstand the required tensile capacity. An underestimation can lead to inefficient design, while an overestimation risks premature debonding failure, potentially compromising structural safety and serviceability. In recent applications, the bond force between concrete and CFRP has been increased through the use of the Externally Bonded Reinforcement on Groove (EBROG) method. However, due to the structural complexity introduced by the grooved interface, accurate prediction of its bond strength remains challenging, and conventional analytical models may not fully capture the underlying nonlinear interactions. In this technique, CFRP layers are placed into grooves to enhance the interaction among the adhesive, concrete, and CFRP. However, due to the structural complexity of this connection, accurate prediction of its bond force is challenging and requires the application of artificial intelligence methods. This study develops a machine learning (ML) framework to predict the bond strength of the EBROG technique. Four ML models, Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree, and XGBoost, were implemented, and their hyperparameters were optimized via Bayesian optimization. The models were evaluated using multiple statistical metrics, with the XGBoost algorithm demonstrating superior predictive performance, achieving an R² of 0.987 and an RMSE of 0.522 kN. This represents an improvement of approximately 5.6% in R² and a reduction of over 53% in RMSE compared to the existing analytical model. SHAP analysis provided interpretable, data-driven insights, revealing that fracture energy is the predominant factor governing bond strength and elucidating nonlinear interactions between key design parameters. This ML-fracture mechanics framework not only offers superior prediction but also advances the mechanistic understanding of the EBROG bond behavior. Full article