Search Results (589)

Background: Hereditary angioedema (HAE) is a rare, potentially life-threatening disease in which delayed recognition and inappropriate medication use may result in preventable morbidity and mortality. We aimed to assess physicians’ knowledge regarding HAE-related triggers, clinical features, and management strategies across multiple medical specialties. Methods: This single-center, cross-sectional survey was conducted among 350 physicians at a tertiary training and research hospital. A structured electronic questionnaire assessed knowledge of HAE pathophysiology, diagnosis, medication-related triggers, and management. A predefined composite knowledge score (0–14) was calculated. Results: Although self-reported familiarity with HAE was high, overall disease-specific knowledge was limited. Awareness of critical medication-related triggers—including angiotensin-converting enzyme inhibitors, dipeptidyl peptidase-4 inhibitors, and estrogen-containing therapies—was low across all specialties, with no significant between-group differences. Substantial knowledge gaps were identified in the recognition of clinical features, diagnostic evaluation, and acute management. In multivariable analysis, prior clinical exposure to HAE patients was the only independent predictor of higher knowledge scores (B = 1.097, p = 0.001), whereas specialty group, gender, and years of professional experience were not independently associated with knowledge scores. However, the regression model explained only a small proportion of the variance in knowledge scores. Conclusions: Significant gaps in clinically relevant HAE knowledge persist among physicians from multiple medical specialties. Prior clinical exposure was associated with higher knowledge scores, whereas specialty group, gender, and years of professional experience were not independently associated with physician knowledge. Targeted, practice-oriented educational interventions focusing on medication-related triggers and acute management may help bridge these knowledge gaps, complement experiential learning, and ultimately enhance patient safety. Full article

With the widespread deployment of industrial systems and Internet of Things (IoT) devices, multivariate time series anomaly detection has become increasingly important for ensuring system reliability and operational safety. However, accurately detecting anomalies in complex industrial environments remains challenging because existing approaches often fail to jointly model evolving inter-variable dependencies and multi-scale temporal patterns. To address these challenges, this paper proposes WaveGraphFormer (WGF), a unified framework for multivariate time series anomaly detection. The proposed method introduces a lightweight dynamic graph learning module to capture time-varying dependencies among variables and employs discrete wavelet transform (DWT) to extract multi-scale temporal-frequency features. In addition, a graph-guided residual modulation mechanism is designed to facilitate joint spatio-temporal representation learning. Experiments conducted on five public benchmark datasets (SWaT, WADI, SMAP, SMD, and MSL) demonstrate that WGF consistently achieves competitive performance in terms of F1-score and AUC compared with several state-of-the-art baselines. Ablation studies further validate the effectiveness of each component in the proposed framework. These results highlight the potential of WGF to provide reliable anomaly detection for complex industrial monitoring systems and establish a foundation for future research on adaptive spatio-temporal-frequency modeling. Full article

Accurate voltage degradation prediction is essential for health management and lifetime extension of proton exchange membrane fuel cell (PEMFC) systems. During long-term constant-current operation, stack voltage evolves nonlinearly and is influenced by coupled variations in temperature, pressure, flow rate, and humidity, while many deep learning-based models lack physical interpretability. This study proposes an interpretable Transformer-based framework for PEMFC voltage degradation prediction under constant-current operation. The framework integrates outlier correction, interpolation, Savitzky–Golay filtering, Z-score normalization, sliding-window reconstruction, Transformer-based prediction, and feature-ablation interpretation. Using multivariate sensor measurements and historical voltage as inputs, the Transformer was compared with RNN, LSTM, and GRU baselines under identical preprocessing and evaluation conditions. The models were evaluated chronologically by continuously applying the sliding-window model over the held-out final 20% of the aging sequence. The Transformer achieved the best performance, with MAE of 8.2 × 10⁻⁴, RMSE of 1.18 × 10⁻³, MAPE of 0.0256%, and R² of 0.9961. Compared with the second-best RNN model, it reduced MAE, RMSE, and MAPE by 9.89%, 7.81%, and 9.86%, respectively. Feature ablation showed that flow- and pressure-related variables contributed 50.08% and 26.78% of the total importance, respectively. Full article

