Search Results (11,138)

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

Outlier detection in time series is traditionally formulated as the identification of rare or extreme observations with respect to global statistical properties. While effective for stationary processes, this perspective becomes insufficient in complex and non-stationary systems, where atypical behavior may manifest as disruptions of stable relationships rather than numerical extremeness. This paper proposes a rule-based framework for detecting structural outliers in non-stationary time series. Regular system behavior is represented by an interpretable set of deterministic IF–THEN rules describing stable relational patterns between features. Each rule defines a logical context and an admissible range of a diagnostic quantity, estimated nonparametrically from historical observations satisfying the rule condition. For a given observation, the set of active rules is identified and a structural inconsistency score is computed as the fraction of violated rule consequences. Additionally, observations lacking support from high-frequency contexts are treated as candidates for structural atypicality. The method is deterministic and avoids the need for explicit probabilistic modeling or iterative parameter learning, which simplifies interpretation and implementation. The framework is illustrated on daily EUR/USD data (2010–2022) using technical indicators (EMA, RSI) and absolute log-returns as the diagnostic measure. Results provide evidence that structurally atypical events can be identified even when global statistical thresholds remain unviolated, suggesting the practical relevance of relational analysis for non-stationary time series monitoring contexts. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

Background: Spinopelvic dissociation secondary to sacral fracture–dislocation is a rare but severe injury, most often resulting from high-energy trauma. Management remains challenging, particularly in adolescents, and the optimal choice between open and percutaneous fixation is still debated. Methods: We present two adolescent cases of traumatic sacral fracture–dislocation with spinopelvic dissociation, one treated with percutaneous fixation and one with open lumbopelvic stabilization both with the use of navigation. The systematic literature review included 29 published studies. Together with the present two-patient case series, the overall analysis comprised 30 studies/series and 739 patients. Data on demographics, mechanisms of injury, neurological involvement, treatment strategies, and outcomes were extracted and analyzed. Results: Case 1 (18 years) was managed with closed reduction and percutaneous fixation, achieving complete neurological and functional recovery at 6 months. Case 2 (14 years) underwent open reduction, decompression, and lumbopelvic fixation, with favorable radiological outcomes but residual sphincter dysfunction at follow-up. In the literature, the weighted mean age was 40.6 years (range 5–91), with 48.6% presenting neurological deficits, most frequently cauda equina syndrome. Surgical management was performed in nearly all cases, with mean time to fixation ranging from 3.6 to 8.6 days. Open techniques were predominantly used in patients with severe displacement or neurological compromise, whereas percutaneous fixation was associated with reduced surgical morbidity and satisfactory neurological recovery in selected patients. Permanent bladder and bowel dysfunction persisted in up to 33% of cases. Conclusions: Spinopelvic dissociation following sacral fracture–dislocation remains a rare and highly unstable injury with frequent neurological impairment. Early surgical stabilization may be beneficial when the patient’s clinical condition permits, and the choice between open and percutaneous fixation should be individualized according to fracture morphology, neurological status, and the need for direct decompression. Our adolescent cases highlight both the potential for complete recovery and the risk of residual dysfunction, reflecting the complexity of these injuries. Full article

The transits of Venus occur in couples every 105/122 years: the observed ones were in 1639; 1761–1769; 1874–1882; and 2004–2012. The next couple will occur in the years 2117 and 2125. We need all four contacts to determine the solar diameter accurately. The black-drop phenomenon blurs internal contacts, so we developed a parabolic analysis of the chords drawn by the disk of Venus on the solar limb. The extrapolation of the zeroes gives the contact timings. We tested this method with some high-quality images obtained in 2004 and 2012, and we applied it to the observations of 2012 in a visual band (Huairou Solar Observing Station, hazy weather) and H-alpha (Shen Zen Astronomical Observatory). To exclude a reduction in the measured diameter by the haze, we made two series of measures at the Clementine Gnomon (Rome) and at the PHYSIS telescope (Rome), under various sky transparencies and with diffraction-limited instruments. The haze and the low altitudes above the horizon reduced accuracy at all first contacts examined, without changing the solar diameter. Our measures obtained in China during the transit of 2012 yielded a photospheric radius R_⊙P = 959.33″ ± 0.06″, based on 76 + 75 diffraction-limited images; this is compatible with the chromospheric radius measured at the base of the spiculae, which is R_⊙C = 959.78″ ± 0.11″, relying on 7 + 5 diffraction-limited series of images. Full article

