Search Results (753)

Problems with power transformers reduce grid reliability and can lead to large financial losses. Traditional Dissolved Gas Analysis (DGA) methods, such as the Key Gas Method, Ratios, and Duval Triangle, often give unclear results when faults are complex or occur together. This study introduces a Statistically Guided Decision Tree (SGDT) framework, a combined approach that uses statistical DGA analysis and decision tree learning to identify transformer faults. This approach creates rule-based fault categories using advanced statistical analysis of real DGA data and tests how well these categories work with a Decision Tree model. Advanced statistical techniques such as dispersion and association metrics, confidence intervals, and distribution characteristics were used on a wide range of DGA records collected from a 275 kV transformer to define threshold values. Thereafter, fault classification rules were developed, and finally, a decision tree algorithm was developed to evaluate whether the gas concentration-based rules for fault labelling aligned with real data behaviour. The classification accuracy of 0.993 was achieved, indicating a high rate of correctly identified fault types. The F1-score, representing the harmonic mean of precision and recall, was 0.980, confirming both high precision and recall. Specifically, the recall was 0.980, meaning that 98% of real fault cases were correctly found, while the precision was 0.981, showing that 98.1% of predicted fault cases were correct. The Area Under the Curve (AUC) was 0.987, showing the model could clearly tell the difference between fault and non-fault cases. This work demonstrates the effectiveness of the current proposed SGDT framework, and this will help utilities that want to digitise their transformer maintenance and diagnostics for better decision-making. Full article

Background/Objectives: Nonsurgical treatment for pancreatolithiasis is widely performed. However, treatment success remains difficult to predict before treatment initiation, complicating the selection of an appropriate treatment strategy. This study aimed to predict pancreatic stone clearance after nonsurgical treatment for pancreatolithiasis associated with chronic pancreatitis by integrating pretreatment computed tomography (CT) images and clinical information using deep learning and machine learning models. Methods: Of 195 patients with pancreatolithiasis associated with chronic pancreatitis who underwent nonsurgical treatment, including extracorporeal shock wave lithotripsy, at our institution between 1992 and 2024, only 91 (47%) had extractable pretreatment noncontrast abdominal CT images and were included in the AI analysis. Multiple deep learning models (VGG16/19, InceptionV3, ResNet50, DenseNet121/169/201, Vision Transformer, and Swin Transformer) were trained using CT images, and their predictive performance was compared. Imaging-derived and clinical predictors selected using only the training data in each patient-level cross-validation fold were combined and used as inputs for conventional machine learning models, including random forest, support vector machine, naïve Bayes, neural network, and gradient boosting. Results: Successful pancreatic stone clearance was achieved in 54 of 91 patients (59%). Compared with the 104 patients without extractable CT data, the analyzed cohort had a higher proportion of asymptomatic pancreatolithiasis (43% vs. 15%) and a markedly lower pancreatic stone clearance rate (59% vs. 88%), indicating potential selection bias. Asymptomatic pancreatolithiasis and a pancreatic stone size of ≥15 mm, defined using a data-derived exploratory cutoff, were significantly associated with unsuccessful stone clearance. Among the deep learning models, ResNet50 achieved the highest performance (area under the receiver operating characteristic curve [AUC], 0.718), followed by Vision Transformer (Large model, 16 × 16 patches) (AUC, 0.700). When image-derived features were combined with clinical features, the neural network achieved the best performance, with a mean AUC of 0.757, a median sensitivity of 0.568, a median specificity of 0.704, and a median accuracy of 0.659. Conclusions: A neural network integrating CT-derived image features with clinical information showed moderate internal predictive performance for pancreatic stone clearance. Because this was a single-center retrospective study without external validation, the present model should be regarded as a preliminary predictive model requiring validation in independent cohorts before clinical application. Full article