Background/Objectives: Post-infectious inflammatory response syndrome (PIIRS) is a rare complication of cryptococcal meningitis. The objective of this study was to identify predictors that can be used to identify patients at risk of PIIRS before its onset. Methods: A total of 149 patients with cryptococcal meningitis who did not develop PIIRS (controls, n = 84) and developed PIIRS (cases, n = 65) were included in the study. Clinical presentation data, treatment data, and laboratory data at admission were collected and compared between the two groups using univariate and multivariable analyses. Inflamed areas within T2 FLAIR MRI scans were manually annotated, and a total of 110 radiomic features were extracted per patient. Data were split into training, test, and validation sets, and multiple machine learning models integrating radiomic, spatial, and clinical features were developed and evaluated using area under the curve (AUC) and related performance metrics. Results: Ventriculoperitoneal shunt (odds ratio: 4.64 [1.84–12.46]; p-value = 0.001), complement component 3 (C3) (odds ratio: 5.78 [1.55–24.42]; p-value = 0.012), and lumbar puncture opening pressure (odds ratio: 1.01 [1.00–1.01]; p-value = 0.008) were independent risk factors associated with PIIRS development. All four radiomics models performed very well in predicting PIIRS, with the final model (region of interest + location + C3) achieving the best overall performance set (test set AUC: 0.948 [0.840–1.000], validation set AUC: 0.943 [0.814–1.000]). Conclusions: Radiomics-based classification models demonstrated good performance for predicting PIIRS. These findings should be considered hypothesis-generating given the small sample size and require validation in larger multicenter studies before clinical implementation. Full article

This study presents a comprehensive analysis and predictive modeling framework for the axial compressive strength (f_cc) and ultimate axial strain (ε_cu) of concrete columns confined within fiber-reinforced polymer (FRP) systems. Large databases comprising 3312 samples for f_cc and 3319 for ε_cu were compiled from the literature, encompassing a wide range of key variables, including unconfined concrete strength from 7 MPa to 204 MPa and diverse FRP confinement configurations. The datasets were subjected to extensive statistical and multivariate analyses to identify the primary factors influencing axial behavior and guide feature selection for predictive modeling. Three groups of machine learning (ML) algorithms were subsequently considered: (i) artificial neural networks (including multilayer perceptrons with one and two hidden layers), (ii) kernel-based models (Gaussian process regression and support vector regression), and (iii) tree-based ensemble models (gradient boosting machine, eXtreme gradient boosting, and light gradient boosting machine). Hyperparameters were optimized using grid search cross-validation, while feature importance analyses were performed to quantify the contribution of each input variable. Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness. Comparative analysis with the top performing regression-based formulations further highlighted the accuracy, robustness, and generalization capability of the eXtreme gradient boosting model. The findings provide a data-driven and interpretable framework for the design and prediction of FRP-confined concrete columns. Full article

Volatile fingerprints provide useful information for characterizing Astragali Radix (AR), a food–medicine homologous plant material, but differences among wild, wild-simulated, and cultivated samples remain unclear. In this study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC–MS) and headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) were integrated with multivariate analysis and interpretable machine learning to characterize volatile profiles and identify candidate discriminatory compounds in 117 AR samples from different cultivation patterns. HS-SPME-GC–MS tentatively identified 29, 34, and 45 volatile compounds in wild, wild-simulated, and cultivated samples, respectively. Esters were the predominant class in all groups, although the relative abundance of esters and the overall chemical-class composition varied among cultivation patterns. HS-GC–IMS tentatively identified 57, 50, and 55 compounds, respectively, comprising mainly low-molecular-weight aldehydes, alcohols, and ketones and thereby providing complementary volatile fingerprint information. Partial least squares discriminant analysis (PLS-DA) showed that the volatile fingerprints captured cultivation-pattern-associated differences, with the HS-GC–IMS model showing clearer group separation. Random forest, support vector machine, and CatBoost models were further constructed using the HS-SPME-GC–MS profiling results. By integrating variable importance in projection (VIP) and SHapley Additive exPlanations (SHAP) values, γ-hexalactone, methyl eugenol, methyl (9Z,11E)-octadeca-9,11-dienoate, eugenol, and ethyl linoleate were selected as candidate discriminatory compounds. Based on the HS-GC–IMS results, 1-octen-3-one, pentyl acetate, (Z)-2-penten-1-ol, 2-heptanone, and the monomeric signal of 2-ethyl-6-methylpyrazine were also identified as candidate discriminatory compounds. These compounds may be related to fatty acid-derived metabolism, aromatic secondary metabolism, and terpenoid-related processes. The integration of two complementary volatile-analysis platforms with VIP- and SHAP-based interpretation provided broader coverage of volatile features and improved the interpretability of candidate-compound screening. These findings provide an interpretable analytical workflow and candidate discriminatory compounds that may support future rapid screening, cultivation-pattern authentication, and volatile-profile-based differentiation of AR, pending independent external validation. Full article

