Search Results (1,970)

The study presents the results of research aimed at developing digital models for determining the operating parameters of railway power supply systems equipped with distributed generation plants based on renewable energy sources (RESs). RESs can be used in railway transport to increase the reliability of power supply to facilities located in areas with insufficiently developed power grids. This primarily applies to consumers, for whom a power failure can lead to significant damage, accidents, and a threat to human life. RES can serve as independent power sources for special-group consumers and can increase energy conversion efficiency. Furthermore, large-scale implementation of renewable energy sources can significantly reduce energy supply costs and improve power quality. The study employs phase-coordinate modeling, which is characterized by the following features: a systems approach, which implies determining operating conditions while considering the properties and characteristics of complex traction and supply networks; versatility, which enables modeling of power supply systems of various structures and designs; and comprehensiveness, which involves calculating normal, emergency, and special operating parameters—crucial for scenarios such as ice melting on catenary wires. The modeling results obtained using the Fazonord AC-DC software (ver. 5.3.5.2) show that RES-based distributed generation plants provide a variety of beneficial effects: reduction in electricity consumption from power system networks; decrease in voltage unbalance and harmonic distortion on the busbars of regional windings of traction substations; and stabilization of voltage levels on current collectors of electric locomotives. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

To improve the accuracy of fault prediction for power generation steam turbines and address the challenges associated with high-dimensional, nonlinear monitoring data and cumbersome hyperparameter tuning, this study proposes an intelligent fault prediction method. Although mutual information (MI)-based feature selection and Bayesian optimization (BO) for hyperparameter tuning have each demonstrated individual success in fault diagnosis applications, existing approaches predominantly treat these two critical aspects as isolated and independent procedures. This separation limits the synergistic potential between feature quality and model configuration, leaving a gap in coordinated, fully automated fault prediction frameworks for steam turbines. To bridge this gap, the proposed method, termed BO-CNN-BiLSTM, presents an automated pipeline that sequentially integrates MI-based adaptive feature selection with Bayesian optimization for hyperparameter tuning of a CNN-BiLSTM network. Initially, MI combined with K-means clustering automatically identifies and retains key features strongly associated with fault states, effectively reducing input dimensionality. Subsequently, a BO framework is employed to autonomously search for the optimal hyperparameter configuration, achieving seamless integration from feature selection to model optimization. Validation via a self-built physical-information fusion experimental platform demonstrates that the optimized model attains a root mean square error (RMSE) of 0.324 and a coefficient of determination (R²) of 0.888 on the test set. Its predictive performance significantly surpasses that of models lacking Bayesian optimization, as well as those employing standalone CNN or BiLSTM architectures. This study thus presents a highly automated, accurate, and practical intelligent fault prediction scheme for steam turbines in power generation. Full article

This study proposes a vision-based framework for automated inspection and quality grading of beef steaks by integrating fat marbling distribution analysis and lean-meat color evaluation. In frozen beef products, surface frost often generates specular reflections that resemble both fat and lean regions, thereby reducing segmentation accuracy. To address this challenge, a sequential and interpretable analytical framework is developed. First, homomorphic filtering is applied to suppress frost-induced illumination artifacts, followed by curvelet transform combined with square-ring filtering to separate fat and lean regions based on their multi-scale and directional characteristics. For marbling analysis, the convex hull, skeleton, and principal axis of the steak are extracted, and a chi-square goodness-of-fit test is performed within eight predefined regions to quantitatively evaluate marbling distribution uniformity and identify localized fat accumulation. For lean-meat evaluation, RGB color features are extracted and classified using a Support Vector Machine (SVM) to determine redness levels. The resulting marbling and color information are subsequently integrated through a weighted grading strategy to estimate the final quality grade. Experimental results demonstrate a fat detection rate of 92.68%, a false-positive rate of 4.97%, and a correct classification rate of 94.09% for fat segmentation, while the SVM-based lean-meat color classifier achieves an accuracy of 96.67%. Furthermore, the proposed grading framework attains an overall grading accuracy of 90.38%, showing strong agreement with human evaluation. Full article