Szechuan pepper pungency is traditionally assessed by subjective sensory panels or laboratory instruments, hindering on-site detection. Perceived numbing sensation does not always correspond directly to alkylamide content because multiple electroactive constituents, including polyphenols and sanshools, may contribute to the overall sensory response. Chromatographic methods quantify individual compounds but may not fully reflect integrated human pungency perception. Electrochemical detection bypasses separation, as the voltammetric response integrates oxidative signals from multiple electroactive species. We developed a portable electrochemical system with a custom programmable-gain potentiostat, three-electrode detector, and STM32-controlled software for differential pulse voltammetry (DPV) measurement. Coupled with machine learning, it assessed Zanthoxylum bungeanum (red peppers) and Zanthoxylum schinifolium (green peppers). Fifteen replicate scans from each of 22 origins yielded 330 DPV curves calibrated against general Labeled Magnitude Scale (gLMS) scores from a trained panel. An artificial neural network (ANN) achieved R² = 0.937 for red peppers, while principal component analysis–support vector regression (PCA–SVR) achieved R² = 0.860 for green peppers. Competitive adaptive reweighted sampling (CARS) identified three characteristic potential intervals for each type: 0.17–0.21, 0.57–0.62, and 0.69–0.80 V for red peppers, and 0.24–0.27, 0.56–0.68, and 0.69–0.77 V for green peppers. These intervals indicate that pungency-related electrochemical information is distributed across multiple potential regions. The system shows potential for rapid and objective quality assessment of Szechuan pepper. Full article

This study compared net photosynthesis (A), water-use efficiency (WUE), intrinsic water-use efficiency (iWUE), and A–${\mathrm{C}}_i$ curve-derived photosynthetic parameters (maximum carboxylation rate, $V_{\mathrm{cmax}}$; maximum electron transport rate, $J_{\max }$) across nine mangrove and semi-mangrove species at a tropical coastal site in Bali, Indonesia. Gas exchange measurements were conducted using portable photosynthesis systems (LI-6400 and LI-6800), and A–${\mathrm{C}}_i$ curves were fitted to the Farquhar–von Caemmerer–Berry (FvCB) model. Sonneratia alba Sm. exhibited the highest A (15.29 ± $2.39$ μmol ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$), suggesting comparatively high photosynthetic performance, while Hibiscus tiliaceus L. and Pongamia pinnata (L.) Pierre showed the highest iWUE values (88–$97$ μmol ${\mathrm{mol}}^{-1}$), indicating relatively efficient carbon gain per unit stomatal conductance. Significant overall interspecific variation was detected in $V_{\mathrm{cmax}}$ and $J_{\max }$ ($p=0.003$ and $p=0.016$, respectively). The observed interspecific variation in A, iWUE, $V_{\mathrm{cmax}}$, and $J_{\max }$ suggests differences in photosynthetic characteristics and leaf-level water-use efficiency among species. These ecophysiological baseline data may contribute to species selection for mangrove restoration and to understanding physiological responses to environmental change in tropical coastal forests. Full article

Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper presents an unsupervised multi-sensor framework that requires no fault labels, integrating physics-informed dual health indices, HI-P (sludge load) and HI-V (mechanical stress), with a Gaussian Mixture Model anomaly detector and a physics residual attribution module. Governing equations motivate the use of these indices from five sensors: inlet and outlet flow meters (100 Hz), an air pressure transducer (100 Hz), and inlet and outlet accelerometers (1652 Hz). Trained on one healthy baseline day (86,218 one-second windows), the Gaussian Mixture Model achieves 100% day-level classification performance on the evaluated dataset (F1 = 1.00) across 455,201 test windows from nine operating days, with window-level receiver operating characteristic area under the curve (ROC-AUC) = 0.8580 and precision–recall AUC (PR-AUC) = 0.9082. Residual attribution analytically confirms that pressure residuals drive Episode 1 (HI-P peak 3.63 times baseline, Cohen’s d = 1.70) and vibration residuals drive Episode 2 (HI-V peak 5.44 times the baseline, d = 4.10), providing empirical support for the proposed physics-informed formulation without requiring fault labels. Comparisons with four unsupervised benchmarks confirm that this is the only approach that simultaneously enables label-free operation, physics-driven features, exact attribution, real-world deployment, and perfect day-level F1. Full article

The Ti-6Al-4V alloy is widely used in aerospace and deep-sea applications due to its exceptional strength and corrosion resistance. However, its application is often constrained by high deformation resistance and a narrow hot-working temperature window, primarily attributed to its heat and mass transfer characteristics. To address these limitations, a novel Ti-6Al-1.3V-0.9Fe alloy was designed with an equivalent molybdenum content. In this study, Gleeble thermal simulation tests were conducted to investigate the impact of Fe on the hot deformation behavior under various conditions and to identify the optimal processing window for this alloy. The effects of deformation temperature and strain rate on the flow stress curves and peak stress were systematically analyzed, along with the role of Fe in microstructural evolution during hot deformation. The results demonstrate that the addition of Fe significantly refines the grain size of the Ti-6Al-1.3V-0.9Fe alloy. As expected, the flow stress decreases with increasing deformation temperature and increases at higher strain rates. Under high-temperature and low-strain-rate conditions, the alloy exhibits steady-state flow behavior, indicating improved hot workability. Based on the constitutive modeling, the apparent activation energy (Q) for hot deformation was calculated to be 503.81 kJ/mol. Finally, the optimal hot-working parameters for the Ti-6Al-1.3V-0.9Fe alloy were identified as a temperature range of 760 °C to 860 °C and a strain rate between 0.01 and 0.16 s⁻¹. Full article

