Search Results (6,047)

The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makespan), this paper first formulates a mixed-integer linear programming (MILP) model based on the machine-position modeling idea. Exact solution analyses on small-scale instances reveal that the strong coupling effect of these triple constraints concentrates the computational bottleneck on the time-consuming proof of optimality, thereby underscoring the strongly NP-hard nature of the investigated HFSP-SDST-T problem. To efficiently solve large-scale instances, a novel adaptive co-evolutionary memetic algorithm (ACMA) is proposed. ACMA adopts a dual-population co-evolutionary framework, where a customized genetic algorithm (GA) is designed for global exploration and a Lévy flight-enhanced particle swarm optimization (PSO) improves local search capability. To dynamically balance exploration and exploitation, a Dynamic Role Allocation (DRA) mechanism is developed to adaptively reassign individuals between the two populations according to their evolutionary states. Moreover, a progressive two-stage memetic enhancement strategy is proposed to overcome premature convergence by sequentially activating deep variable neighborhood search (VNS) and a catastrophe-based diversification strategy, enabling adaptive responses to different stagnation levels. Extensive experiments, including ablation studies, comparisons with benchmark algorithms, and computational complexity analysis, are conducted on small- and large-scale instances. The results show that ACMA consistently obtains the exact optimal solutions obtained from the MILP model for small-scale instances and achieves competitive performance on large-scale complex instances. Furthermore, Wilcoxon signed-rank tests confirm the statistical significance of the performance differences, supporting the reliability of the experimental results. Full article

Bitter peptides are a practical barrier in food-grade protein hydrolysates, fermented products, and peptide-based supplements because they can compromise flavor before nutritional or functional value is realized. Sensory panels and mass-spectrometry-based identification remain reliable, but their throughput is limited for early screening of large peptide pools. Existing predictors usually emphasize either interpretable hand-crafted descriptors or deep sequence representations, whereas these two information sources may be complementary for food-oriented bitter peptide screening. Here, we propose iBitter-HF, a hybrid feature embedding method that integrates seven classes of hand-crafted descriptors with Unified Representation (UniRep) features. Light Gradient Boosting Machine (LGBM)-based feature-importance ranking was used to organize the candidate embeddings, and eXtreme Gradient Boosting (XGB) was used for classification of the selected feature subset. On the public BTP640 benchmark, the finalized 135-feature model achieved 96.9% accuracy on the independent test set. Literature-based comparison indicated competitive performance relative to eight reported bitter peptide predictors, and dimensionality reduction visualization suggested clearer local organization of bitter and non-bitter peptides after feature optimization. These results support iBitter-HF as a computational aid for sequence-level bitter peptide screening and debittering-oriented design of protein hydrolysates. Full article

Oil palm plantations play an important role in agricultural production and carbon storage in tropical regions. The accurate estimation of aboveground carbon (AGC) is essential for sustainable plantation management, climate change mitigation, and carbon monitoring. Although field measurements provide reliable estimates, they are often time-consuming, labor-intensive, and costly, particularly over large plantation areas. Recent advances in remote sensing and machine learning offer efficient alternatives for AGC estimation using satellite imagery. In this study, we developed a machine learning framework for AGC estimation in oil palm plantations using Sentinel-2 multispectral imagery and Sentinel-1 synthetic aperture radar (SAR) data. Field measurements were integrated with spectral variables, vegetation indices, and SAR-derived parameters extracted from satellite data. A hybrid feature selection approach combining Pearson correlation, mutual information and mRMR was used to identify the most relevant variables. Six machine learning algorithms were evaluated, including Linear Regression, Random Forest, XGBoost, Gradient Boosting, LightGBM, and Extra Trees. Because the 160 observations comprise sixteen 10 m × 10 m grid cells nested within ten 40 m × 40 m field plots, model performance was assessed with leave-one-plot-out cross-validation: all sixteen cells of a plot were held out together, and the hybrid feature selection was repeated inside every fold using only that fold’s training plots. Performance was measured on pooled out-of-fold predictions using R², root mean squared error (RMSE), and average absolute relative error (AARE%). Under this spatially independent design the combined Sentinel-1 + Sentinel-2 dataset gave the highest accuracy (R² = 0.7950, RMSE = 4.14 t C ha⁻¹, AARE = 33.53%), followed by Sentinel-1 alone (R² = 0.7631, RMSE = 4.46 t C ha⁻¹) and Sentinel-2 alone (R² = 0.6108, RMSE = 5.71 t C ha⁻¹). Linear Regression and Extra Trees were the most robust models, whereas the boosted ensembles did not generalize to unseen plots. Repeating the evaluation with an ungrouped random split of the same data inflated R² by up to 0.70, showing that a large part of the accuracy obtainable under that design reflects within-plot spatial autocorrelation rather than predictive skill. These findings indicate that optical-SAR imagery combined with machine learning can provide useful AGC estimates in oil palm plantations, and that spatially independent validation is essential for reporting them honestly. The proposed framework can be used to support plantation-scale carbon mapping, monitoring, and carbon stock assessment, subject to further calibration and independent validation across additional plantations. Full article