The initial seconds after an earthquake are critical for rapid magnitude estimation to support real-time early warning. This study evaluates the determination of P-wave moment magnitude (M_wp) using strong-motion records from the 23 April 2025 Marmara Sea earthquake. High-quality accelerometric data from the Turkish National Strong Motion Network were analysed to extract early P-wave features within the first 3 s after P-wave onset. Results show significant rupture-directivity effects, whereby stations located approximately along the fault strike and rupture-propagation direction recorded larger ground-motion amplitudes and higher station-based Mwp estimates than stations located near nodal directions. The mean M_wp was 6.5 ± 0.2, consistent with the Global Centroid Moment Tensor (GCMT) moment magnitude estimate. Magnitude estimation was achievable within 8–20 s of P-wave arrival, confirming the method’s real-time applicability. Our findings demonstrate that strong-motion P-wave analysis can provide rapid and reliable magnitude estimates suitable for earthquake early warning, tsunami warning, and rapid-response applications. In the Marmara Sea region, where tsunami arrival times may be on the order of 20–30 min and critical infrastructure is concentrated in densely populated coastal areas, rapid determination of magnitude within seconds of earthquake initiation can provide valuable information for emergency management and hazard mitigation decisions. Full article

This study presents an Indoor Environmental Quality (IEQ)-aware framework for residential valuation by integrating low-cost IoT sensing, transparent scoring, and hedonic price modeling. The analysis uses a dataset of 244 apartments across 12 districts in Tehran. It combines indicators of thermal comfort, particulate exposure, lighting, acoustics, stability, exceedance, and uncertainty with conventional housing covariates (area, age, bedrooms, floor level, renovation status, amenities, and accessibility proxies). Results show that pooled IEQ–price relationships are weak and confounded, whereas controlled specifications produce modest but consistent improvements in explanatory fit after IEQ features are introduced. Conventional location and structural attributes remain the dominant determinants of price per square meter. Still, IEQ contributes a non-redundant information layer that improves within-segment differentiation and interpretability for inspection and listing workflows. Methodologically, the framework extends beyond average comfort metrics by incorporating volatility, threshold exceedance duration, and sensor uncertainty, enabling uncertainty-aware reporting rather than single-point scoring. In practice, the workflow supports portable sensing, reproducible analytics, and privacy-preserving edge aggregation, suitable for PropTech deployment. The findings support a cautious but actionable conclusion: IEQ should be treated as an incremental valuation signal rather than a standalone pricing determinant. In this context, IEQ is conceptualized as a supplementary attribute block that may add explanatory value beyond conventional housing covariates rather than as a standalone pricing determinant. Full article

Loquat (Eriobotrya japonica) is a commercially important subtropical fruit, and its internal sweetness is an important indicator of market quality. Accurate and non-destructive determination of total soluble solids content (TSSC) is therefore essential for fruit grading and quality evaluation. In this study, short-wave infrared hyperspectral imaging (1000–2400 nm) was combined with a multi-scale spectral attention adaptive convolutional neural network (MSSA-ACNN) for rapid TSSC prediction. Spectral data were preprocessed using an SG-MSC-DT strategy to reduce noise and scattering effects, while conventional models (PLSR, Ridge, and SVM) were used for comparison. The proposed model combines multi-scale feature extraction with a dual-path attention mechanism, enabling adaptive enhancement of informative chemical wavebands while suppressing irrelevant variations. Experimental results, rigorously validated through a 5-fold cross-validation strategy, demonstrated that the proposed approach achieved the best predictive performance, with an R_p² of 0.942, RMSEP of 0.505, and RPD of 3.091, outperforming traditional methods. In addition, attention weight analysis revealed that the model mainly focused on spectral regions associated with water and carbohydrate absorption, indicating consistency between the learned features and known chemical information. These results suggest that the proposed method provides an effective and interpretable approach for non-destructive evaluation of loquat quality and shows potential for application in intelligent fruit grading systems. Full article

A series of co-doped LiNbO₃:Mg:Er crystals were grown in a single technological cycle and under the same technological conditions by Czochralski. In each subsequent step of the growth cycle, the content of Mg and Er dopants decreased. The initial concentration of dopants in the melt was [Mg] = 4.0 mol% and [Er] = 0.78 mol%. The melt was obtained from a homogeneously doped batch. The batch included the Nb₂O₅:Mg:Er precursor synthesized by the liquid-phase method. The physicochemical features of crystallization were studied. The optical properties of the crystals were investigated using laser conoscopy and photoinduced light scattering. Macro- and microdefect structures were studied by optical microscopy. Quantitative phase analysis was performed for single-crystal samples. The defect structures of powdered LiNbO₃:Mg:Er samples were determined by refining XRD patterns by Rietveld. The optical quality of doubly doped crystals corresponds to that of singly doped LiNbO₃:Er crystals. Mg significantly reduces the transparency of LiNbO₃:Mg:Er crystals in the ultraviolet and violet spectral ranges. The optimal dopant concentration in the melt was [Er] = 0.63 mol% and [Mg] = 3.0 mol%, and [Er] = 0.47 mol% and [Mg] = 3.07 mol% in crystal. The optical properties of LiNbO₃:Mg:Er crystals make them promising active nonlinear optical materials for generating and converting laser radiation. Full article