When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or missed detections. Meanwhile, fault samples are usually limited in practical applications. To address these problems, this study proposes an anomaly detection method based on multi-feature relational modeling and conditional-memory augmentation. The method performs the cycle-wise alignment of multi-point vibration signals according to the firing phase of each cylinder. It integrates local waveform morphology, impact energy, and energy-centroid information in the non-uniform angular domain to construct a raw–relative dual relational representation. It further uses speed conditions to modulate latent features and employs a sparse normal memory to constrain reconstruction sources, enabling the model to learn normal relational patterns under different operating conditions using only normal samples. Tests involving misfire, intake-valve clearance anomaly, and exhaust-valve clearance anomaly were conducted on a TBD234V12 diesel-engine test bench. The proposed method achieved an accuracy, true positive rate (TPR), F1-score, and area under the receiver operating characteristic curve (AUROC) of 97.44%, 98.98%, 98.30%, and 98.88%, respectively, with a false-positive rate (FPR) of 7.21% under the sample-level alarm definition. The results show that the method reduces the interference of operating-condition-induced normal-pattern drift with anomaly determination and improves the accuracy of fault warning within the range of the operating conditions covered in this study. Full article

Bilateral vertical ground reaction force (vGRF) plates are the dominant sensor for field-deployed neuromuscular monitoring in athletes, with the countermovement jump (CMJ) being the dominant test. Detecting movement onset on the vGRF trace is a single decision that propagates through impulse–momentum integration to bias jump height. Existing onset method comparisons used fewer than 100 athletes, and bilateral weight rocking, a form of pre-jump postural sway during quiet standing, has not been characterized at scale as a method-specific source of disagreement. We analyzed 32,952 NCAA Division I CMJ trials from 579 athletes across 15 teams recorded on bilateral vGRF plates. Five hypotheses were pre-specified, and a per-trial decision rule was developed under leave-one-athlete-out cross-validation. Pooled, 45.89% of trials showed false-early firing on at least one of five non-first-derivative onset methods, relative to a rate-of-change reference, at a 30 ms threshold. On the rocking-amplified subset, a bidirectional body weight band method overestimated jump height by 0.943 cm versus the reference. The decision rule reached an area under the receiver operating characteristic curve of 0.71 with near-perfect calibration. It is a per-trial screening flag, not a deterministic classifier, and deployment should anchor on balanced-accuracy or high-precision operating points, holding the onset method constant across an athlete’s monitoring timeline. Full article

Hepatitis B virus (HBV) infection remains a major concern among people living with HIV (PLWH), particularly in high-endemic settings such as Vietnam. The study evaluated the serological and molecular characteristics of HBV among PLWH receiving antiretroviral therapy (ART) in southern Vietnam. A cross-sectional study was conducted among 316 HIV-infected patients receiving ART. Serological markers (HBsAg, anti-HBs IgG, and anti-HBc IgG) and HBV DNA were assessed using highly sensitive assays, and the Pre-S1/Pre-S2/S regions were sequenced for genotype and mutation analysis. Associations were evaluated using appropriate statistical methods. HBV DNA was detected in 32.6% of the patients, whereas HBsAg was present in only 16.1%. The most common serological profile was susceptibility (36.4%), followed by occult HBV infection (OBI) (17.4%), including 6.6% with completely seronegative OBI, and chronic HBV (CHB) (15.8%). Males and older individuals showed a significantly higher risk of CHB (adjusted odds ratio [aOR] = 2.58 and aOR = 1.87, respectively). Genotype B predominated (78.4%) overall, but genotype C was significantly more frequent in OBI than in CHB patients (31.5% vs. 8.3%, p = 0.008). Moreover, the burden of surface S gene escape mutations was significantly higher in the OBI group (p = 0.023). The LASSO model showed a moderate discriminatory ability for predicting OBI status (Area Under the Receiver Operating Characteristic [ROC] Curve [AUC] = 0.76). Lamivudine-associated resistance (rtM204I/V) was the most common RT mutation detected in the overlapping RT/S region. HIV-infected individuals in southern Vietnam face a burden of both CHB and OBI, including a high proportion of seronegative OBI. The presence of seronegative OBI further supports the risk of underdiagnosis when relying only on standard serological markers, and reflects the need for integrated strategies combining highly sensitive serological assays with molecular diagnostics to improve HBV detection among high-risk populations. Full article