Increasing variability in electricity production and demand contributes to greater electricity price volatility across different markets. The effective implementation of business processes related to broadly defined electricity trading therefore requires reliable electricity price forecasts. Consequently, electricity price forecasting has grown in both importance and research interest. This article presents an in-depth literature review of short-term point electricity price forecasting at the native temporal resolutions of the reviewed markets, primarily hourly and 30 min intervals, with selected studies using 15 min and 5 min intervals. The review concerns forecasts of individual market-interval prices and does not address forecasts of daily aggregated prices. The analysis covers the input data used in forecasting models, the main forecasting approaches, modelling techniques, and error measures. Forecast quality is examined with respect to market characteristics, forecasting methods, and explanatory variables. General trends in the reported results are identified, together with descriptive relationships among selected error measures. Particular attention is given to the RMSE-to-MAE ratio, referred to in this review as the Error Dispersion Factor (EDF), which is treated solely as a descriptive summary of the relative inequality of absolute forecast-error magnitudes in a given sample. The article concludes with findings and recommendations concerning best practices in electricity price forecasting. The review differs from broader conceptual and market-specific surveys by focusing narrowly on short-term point forecasts for individual market delivery intervals and by providing a structured quantitative synthesis of studies published between January 2021 and May 2026. Full article

To address seed contamination, narrow-row mechanized cutting difficulties, and potential damage to maternal plants in muddy paddy fields during hybrid rice seed production, a walk-behind self-propelled hybrid rice male parent pulverizing and cutting machine was designed. The machine primarily consists of three key structures: a key cutting device, a gravity-free crop dividing device, and a crawler walking mechanism. The cutting device features an innovative mechanism where main-shaft rotation drives flail blades into inertial autorotation, while a stopper bar physically constrains their maximum swing amplitude to guarantee a 500 mm working width. Crucially, the gravity-free crop dividing device safely pushes aside adjacent maternal plants to effectively prevent accidental mechanical injury. A flexible plant model and a kinematic model were established using DEM software EDEM 2024. A three-factor, three-level orthogonal experiment indicated that the primary order of influence on the male parent cutting rate is forward speed > flail-blade rotational speed > blade arrangement. The optimal simulation parameters were a 0.4 m/s forward speed, a 1700 r/min blade rotational speed, and a straight–curved blade arrangement, yielding a simulated cutting rate of 97.60%. Furthermore, field tests demonstrated that under these optimal parameters, influenced by complex paddy conditions and natural plant lodging, the actual average cutting rate was 91.39%. The machine exhibited excellent passability and pulverizing performance, thoroughly satisfying the requirements of agronomic and agricultural machinery integration. Full article