To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value. Full article

Background: Accurate preoperative prediction of lymph node metastasis (LNM) is essential for tailoring treatment strategies in T1 colorectal cancer (CRC). Although the neutrophil-to-lymphocyte ratio (NLR), an easily acquired inflammatory biomarker, correlates with tumor progression, its incremental value when incorporated into machine learning (ML) models for predicting LNM in T1 CRC remains unclear. Therefore, this study evaluated the independent predictive value of preoperative NLR for LNM and constructed interpretative ML models to stratify LNM risk in T1 CRC. Methods: We retrospectively enrolled 533 pathologically confirmed T1 CRC patients. NLR was calculated as the neutrophil–lymphocyte count ratio. Multivariable logistic regression identified NLR’s independent correlation with LNM. Features were screened via combined least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Six ML models, including Logistic Regression (LR), Random Forest (RF), XGBoost, LightGBM, CatBoost, and Logistic Regression with Splines (LR-Spline), were developed and validated. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. Shapley additive explanations (SHAP) were applied for feature interpretability. Results: Elevated preoperative NLR was independently associated with an increased risk of LNM (adjusted OR = 2.83, 95% CI: 1.48–5.41, p = 0.002), exhibiting stability across all clinical and pathological subgroups. The optimized LightGBM outperformed other models with an area under the receiver operating characteristic curve (AUC) of 0.768, alongside favorable clinical utility. Conclusions: An elevated preoperative NLR is a robust, independent predictor of LNM in patients with T1 CRC. An optimized LightGBM model integrating NLR with routine clinicopathological indicators offers accurate risk stratification, potentially refining surgical decision-making and sparing low-risk T1 CRC patients from unnecessary radical resections. Full article

In the multivariate time-series fault diagnosis task for aviation electro-hydrostatic actuators (EHA), the overall signal morphologies of different fault categories are relatively similar, while the key discriminative differences are hidden in local segments and variations in variable coupling. Therefore, existing Transformer-based methods usually have difficulty characterizing local specificity. To address this issue, this paper proposes a Local Prototype-Global Generic Dual-branch Transformer (LPG-Former). First, to obtain local information capable of characterizing class differences, a class-specific discriminative prototype (CDP) is constructed. The CDP selects discriminative time points from the time-series samples of each class to capture key local morphological variations, and constructs local prototypes carrying class-related local differential features. To further improve the ability of the CDP to capture multivariate fault coupling relationships, a multivariate coupling-aware prototype matching strategy (MCPM) is designed. The MCPM extends univariate prototypes into multivariate local prototype blocks and jointly measures local dissimilarity, variable correlation, and trend consistency, thereby enabling prototype learning with awareness of multivariate coupling relationships. Finally, to fuse local discriminative information and global temporal information, a dual-branch Transformer is constructed. LPG-Former encodes the differential features between the CDP and the best-fit subsequence (BFS) of the input sample through a Local Prototype Transformer, and complements global generic information through a Global Generic Transformer, thereby achieving collaborative dual-branch representation. Experimental results on an eight-class EHA operating-state dataset show that LPG-Former achieves an accuracy of 98.74% and an F1-score of 98.78%, significantly outperforming classical methods such as InceptionTime and TapNet. Full article

