Search Results (861)

This study investigates nitrogen sources and biogeochemical pathways in a highly urbanized shallow aquifer in Shinagawa Ward, Tokyo, using an integrated approach combining hydrochemical analysis, multivariate statistics (PCA and K-means cluster analysis), and stable nitrogen isotopes (δ¹⁵N-NH₄⁺, δ¹⁵N-NO₃⁻, δ¹⁵N-DON, and dual δ¹⁵N–δ¹⁸O-NO₃⁻). K-means clustering (K = 2, silhouette = 0.54) partitioned all 41 samples into a background group (n = 34) and an ion-enriched group (n = 7; wells sbi 1, 2, 3, 4, 5, 13, and 19), with the latter exhibiting hydrochemical signatures consistent with localized sewage leakage. The convergence of hydrochemical, multivariate, and isotopic evidence suggests that soil organic matter may represent the dominant diffuse background source of nitrogen across the study area. DON constitutes the dominant fraction of total dissolved nitrogen (TDN), while the linear correlations between TDN and DON concentrations (r = 0.77, p < 0.001) and between δ¹⁵N-TDN and δ¹⁵N-DON (r = 0.88, p < 0.001) indicate a common primary source. The dominance of DON combined with the theoretical inverse relationship between δ¹⁵N-DON and DON concentration is consistent with active soil DON mineralization, supported by an isotope fractionation factor (ε = −4.4 ± 0.78‰). Dual isotope analysis of NO₃⁻ (δ¹⁵N–N–δ¹⁸O slope = 0.51) points towards denitrification as an ongoing process in the aquifer. Taken together, the isotopic variations among nitrogen species suggest a transformation sequence from soil organic nitrogen → DON → NH₄⁺/NO₃⁻ → N₂, though each step in this sequence is supported to varying degrees of confidence. These findings highlight the value of δ¹⁵N-DON as a tracer for nitrogen source attribution and cycling in urban groundwater systems, and underscore the importance of considering all dissolved nitrogen fractions in contamination assessments. Full article

Electric bicycles are popular due to their environmental benefits and convenience. However, electric bicycle fires caused by series arc faults remain a serious safety concern. This study focuses on series arc fault identification for electric bicycles under complex operating conditions, covering state of charge (SoC), torque, and speed variations, and simultaneously considers normal state, DC-side series arc fault, and AC-side series arc fault conditions. Five time-domain features, namely root mean square (RMS), standard deviation (STD), skewness (SK), kurtosis (KUR), and current amplitude (CA), and three frequency-domain features, namely amplitude–frequency energy (AFE), amplitude–frequency mean (AFM), and amplitude–frequency kurtosis (AFK), are extracted. An improved principal component analysis (PCA)-based feature fusion method transforms the eight original time–frequency features into a five-dimensional PCA-fused feature representation consisting of PC1, PC2, PC3, fused PC4–PC7, and PC8. The fused features are classified using a radial basis function (RBF)-support vector machine (SVM) model. The proposed method achieves 98.68% test accuracy, 0.9869 Macro-F1, and 0.9931 Macro-AUC. A classifier comparison and feature-level latency analysis are also provided to clarify the accuracy–cost tradeoff and deployment feasibility. The results indicate that the proposed method can provide an interpretable and lightweight solution for electric bicycle controllers, battery management systems (BMSs), and onboard safety-monitoring applications. Full article

Vermicomposting is an eco-friendly biotechnology for organic waste valorization. As the primary product of earthworm biotransformation, vermicompost is a high-value bio-organic fertilizer abundant in diverse biologically active components. To date, most studies have focused on quality variation during the earthworm transformation process, while research on quality variations in the resulting vermicompost fertilizer during long-term storage remains scarce. To explore the shelf-life of vermicompost fertilizer and its key influencing indicators, this study investigated the changes in quality indicators in sealed-packaged vermicompost over a 180-day period using two typical vermicompost, namely cattle manure vermicompost (CM) and straw-amended cattle manure vermicompost (CMS). The temporal dynamics of physicochemical properties, nutrient contents, humification indices, enzyme activities, and microbial communities were monitored. The vermicompost quality was evaluated, and core quality drivers were identified using an integrated principal component analysis-partial least squares (PCA-PLS) approach. The results indicated that moisture content (MC), total organic carbon (TOC), and total nitrogen (TN) declined progressively, whereas available phosphorus (AP) and available potassium (AK) peaked at day 150 and day 120, respectively, and the humification rate (HR) increased by 2.6–4.0-fold. Bacterial diversity and relative abundance slightly decreased, accompanied by taxonomic differentiation, whereas fungal communities maintained stable diversity. Most enzyme activities, including urease, phosphatase, catalase, and dehydrogenase, reached their maxima at day 120. Comprehensive quality scores peaked at day 150, with a marked decline observed by day 180. The recommended shelf-life of vermicompost fertilizer is 150 days. The key quality determinants include TN, electrical conductivity (EC), pH, actinomycete abundance, TOC, TP, bacterial abundance, AP, AK, and HR. These findings provide theoretical support and references for the storage management and quality control of commercial vermicompost products in practice. Full article