Sustainable rural cultural tourism requires effective approaches to improving the visibility, accessibility, and decision feasibility of dispersed cultural resources, particularly in destinations where service information is fragmented across online and offline channels and tourists face substantial uncertainty in coordinating transport, accommodation, and cultural experiences. This study examines how artificial intelligence (AI) agents can support the sustainable digital transformation of rural cultural tourism by alleviating information asymmetry, releasing latent tourism demand, and facilitating calibrated human–machine trust. Drawing on human–machine trust theory and the Stimulus–Organism–Response framework, this study conceptualizes AI agent functionality through three dimensions: AI Information Quality (AIQ), Information Extensibility (IE), and AI Planning Autonomy (APA). Travel Planning Risk Awareness (TPRA), Human–Machine Trust (HMT), Planning Satisfaction (PS), Rural Cultural Tourism Attractiveness (RCTA), and Rural Cultural Tourism Intention (RCTI) are further incorporated into an integrated model comprising four pathways: information empowerment, autonomy–risk awareness tension, trust boundary, and demand release. Using the Ctrip AI Travel Assistant as the research context, 413 valid questionnaire responses were analyzed through a hybrid Structural Equation Modeling–Artificial Neural Network approach. The results support 12 of the 14 hypotheses. AIQ significantly influences IE (β = 0.530), PS (β = 0.304), and HMT (β = 0.380). HMT functions as a central mechanism connecting AI empowerment with tourism decision-making and exerts the strongest effect on RCTA (β = 0.485), reaching 100% normalized importance in the corresponding ANN model. TPRA positively affects HMT (β = 0.262), indicating that risk awareness can facilitate rational and calibrated trust rather than simply inhibiting AI acceptance. RCTA (β = 0.281) and PS (β = 0.218) jointly promote RCTI through the complementary mechanisms of destination pull and planning push. The findings demonstrate that AI agents can contribute to the sustainable development of rural cultural tourism by improving information accessibility, strengthening responsible human–AI collaboration, and transforming fragmented cultural resources into credible and actionable travel-planning options. This study provides implications for sustainable destination marketing, responsible AI travel-service design, rural revitalization, and the long-term development of rural cultural tourism, while clarifying trust as a psychological gate in AI empowerment. Full article

Redox-active polymers have become an important component of second-generation electrochemical biosensors, solving the problem of efficient charge transfer between the biological recognition material and the electrode surface. In this review, we discuss the basic design principles, electron transfer mechanisms, and synthesis strategies from the perspective of biosensor applications. Three main classes of redox centers are considered—metal complexes, metallocenes, and organic radicals—as well as polymer matrices, and the factors affecting their stability and operability are discussed. Particular attention is paid to hybrid nanocomposites based on carbon nanotubes, graphene, and metal nanoparticles. The review concludes that despite significant advances in molecular design and the development of nanocomposites, the commercialization of biosensors based on redox polymers is hindered by unresolved issues related to biofouling, metal center instability, and low reproducibility. This emphasizes the need for standardized synthesis and integration of machine learning-based design to achieve a balance between electron transfer kinetics, biocompatibility, and operational properties. Full article

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article

Scrap charge selection has a significant influence on the thermal balance of the basic oxygen furnace (BOF) process and consequently on the final melt temperature. This paper presents a hybrid deterministic, regression, and machine learning framework for endpoint temperature prediction and steel scrap charge optimization in BOF steelmaking. The proposed methodology combines an existing deterministic BOF simulation model with regression analysis, machine learning surrogate models, and constrained nonlinear optimization. The dataset was constructed from operational records of 180 industrial BOF heats. The masses of seven scrap categories and the target endpoint temperature were obtained from these operational records, whereas the endpoint temperature used as the output for training the machine learning surrogate models was generated by the existing deterministic BOF process model. Three machine learning approaches, namely Support Vector Regression (SVR), Random Forest Regression (RF), and Gaussian Process Regression (GPR), were implemented and evaluated for endpoint temperature prediction using the masses of seven scrap categories and the target endpoint temperature as model inputs. Among the investigated surrogate models, Gaussian Process Regression achieved the best approximation performance, with a test MAE of 10.47 °C, RMSE of 16.38 °C, and $R^2$ = 0.870, and was subsequently used in the optimization framework. In addition, the deterministic BOF simulation model was incorporated into a model-based optimization procedure using the same optimization objective. Both approaches were formulated as constrained optimization problems minimizing the deviation between the predicted and target endpoint temperatures while satisfying the total scrap mass constraint. The proposed framework provides a model-based approach to temperature-oriented scrap charge planning using either a machine learning surrogate model or a detailed deterministic process model. Full article