This study investigates flood behavior in the Wadi Ibrahim watershed by evaluating the sensitivity of flood estimates to different Peak Rate Factor (PRF) values, with a focus on their influence on peak discharge (Q_p), time to peak (t_p), volume (V) and inundation depth. Flood simulations were conducted using the Ari-Zo model, an empirical rainfall–runoff approach designed for arid regions, combined with unit hydrograph derivation across multiple PRF scenarios (Low, Medium, High, and NRCS) for 50-, 100-, and 200-year return periods. Envelope curves were constructed as validation to characterize the range of potential flooding. Comparative analysis indicates that the widely used NRCS model, including in Saudi Arabia, consistently underestimates Q_p and overestimates t_p relative to the Ari-Zo model. The Ari-Zo model produces sharper, faster-rising hydrographs with shorter durations, reflecting the rapid flash flood response characteristics of arid catchments. The Q_p in the High scenario is up to 71% higher than in the NRCS scenario. Hydraulic modeling shows that the different water depths of the Ari-Zo model relative to the NRCS scenario are 0.8 m (Low-NRCS), 1.8 m (Medium-NRCS), and 3.3 m (High-NRCS) for the 50-year return period, with the High scenario inundating nearly the entire valley bottom. The study highlights that PRF selection and model choice significantly influence flood predictions, emphasizing the importance of using arid-zone-adapted models to ensure reliable flood risk assessment and resilient infrastructure planning. Full article

Accurate extraction of single-diode model (SDM) parameters is essential for photovoltaic performance analysis, especially when only datasheet values or graphical I-V characteristics are available. This study presents a graphical–deterministic parameter extraction framework that combines calibrated curve digitization, local differential analysis, uncertainty-aware slope estimation, and analytical SDM closure. Unlike conventional datasheet-based analytical methods that operate directly from tabulated characteristic points, the proposed approach explicitly incorporates the graphical-to-numerical conversion stage and evaluates its impact on the estimation of the resistive parameters. The shunt and series resistances are obtained as effective local slope estimates near short-circuit and open-circuit conditions, respectively, while the photocurrent, saturation current, and ideality factor are determined from characteristic operating point equations. The ideality factor is solved through a deterministic scalar root procedure within the physically admissible interval $1\le \mathrm{n}\le 2$. The method was evaluated using four photovoltaic devices, including laboratory-scale cells and commercial modules. Normalized reconstruction errors of 0.82% and 0.88% were obtained for the RTC-France cell and the KC200GT module, respectively. The INAOE laboratory cell and the SP450M half-cut module showed higher sensitivity to graphical slope extraction and energetic closure. For the SP450M module, the use of an equivalent series cell number ${\mathrm{N}}_{\mathrm{S},\mathrm{e}\mathrm{q}}$ = 72 improved agreement with the digitized graphical I–V curve, although the reconstructed maximum power remained below the nominal datasheet value, revealing a graphical/datasheet consistency issue. These results show that the proposed framework is a transparent and reproducible alternative for SDM identification from graphical sources, while also defining its sensitivity limits when applied to low-resolution curves or half-cut high-power modules with complex equivalent electrical configurations. Full article

Background/Objectives: Cervical pedicle screw (CPS) placement provides superior biomechanical stability but remains technically demanding and associated with a risk of screw malposition. While recent advances in imaging and navigation have improved placement accuracy, reliable prediction of malposition remains challenging. The aim of this study was to evaluate whether machine learning (ML) models can predict CPS malposition using structured clinical and anatomical features. Methods: We performed a retrospective analysis of 862 pedicle screws from 168 posterior cervical spine surgeries conducted at our institution between 2018 and 2025. Clinical, procedural, and anatomical variables, including age, sex, body size parameters, surgical indication, vertebral level, pedicle angle, and pedicle width, were evaluated. Pedicle morphology was partially derived from CT-based automated segmentation using TotalSegmentator (v2.13.0), while selected anatomical parameters were manually measured. Supervised ML models, including Random Forest, Balanced Random Forest, XGBoost (v3.2.0), Support Vector Machine, and K-Nearest Neighbor, were trained and compared using Python and scikit-learn to predict inaccurate screw placement. Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (ROC AUC), F1-score, precision, and recall. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results: The dataset showed a clinically representative class distribution, with 91.1% of screws classified as acceptable and 8.9% as inaccurate. Across all models, predictive performance was moderate and consistent. Balanced Random Forest achieved the highest discriminative performance (ROC AUC 0.69) and provided the most balanced classification profile, while other models demonstrated comparable overall performance with varying sensitivity to the minority class. SHAP analysis identified anatomical and procedural variables, including pedicle width and angle, as relevant contributors to model output. Feature contributions were distributed across variables, with substantial overlap between outcome groups. Conclusions: ML-based prediction of CPS malposition using clinical and anatomical features demonstrates consistent and interpretable performance. The results highlight that predictive performance is primarily influenced by dataset characteristics, including class distribution and feature overlap, rather than model selection alone. This study provides an important baseline for ML-based CPS prediction and supports future research integrating larger datasets and more detailed anatomical representations to enhance predictive accuracy. Full article