High-entropy alloys (HEAs) are a transformative class of materials with remarkable structural and functional properties. Solid-state processing techniques, such as high-energy ball milling, are being increasingly used for their production. In these processes, the use of a process control agent (PCA) seems to be essential to prevent excessive cold welding and agglomeration; however, the influence of different PCAs on alloy formation remains insufficiently understood. This study systematically examined the effects of the PCA type, milling configuration, and elemental substitution on HEAs mechanosynthesis. A non-equiatomic alloy, Al₁₀Cr₁₂Fe₃₅Mn₂₃Ni₂₀ (selected for its known single-phase Face Center Cubic (FCC) behavior), was used to explore the PCA and mill-type effects. The alloy was synthesized in a planetary mill (Fritsch Pulverisette 7) and a vibratory mill (SPEX 8000M) using diverse PCAs, including liquid (methanol, ethanol, isopropyl, and n-heptane) and solid (stearic acid and sodium chloride) agents. In addition, lightweight equiatomic alloys MgAlTiNi(Co,Cr,Fe) were used to explore the influence of different PCAs and the effect of elemental substitution under similar PCA conditions as those used with the equiatomic alloy. The products were characterized using X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, and differential thermal analysis techniques. The results highlighted that the PCA selection, milling configuration, and alloy chemistry influenced the phase evolution, particle size distribution, and thermal behavior. The results provide insights into the mechanosynthesis of selected high-entropy alloys produced under different PCA and milling conditions. Full article

For hydrological parameterization in high-Andean catchments, it is necessary to understand whether near-surface hydro-structural soil properties can provide a surrogate signal of particle-size composition when direct texture information is sparse. This study evaluated the extent to which sand, silt, and clay fractions can be approximated from organic matter ($\mathrm{OM}$), bulk density (${\rho }_b$), and saturated hydraulic conductivity ($K_{\mathrm{sat}}$) in the Zamora Huayco (ZH) and Irquis catchments, southern Ecuador. A harmonized dataset ($n=44$) was analyzed through exploratory statistics, compositional assessment, correlation analysis, PCA, fraction-wise regression, $\mathrm{ILR}$-based modeling, AIC/BIC term reduction, sensitivity analysis excluding $\mathrm{OM}$, nested $\mathrm{LOOCV}$, and bootstrap-based uncertainty intervals. Among LULC classes, samples classified as paramo occupied a distinct high-Andean hydro-edaphic domain, characterized by a differentiated relationship between soil physical properties and hydrological behavior. PCA showed that the dominant covariance structure involved $\mathrm{OM}$, ${\rho }_b$, $K_{\mathrm{sat}}$, and the redistribution between sand and silt. The BIC-reduced $\mathrm{ILR}$ model provided the most balanced formulation, with positive nested $\mathrm{LOOCV}$ performance for sand, silt, and clay ($R_{\mathrm{LOOCV}}^2=0.147$, $0.704$, and $0.124$, respectively) and exact $100\%$ compositional closure after inverse transformation. Silt was the most stable predicted fraction, whereas sand and clay retained larger residual uncertainty, stronger tail departures, and partial compression of the observed variability. The proposed equations provide local hydro-pedotransfer support, although their predictive signal remains dependent on further refinement, uncertainty assessment, and external validation before regional application. Full article

Reliable diagnosis of incipient transformer faults is essential for preventing catastrophic failures and enabling predictive asset management in power systems. Although dissolved gas analysis (DGA) is the most established diagnostic tool for assessing transformer internal condition, fault discrimination remains difficult when gas features are highly correlated, redundant, and only partially separable across fault classes. This study presents a PCA-enhanced artificial neural network (ANN) framework for multiclass transformer fault diagnosis using DGA data. The method is developed on 595 samples classified into six IEC 60599 fault categories and uses a 15-feature representation comprising raw gas concentrations, total hydrocarbon content, and engineered gas-ratio descriptors. To identify an evidence-based diagnostic representation, principal component analysis (PCA) was evaluated across all dimensionalities from k = 1 to 15 before ANN training. The proposed model was benchmarked against alternative feature sets and conventional classifiers, including Gaussian Naïve Bayes, k-nearest neighbours, support vector machines, and ANN without PCA. The best-performing configuration was obtained at k = 13, yielding a test accuracy of 68.1%, compared with 63.9% for ANN without PCA, 56.3% for raw-gas-only ANN, and 33.6% for the IEC three-ratio feature configuration. In addition to improving diagnostic performance, the PCA stage revealed interpretable component structures associated with dominant gas and ratio patterns underlying fault separation. The results indicate that PCA-based feature extraction improves ANN generalization by reducing redundancy and multicollinearity in DGA-derived variables, and provides a practical, lightweight, and interpretable framework for transformer fault diagnosis. Full article