Predictive maintenance (PdM) has proven to be a critical strategy for minimizing downtime and optimizing operational efficiency in industrial systems. In this work the authors propose a data-driven PdM framework for circulating water pumps (CWPs), combining statistical analysis with machine learning. This begins with the analysis of a multivariate time-series dataset consisting of 51 sensors monitoring motor electrical parameters, pump hydraulics, vibration, temperatures, broken states, recovery states and finally normal states. With the help of a statistical analysis, utilizing boxplots and both Pearson and Spearman correlation indices, the aim is to identify the key degradation parameters. Notably, motor phase current (Pearson r = −0.872), pump vibration (r = −0.809), and discharge pressure (r = −0.732) showed the strongest negative correlation with failure states, revealing a complete system shutdown pattern during failure conditions. Applying these results to the development of a Random Forest classification model was the next step. Due to class imbalance and label interpretation challenges, i.e., normal state, broken state and recovery state, the reformulation of the multi-class problem into a binary task focusing on “at-risk” states was executed. Therefore, this led to the critical finding of the reversal of conventional state labels, where RECOVERING states were found to correspond to inactive post-shutdown conditions requiring maintenance intervention, whereas BROKEN states exhibited characteristics more consistent with partial degradation. Lastly, the statistically selected feature model achieved a Recall of 0.76, a Precision of 0.70 and an F1-score of 0.73. Furthermore, a physics-based grouped subsystem representation combining vibration, electrical, hydraulic, thermal and rotational measurements substantially improved classification performance, achieving a Precision = 0.997, a Recall = 0.992 and an F1-score = 0.995. These results demonstrate the effectiveness of combining statistical analysis, engineering knowledge and machine learning for the predictive maintenance of industrial pumping systems. Full article

The marine capture fisheries sector of the Philippines employs approximately 2.3 million Filipinos, yet recent declines (including a 15.3% drop in Q1 2026 production relative to Q1 2025) underscore the need for forecasting systems resolved at the regional and sectoral level. Existing Philippine approaches rely on univariate classical time-series methods and seldom integrate multivariate oceanographic predictors. This study addresses three questions: (RQ1) How do nine candidate machine learning algorithms compare in forecasting regional fish production from environmental predictors? (RQ2) Which environmental predictors most strongly drive model output, as quantified by explainable AI (XAI) SHAP-based feature attribution? (RQ3) To what extent do model performance and predictor importance vary across regions? Across 32 region–sector panels spanning 2002–2025, kernel and neural network models were selected as the best-performing architecture in 26 of 32 panels (81.3%), achieving a mean composite score 12.7% higher than tree-based ensembles, a gap attributable to extrapolation along trending physical predictors. Feature attribution identified the partial pressure of CO₂ as the leading driver in both sectors, exceeding the second-ranked variable by factors of 2.5 (commercial) and 3.4 (marine municipal). Regional heterogeneity in retained predictors, winning algorithms, and SHAP attribution rankings supports region-specific forecasting as a necessary design choice. Mean absolute percentage error of 22–25% and directional accuracy of 0.62–0.66 indicate operational utility for early-warning applications, establishing a basis for evidence-driven priority-setting in Philippine fisheries governance. Full article

Dust concentration during intermittent processing of powder and granular materials is characterized by high-frequency abrupt changes, local accumulation, and complex coupling among multiple sensors. Existing forecasting models still exhibit limitations in modeling global dependencies and characterizing local trends. To address these issues, this paper proposes an iTransformer-based dust concentration forecasting model that integrates a dual-stage feed-forward network and a DLinear branch. With iTransformer as the backbone network, the proposed model captures the coupling relationships among multi-source sensing signals through variate-wise modeling. A progressive dual-stage feed-forward feature refinement mechanism is constructed to enhance the model’s representation capability for transient variations and peak fluctuations in dust concentration. In addition, a collaborative modeling framework consisting of an iTransformer main branch and a DLinear auxiliary branch is designed to jointly learn global nonlinear features and local linear trends. An adaptive gated fusion mechanism is further introduced to dynamically allocate the contribution weights of different branches according to sequential characteristics. Experiments were conducted on a public 1 Hz smoke-sensing dataset, which was used as a proxy benchmark for high-frequency multivariate PM2.5 forecasting rather than direct industrial dust data. Under the setting of a 300-step input length and a 60-step forecasting horizon, the proposed model achieves an MSE of 1.8292 × 10⁻³, an MAE of 0.0334, an RMSE of 0.0428, an MAPE of 0.0177, and an R² of 0.9744, outperforming the compared baseline models in overall performance. The results indicate that the proposed method improves overall forecasting accuracy and provides a methodological reference for sensor-driven particulate concentration forecasting and early warning, while further validation using field data from actual powder and granular material processing workshops is still required before practical deployment. Full article