Accurate state-of-charge (SOC) estimation is essential for reliable operation of vanadium redox flow batteries (VRFBs), yet many model-based methods treat the open-circuit-voltage (OCV)-SOC relationship as a fixed calibration curve. This study experimentally investigates condition-dependent SOC-OCV behavior using a laboratory-scale VRFB equipped with a bypass OCV cell. The bypass OCV method was validated against an intermittent discharge–rest method, with OCV differences below 5 mV. SOC-OCV characteristics were then examined under different electrolyte flow rates, cycling histories, electrolyte/component refreshing conditions, and a dynamic stress test profile. The results show that the SOC-OCV curve varies with cycling history and flow rate, while electrolyte/component refreshing and dynamic operation further modify the measured OCV response. Fixed-curve-based SOC inversion confirms that calibration mismatch can introduce substantial SOC estimation errors, with a case-specific maximum error of 11.36% observed when the initial post-preconditioning constant-current curve was applied to the cell after 50 cycles under the tested 96 mL min⁻¹ DST condition. These findings highlight the need for adaptive OCV correction and condition-dependent SOC-OCV mapping in practical VRFB SOC estimation. Full article

We report on results referring to the growth and characterization of Sn-oxide-semiconductor thin films (SnO₂, SnO and intermediate Sn₃O₄) on silicon substrates forming Type II heterojunction band alignment. The samples were produced by a two-step procedure: (a) growth of a metallic Sn layer by RF magnetron sputtering deposition followed by (b) post-growth treatment of the Sn films with oxygen plasma etching for the formation of the oxides in various time steps. Various annealing steps were considered. The structural and chemical properties of the prepared thin films were determined by means of X-Ray Diffraction (XRD) and X-Ray Photoelectron Spectroscopy (XPS). The results demonstrated the presence of SnO₂ and SnO in a tetragonal structure and the intermediate Sn₃O₄ in a triclinic structure. The electric properties of thin films were investigated with four-probe I–V characteristics under various conditions. The evaluation of their photo-sensing properties was performed by means of photocurrent J–t curves using a solar simulator. We found that the sample with equal concentrations of SnO₂ and SnO exhibited superior responsivity and detectivity values as well as a responsivity of 12.5 A/W and detectivity of 1.1 × 10¹⁰ Jones for V = 0 under yellow light illumination and an intensity of 2 mW/cm². These excellent values, in combination with the low-cost manufacturing, indicate that the method is promising for future applications. Full article

Vegetation-impervious surface-soil (V-I-S) fractions offer a continuous sub-pixel representation of urban surface heterogeneity. In this study, the influence of urban surface characteristics represented through V-I-S fractions on hydrological processes is analyzed at decadal intervals, i.e., 2000, 2010, 2020, and the projected year 2030 for the Mula–Mutha river catchment, Maharashtra, India. Pune city, as a major urban centre in this region, is experiencing significant changes in land surface characteristics over time, which have direct implications for its hydrology. The analysis uses the Soil and Water Assessment Tool (SWAT) to model these changes and their effects on water resources. Results show that the urban area has increased from 14% (2000) to 25% (2020), with projections indicating a further rise to 34% (2030). Such transitions yielded an increase in surface runoff from 47% (2000) to 53% (2020) and projected to reach 54% (2030). Groundwater recharge has declined from 10% to 6% and is expected to fall to 4% by 2030. Model validation using discharge data at Mirawadi outlet yielded a coefficient of determination of 0.72 using land use/land cover (LULC) data and 0.79 for simulations based on runoff Curve Number (CN) derived from V-I-S fractions, indicating the improved model performance. This study presents a novel framework, which incorporates remote sensing-derived V-I-S fractions to assess the spatiotemporal impact of urban expansion on water balance components. Full article