Thickness accuracy is a critical quality indicator in medium and heavy plate production, as it directly affects material utilization, product performance, and manufacturing cost. The rolling process of medium and heavy plates is highly nonlinear. It also involves multivariable coupling and dynamic fluctuations in operating conditions. Therefore, achieving highly accurate and reliable thickness prediction in industrial applications remains a major challenge. To address this issue, this paper develops a joint point-interval prediction framework for medium and heavy plate thickness in industrial applications. First, recursive feature elimination with a LinearSVR estimator (LinearSVR-RFE) is employed to eliminate low-contribution features from the original process feature set, retain informative variables, and construct a compact and effective feature subset. Second, Bayesian optimization is employed to tune the hyperparameters of multiple machine learning regression models. A Stacking ensemble strategy is then adopted to improve the accuracy and robustness of point prediction under complex production conditions. Finally, quantile regression is introduced based on the optimal point prediction model to construct prediction intervals at multiple confidence levels. This provides uncertainty-aware results for production decision-making. Experimental results based on real industrial data from a 3500 mm medium and heavy plate production line show that the proposed framework achieves strong point prediction performance on the test set. The optimal Stacking model achieves a coefficient of determination (R²) of 0.9845 with a root mean square error (RMSE) of 0.73 mm on the test set. In addition, the framework produces prediction intervals with a good balance between coverage and compactness at confidence levels from 80% to 95%. For example, at the 90% confidence level, the interval prediction module achieves a PICP of 0.9043 and a PINAW of 0.0711. The results indicate that the proposed framework provides an effective solution for intelligent thickness prediction and quality evaluation in industrial rolling processes. It also shows good potential for engineering applications. Full article

Honey is a natural food product which in traditional production represents a clear example of the “farm-to-table” principle, as it excludes any processing of the original product. This study proposes an analytical approach for determining 30 most frequently determined chemical elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Co, Cr, Cs, Cu, Ga, In, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Rb, S, Se, Sr, Te, V, and Zn) in honey, emphasizing the use of a relatively large sample mass to overcome sample heterogeneity and ensure accurate and reliable results. About 31 linden and 16 rapeseed honey samples from different Bulgarian regions were analyzed. Pollen analysis data showed that pollen content ranged from 30 to 78% for linden and 30 to 93% for rapeseed honey. The results identify a group of elements—K, Ca, Mg, Sr, and Rb—whose concentrations show statistically significant dependence on the floral origin and purity of the honey. Based on these findings, these elements are proposed as potential markers for identifying the botanical origin of honey. Furthermore, macronutrients and micronutrients (P, S, B, Cu, Fe, Mn, and Zn), which are generally subject to homeostatic regulation, as well as micro-elements (Al, As, Cd, Co, Cr, and Pb), which are more strongly influenced by environmental factors, showed limited discriminatory potential and no clear correlation with floral purity and botanical origin. Therefore, they should not be used as criteria when assessing the botanical origin of honey, but rather as indicators of environmental pollution and potential quality or safety concerns. Overall, the research contributes to improving the reliability of botanical classification of honey by combining robust analytical methodology with statistically validated elemental markers, while also distinguishing between natural compositional features and contamination-related signals. Full article