Partial discharge (PD) is a key indicator of insulation degradation in high-voltage transformers and can provide early warning of incipient failure. Although artificial neural networks (ANNs) have been applied to PD classification, their performance may be affected by redundant features and overfitting when using expanded feature spaces. This study proposes a PD severity classification framework that combines physics-informed feature engineering, principal component analysis (PCA), and a multilayer perceptron (MLP) neural network. PD measurements were acquired from a physical transformer using the IEC 60270 electrical measurement method, yielding 294 samples labelled into four severity classes: normal, low, medium, and high PD. Two measured variables, namely PD magnitude and applied voltage, were expanded into a 10-dimensional feature space using energy-based, ratio-based, logarithmic, and normalized features. PCA was then used to reduce the feature space, and the retained principal components were used as inputs to the classifier. The results show that the first two principal components captured more than 90% of the total variance and enabled the MLP to achieve 98.3% test accuracy, matching the performance obtained using all 10 engineered features and improving on classification based on the raw measurements alone (91.5%). The proposed PCA-ANN model also achieved perfect precision and recall for the medium- and high-severity classes on the test set, and outperformed K-nearest neighbours, support vector machine, and Gaussian Naïve Bayes models in 5-fold cross-validation. These findings indicate that PCA can reduce feature dimensionality without loss of diagnostic performance, providing an efficient approach for transformer PD severity classification. Full article

This paper applies the comparative PCA-ANN vs. Autoencoder-ANN framework to transformer partial discharge (PD) severity classification, using a 294-sample dataset spanning four severity classes: Normal, Low PD, Medium PD, and High PD. Two raw measurements of discharge magnitude (pC) and applied voltage (kV) are expanded into a 15-dimensional physics-informed feature space. Both linear (PCA) and nonlinear (bottleneck Autoencoder) feature extraction are evaluated exhaustively across all latent dimensions k = 1–15, feeding an identical ANN classifier. PCA + ANN achieves perfect test accuracy of 100.0% at k = 9, while Autoencoder + ANN achieves 98.3% at k = 8. PCA + ANN demonstrates superior performance on this dataset, attributed to the low intrinsic dimensionality of the two-measurement PD feature space and the highly separable nature of PD severity classes in the engineered ratio feature space. The Autoencoder provides a more compact latent representation but introduces classification errors for the Normal class due to its extreme under-representation. Cross-validation confirms PCA + ANN stability (97.4 ± 0.9% vs. 97.0 ± 1.0%). These results, alongside the companion DGA study, provide the complete baseline for comparing linear and nonlinear feature extraction across two transformer diagnostic modalities. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

Fish phenotyping plays an important role in growth evaluation, selective breeding, and precision aquaculture. Conventional phenotypic measurement methods are labor-intensive, time-consuming, and susceptible to observer variability. To improve measurement efficiency and reproducibility, this study proposes an automated fish phenotyping framework based on Transformer-enhanced instance segmentation. Specifically, a Mask2Former decoder was integrated into the Mask R-CNN architecture to improve boundary delineation and segmentation quality. Based on segmentation outputs, phenotypic parameters, including body length, body height, and projected area, were automatically extracted using PCA-assisted orientation estimation and geometric measurement. In addition, a standardized anatomical landmark annotation framework consisting of 12 reference points was introduced to support reproducible phenotypic description and future extensible morphometric analysis. Body weight was further estimated using polynomial regression based on extracted morphological traits. Experiments were conducted using images from three crucian carp varieties under controlled imaging conditions. The proposed framework achieved 92.7% mAP and 89.4% Boundary IoU, improving segmentation performance over the baseline model. Automated measurement yielded average relative errors of 2.16% for body length and 3.85% for body height, while weight prediction achieved an R² of 0.9479 and a mean relative error of 7.31%. These results demonstrate that Transformer-enhanced segmentation can support accurate and efficient automated phenotyping under standardized conditions and provide a foundation for future deployment in more complex aquaculture environments. Full article