Modern artificial intelligence methods are increasingly used in power systems for renewable energy generation and electricity load forecasting. However, the limited interpretability of complex machine learning and deep learning models constrains their adoption in critical energy applications where transparency and trust are essential. Explainable Artificial Intelligence (XAI) provides tools for interpreting model behavior, yet its application to multivariate time series remains associated with significant methodological challenges. This paper presents a systematic review of XAI applications in solar power, wind power, and electricity load forecasting based on 154 peer-reviewed journal articles published between 2019 and 2026, identified through searches in Scopus, IEEE Xplore, ScienceDirect, and MDPI, following the PRISMA 2020 methodology. The review covers widely used forecasting architectures, including LSTMs, Transformers, and tree-based ensembles, as well as XAI methods. The analysis identifies a fundamental limitation of conventional XAI approaches for multivariate time series, referred to as the curse of dimensionality in XAI-based interpretation of time series, in which each time step is treated as an independent feature, resulting in explanations that are difficult to interpret in practice. To address this challenge, eight categories of XAI adaptations for time series forecasting are systematized. A classification of knowledge extraction mechanisms is proposed, including feature-level, temporal, regime-based, causal, diagnostic, model-level, and decision-support knowledge. The results demonstrate a gradual transition from explainability toward knowledge extraction, where XAI serves not only to explain individual forecasts but also to generate actionable knowledge about data, models, and energy processes. The review is limited to peer-reviewed English-language journal articles published between 2019 and 2026. The findings suggest that Knowledge Extraction represents a key mechanism for building trust in intelligent energy forecasting systems. Full article

The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for environmental monitoring and water-pollution profiling using multivariate records from 13 stations between 2009 and 2022. QML is evaluated here because quantum feature maps can define nonlinear, interaction-rich kernels that remain executable on present quantum hardware, providing an alternative representation to compare with classical PCA, RBF, UMAP, and HDBSCAN baselines rather than a presumed computational advantage. After quality screening, log transformation, standardization, and domain-guided feature selection, pollution profiles are evaluated across PCA, RBF spectral clustering, UMAP/KMeans, UMAP/HDBSCAN, a simulated ZZ-style quantum feature-map kernel, and Qiskit Runtime hardware evaluations of the same kernel concept. The initial cleaned-data results show that classical PCA clustering identifies broad lower-load, high organic/surfactant, and rain-season solids/microbial profiles. UMAP/HDBSCAN provides the strongest cleaned full-sample nonlinear baseline, with a silhouette score of 0.568 after excluding 177 noise samples. The simulated quantum-kernel representation separates station-linked gradients, while matched n = 650 stability diagnostics show near-identical quantum-kernel clustering across random initializations (mean ARI = 0.994 for cleaned data) but retain the RBF kernel as the strongest nonlinear comparator. Two 24-sample Qiskit hardware runs and two matched 8-record hardware checks provide proof-of-execution evidence. The analysis is framed as a controlled representation study, not as a claim of quantum advantage. Full article

Background: Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection. Methods: In this retrospective single-center study, 80 pre-transplant serum samples collected on the day of transplantation were initially evaluated; after spectral quality control, 79 samples were retained for analysis. FTIR spectra were acquired in transmission mode and analyzed in the 600–1900 cm⁻¹ and 2800–3400 cm⁻¹ regions. Multiple preprocessing strategies were assessed, including Rubber Band baseline correction, vector normalization, and first- and second-derivative transformation, with and without normalization. Naïve Bayes classifiers with Leave-One-Out Cross-Validation and Fast Correlation-Based Filter feature selection were applied. Results: Exploratory analysis showed broad overlap between groups, indicating a subtle multivariate spectral signal. In the initial exploratory workflow, classifier performance depended strongly on preprocessing and feature selection. Because non-nested feature selection may produce optimistic estimates, the main supervised analysis was repeated using FCBF nested within each LOOCV training fold. The best-performing nested model was obtained using second derivative transformation followed by normalization in the combined 600–1900 and 2800–3400 cm⁻¹ regions, achieving an AUC of 0.837, accuracy of 0.747, sensitivity of 0.675, specificity of 0.821, balanced accuracy of 0.748, and F1-score of 0.730. Permutation testing with 1000 label-randomized repetitions supported performance above chance expectation, with no permuted model reaching the observed AUC (empirical p = 0.000999). Conclusions: Pre-transplant serum FTIR spectroscopy combined with leakage-aware nested machine learning analysis identified an internally validated spectral signal associated with subsequent biopsy-proven rejection. These findings support FTIR as a promising complementary and hypothesis-generating approach for pre-transplant biochemical risk phenotyping, requiring external multicenter validation before clinical application. Full article