Rational fertilization directly affects the fruit quality of the Korla fragrant pear. However, the variation patterns of fruit appearance and texture indicators under different N-P₂O₅-K₂O ratios are complex, and redundancy among high-dimensional indicators restricts the practical application of quality discrimination and fertilization traceability. In this study, Korla fragrant pear fruits harvested under eight fertilization treatments (including the control) were selected as research materials. Significant differences existed in nutrient composition and application rate among treatments: no N-P₂O₅-K₂O was applied in the CK treatment; for treatments H1–H7, nitrogen (N) application rate ranged from 396.36 to 524.2 g·plant⁻¹, phosphorus (P₂O₅) from 326.08 to 652.17 g·plant⁻¹, and potassium (K₂O) from 450.67 to 1200.08 g·plant⁻¹, with the most prominent differences observed in P-K ratios and application rates. On this basis, 12 appearance and flesh texture indicators were determined, including single-fruit weight, longitudinal diameter, transverse diameter, fruit shape index, pericarp thickness, sclereid content, hardness, adhesiveness, cohesiveness, springiness, gumminess and chewiness. Three machine-learning algorithms, namely Random Forest (RF), Extreme Learning Machine (ELM) and K-Nearest Neighbor (KNN), were used to construct fruit quality discriminant models. The results showed that the RF model achieved the optimal discriminative performance, with accuracy values of 0.876 and 0.865 for the training and validation sets, respectively. Seven core quality indicators, including sclereid content and longitudinal diameter, were screened via feature-importance intersection analysis. The reconstructed RF model based on this indicator set exhibited nearly no loss in discriminative accuracy despite a ~42% reduction in indicator quantity, providing theoretical and technical support for quality grading, fertilization traceability and precision fertilization of Korla fragrant pear. Full article

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management. Full article

Proteins with additives, especially in small quantities, are of great interest as a subject of study. Machine learning approaches implemented on Raman spectroscopy data could provide an insight into the chemical structures of such mixtures or conjugates. Although decision tree models could be powerful in solving either classification or regression tasks and could provide accessible predictions, they are prone to overfitting. Ensemble models that implement several decision trees could overcome the determined problem. Five different model types are discussed: RandomForest, GradientBoosting, AdaBoost, Voting, and Stacking. Raman spectroscopy data of whey protein isolates (5 wt.%) with different amounts of hyaluronic acid (0, 0.1, 0.25, and 0.5 wt.%) were used as datasets. In order to generalize the results of the study, WPI samples from three different manufacturers were used. Optimization established that ensembles of 200 decision trees with a maximum depth of four were optimal. The Stacking algorithm, which used RandomForest, GradientBoosting, and AdaBoost as base models with either LogisticRegressor (classification task) or RidgeCV (regression task), was found to be the most efficient in finding differences between the whey protein isolate and its conjugates with hyaluronic acid: specificity of 68.7% and sensitivity of 95.4% (classification task); R² = 0.764 with mean absolute error of 0.068 (regression task). According to the feature importance plots, the Raman bands that were most influential in predicting the results were 1003 cm⁻¹ (phenylalanine, ring breath), 1125 cm⁻¹ (rocking of NH₃⁺), 1206 cm⁻¹ (C–C stretching), 1240 cm⁻¹ (amide III (β-sheet), N–H in-plane bend, C–N stretch), and 1399 cm⁻¹ (aspartic and glutamic acids, C=O stretch of COO–). The findings of this study may contribute to the development of novel methods for quality control and analysis of complex multicomponent systems in various industrial settings. In particular, the ensemble approach can be adapted for monitoring in food processing or as a screening tool in pharmaceutical formulation development. Full article

Online monitoring of polyacrylamide (PAM) concentration is needed for quality control in continuous fracturing-fluid blending and for closed-loop smart fracturing operations. This study evaluates the feasibility and current limits of an MR-based PAM assay route. Static CPMG-T2 tests on an existing 4.6 MHz magnetic resonance multiphase flowmeter (MRMF) platform showed that T2-based viscosity discrimination is useful when the PAM concentration is above approximately 3‰, but it becomes insufficient in the 1–3‰ low-concentration interval. A 20 MHz laboratory T2-D validation test indicated that the apparent diffusion coefficient responds more clearly to PAM-induced molecular-mobility variation than T2 alone. On this basis, a 23.5 MHz diffusion-capable online detector concept was developed, featuring a permanent-magnet module, a gradient-capable RF probe, compact spectrometer electronics, and a quasi-static bypass sampling process for oilfield installation. The revised interpretation framework combines T1, T2, diffusion coefficient, temperature, signal-quality indicators, repeatability checks, and calibration-domain gating. The present work defines a proof-of-concept route, and the validation requirements for online PAM concentration monitoring; final accuracy, repeatability, RMSE, confidence intervals, and field-calibrated acceptance thresholds must still be determined through controlled loop and field tests. Full article

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM_2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min. Full article