Phenolic compounds in Muscat grape juice contribute to antioxidant capacity, functional properties, and sensory quality; however, conventional enzymatic maceration is often limited in efficiency and typically requires elevated temperatures. This study systematically compared pectinase-assisted heat maceration (P45-HM), low-temperature pectinase maceration (P-CM), and low-temperature maceration mediated by the psychrotolerant yeast Metschnikowia pulcherrima (Mp-CM) in Muscat grape juice. Mp-CM significantly enhanced the extraction and transformation of phenolic compounds, with total phenolic and flavonoid contents increasing by 8.01% and 13.14%, respectively, compared with P-CM, and by 27.06% and 55.28%, respectively, compared with P45-HM. Moreover, Mp-CM exhibited higher antioxidant activities, as determined by DPPH, ABTS, and FRAP assays, as well as greater sodium glycocholate-binding capacity than P-CM (p < 0.05). Correlation analysis revealed strong positive correlations between phenolic composition and biological activities. Volatile compounds were analyzed by HS-SPME-GC-MS combined with principal component analysis (PCA), demonstrating distinct aroma profiles. Mp-CM was enriched in terpenes (14.63% higher than P-CM), whereas P-CM was dominated by esters, suggesting that M. pulcherrima possesses a distinct biotransformation capacity that modulates volatile compounds potentially contributing to the characteristic Muscat aroma. These findings indicate that Mp-assisted cold maceration represents an efficient and promising biological maceration strategy for enhancing the quality of grape juice. Full article

Accurate fault diagnosis of power transformers using Dissolved Gas Analysis (DGA) depends on effective feature extraction to reduce redundancy and improve classification performance. This study compares linear and nonlinear feature extraction methods viz. Principal Component Analysis (PCA) and bottleneck Autoencoders (AE) to determine whether nonlinear representations provide diagnostic advantages for transformer fault classification. A dataset of 595 IEC 60599-labeled DGA samples covering six fault classes (PD, D1, D2, T1, T2, T3) was used. A 15-dimensional feature space was constructed from gas concentrations, total hydrocarbon content, and IEC-aligned gas ratios. PCA and AE were applied for dimensionality reduction across latent dimensions (k = 1–15), followed by an identical Artificial Neural Network (ANN) classifier. Performance was evaluated using test accuracy, cross-validation stability, and per-class F1-scores. The PCA+ANN model achieved a maximum accuracy of 68.9% at k = 11, outperforming AE+ANN, which achieved 66.4% at k = 4. PCA also demonstrated greater cross-validation stability (62 ± 3.5%) compared to AE (62 ± 6.6%). However, AE improved F1-scores for discharge faults (D1 and D2) by enhancing nonlinear separation of overlapping samples. PCA provides superior overall accuracy and stability for transformer fault diagnosis, while AE offers targeted advantages in distinguishing discharge-related faults. These findings establish a consistent benchmark for future studies and highlight the complementary roles of linear and nonlinear feature extraction in DGA-based diagnostic systems. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Background/Objectives: Pediatric sepsis is increasingly recognized as a syndrome involving immune–vascular dysregulation. However, most pediatric biomarker studies focus on individual molecules rather than coordinated patterns of leukocyte–endothelial activation. This study aimed to evaluate whether children diagnosed with sepsis within 48 h of admission showed a coordinated soluble adhesion-molecule activation profile measured at enrollment. Methods: This prospective cohort study included 144 children aged 1–60 months with suspected infection enrolled at Dong Nai Children’s Hospital, Vietnam, from May 2021 to October 2022. Blood samples were collected at enrollment. Sepsis was classified according to the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria within 48 h of admission. Twelve soluble adhesion molecules were measured using a multiplex immunoassay. A composite adhesion activation score was derived by log2 transformation, z-score standardization, and averaging across the 12 markers. Principal component analysis (PCA) was used as an exploratory method to summarize the shared variation across the adhesion-molecule panel. C-reactive protein (CRP) was included as a routinely available inflammatory comparator. Results: Among 144 children, 32 (22.2%) were diagnosed with sepsis within 48 h of admission. Individual marker discrimination was strongest for L-selectin (area under the receiver operating characteristic curve [AUC] 0.883), followed by soluble vascular cell adhesion molecule-1 (sVCAM-1; AUC 0.855), intercellular adhesion molecule-3 (ICAM-3; AUC 0.838), P-selectin glycoprotein ligand-1 (PSGL-1; AUC 0.836), E-selectin (AUC 0.819), and intercellular adhesion molecule-2 (ICAM-2; AUC 0.819). CRP also differed between children with and without sepsis but had a lower AUC than the leading adhesion molecules in descriptive ROC analyses. The composite adhesion activation score was strongly associated with sepsis (odds ratio 7.95 per 1-standard deviation increase; 95% confidence interval 3.44–18.40; p < 0.001) and showed good discrimination (AUC 0.855; 95% confidence interval 0.776–0.931). The first principal component explained 70.0% of biomarker variance, consistent with coordinated elevation of correlated adhesion molecules. Conclusions: In this prospective Vietnamese pediatric cohort, children diagnosed with sepsis within 48 h of admission showed coordinated elevation of soluble adhesion molecules measured at enrollment. These findings support the biological relevance of leukocyte–endothelial activation in pediatric sepsis. However, the adhesion-molecule activation profile should be considered exploratory and hypothesis-generating, requiring external validation and further evaluation against simplified, clinically feasible biomarker approaches. Full article