Precision agriculture increasingly requires intelligent systems capable of integrating multimodal sensing with transparent decision support to enable timely and reliable crop management. This study proposes a hybrid intelligent IoT framework integrating environmental monitoring, wearable plant physiological sensing, AI-based pest-monitoring, machine-learning-based prediction of crop physiological stress, and explainable fuzzy rule-based decision support into a unified architecture for crop stress assessment. A novel Physiological Stress Index (PSI) was developed by combining vapor pressure deficit, relative humidity, Delta-T, leaf capacitance, and relative irradiance to provide an interpretable indicator of crop physiological stress. The proposed framework was experimentally validated under real field conditions using a commercial environmental monitoring station, wearable leaf sensors, AI-enabled pest-monitoring devices, and cloud-based analytics. Correlation analysis confirmed strong relationships between PSI and the principal environmental variables (VPD: r = 0.980, Delta-T: r = 0.990, RH: r = −0.961), demonstrating the internal consistency and sensitivity of the proposed index. At the 15 min forecasting horizon, Linear Regression and Gradient Boosting demonstrated virtually identical performance: Gradient Boosting achieved a marginally lower RMSE and higher R² (RMSE = 0.0273; R² = 0.9810), whereas Linear Regression achieved a slightly lower MAE (MAE = 0.0186). At the 1 h forecasting horizon, Gradient Boosting achieved the strongest performance (R² = 0.9034), indicating increasing relevance of nonlinear modelling at longer prediction horizons. The proposed framework demonstrates the feasibility of combining multimodal sensing, machine learning, explainable artificial intelligence, and edge-enabled IoT technologies to support proactive, transparent, and intelligent precision agriculture. Full article

Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies. Full article

Wood and wood-based materials represent low-carbon sustainable alternatives to petroleum sound absorbers, yet their baseline sound absorption coefficient varies drastically with wood species, anatomical cutting orientation and pore connectivity due to strong structural anisotropy. This review systematically integrates multiscale structural regulation, porous acoustic theories and data-driven optimization into a unified framework, revealing that broadband high sound absorption relies on the synergistic coordination of impedance matching, thermo-viscous dissipation and low-frequency resonant mechanisms, rather than simply maximizing porosity. We quantitatively compare state-of-the-art wood absorbers: directionally frozen wood aerogels achieve near-perfect absorption (α = 0.95–1.00, NRC = 0.82) across 520–6300 Hz, marking the current performance benchmark, while multifunctional superhydrophobic wood aerogels deliver moderate absorption (α ≈ 0.40) but stand out as all-biomass weather-resistant composites. Rigid-frame JCA/JCAL and poroelastic Biot models are clarified for wood’s distinct stiffness characteristics, and existing data-driven approaches are categorized, highlighting that most neural surrogates rely solely on FEM simulation without physical impedance-tube validation. Critical unresolved challenges including poor moisture/fire durability, insufficient industrial scalability and incomplete material databases are summarized, and targeted research priorities covering gradient manufacturing, hybrid physics–machine learning models and lifecycle environmental evaluation are proposed. Full article

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional variational autoencoder (cVAE), a class-conditional latent denoising diffusion probabilistic model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits, implemented and evaluated using a classical quantum-circuit simulator, to augment minority arrhythmia classes, and present results based on the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over ten independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification. To our knowledge, the proposed QLR module is the first use of a parameterized quantum circuit as a distributional refiner within a generative augmentation pipeline. While its performance is comparable to that of the classical latent diffusion framework under the present experimental conditions, the proposed approach demonstrates the feasibility of integrating quantum latent operators into generative medical AI pipelines and provides a foundation for future investigations on quantum-enhanced representation learning and data augmentation. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article