Conventional ultrasound-based assessment of aortic stiffness relies on two-point distension metrics using maximum and minimum vessel diameters within a cardiac cycle, which may not fully reflect time-resolved aortic wall dynamics. This retrospective pilot study investigated the feasibility and clinical relevance of a time-series-based stiffness parameter, termed pulsatile stiffness-β, derived from automated segmentation of archived aortic M-mode ultrasound images. Seventy-nine cases with available aortic M-mode images were analyzed. Automated image processing was used to segment the anterior and posterior aortic walls and reconstruct diameter waveforms. Conventional stiffness-β, pulsatile stiffness-β, and wall asymmetry-related parameters were calculated and compared with demographic, tonometry-derived, hemodynamic, coronary burden, cardiovascular risk, and echocardiographic variables. Conventional and pulsatile stiffness-β were strongly correlated and showed directionally consistent associations with established vascular functional parameters, including systolic blood pressure, pulse pressure, augmentation pressure, age, and cardiovascular risk burden. Pulsatile stiffness-β demonstrated association patterns broadly comparable to conventional stiffness-β, suggesting its role as a waveform-informed extension rather than a superior alternative. Wall asymmetry-related parameters were associated with the Syntax score. Automated analysis of archived aortic M-mode images may provide feasible time-resolved vascular biomarkers for stiffness and wall motion assessment. Full article

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology transforms one-dimensional time-series pressure data into two-dimensional image representations using the element-wise summation of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), which simultaneously preserves global operational trends and local transient dynamics for vision model analysis. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT + GAF + BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score, with an F1-score of 0.93. Visualizations using t-SNE confirm the ViT’s superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems. Full article

Network intrusion detection systems (NIDS) play a crucial role in modern network environments where diverse and rapidly evolving traffic patterns are observed. Although deep learning-based NIDS have demonstrated strong performance within specific datasets, their effectiveness significantly degrades when applied to unseen network environments due to domain discrepancies. In this paper, we first experimentally demonstrate the performance degradation of time-series-based NIDS under cross-domain conditions using multiple benchmark datasets. Then, we propose a LoRA-based domain adaptation framework for time-series-based NIDS models. Instead of retraining the entire model, the proposed approach freezes the backbone network and applies low-rank updates to selected layers, enabling parameter-efficient adaptation to new domains. Experimental results show that the proposed method consistently improves cross-domain detection performance across multiple dataset combinations, particularly in terms of recall, while requiring only a small number of additional parameters. Full article

Accurate waveform classification in noisy environments is an important task in modern communications, radar signal analysis, biomedical signal interpretation, industrial monitoring and other signal processing systems. This paper investigates the performance of three neural network architectures: Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) for multiclass waveform classification in the presence of Additive White Gaussian Noise (AWGN). A time series dataset consisting of multiple waveform classes is generated and corrupted with AWGN across a wide range of signal-to-noise ratio (SNR) levels to simulate noisy signal distortion conditions. The three models are trained and evaluated under identical conditions to ensure a fair comparison. Their classification performance is evaluated in terms of accuracy, Confusion Matrix (CM), Receiver Operating Characteristic (ROC) curve and the Area Under the ROC curve (AUC) across varying SNR values. Simulation results demonstrate that the 1D-CNN effectively captures local temporal patterns and achieves superior robustness in classification at moderate and high SNR levels. The LSTM model demonstrates the ability to capture temporal dependencies in sequential waveform data but exhibits sensitivity to waveform variations due to amplitude, phase and frequency changes and noise at lower SNR values. The MLP, although computationally simpler, shows comparatively limited performance in low-SNR conditions due to its lack of temporal feature extraction capability. For the case of multiclass deterministic waveforms, the accuracy of classification for the 1D-CNN and LSTM is nearly 100% at SNR = 5 dB showing their robustness in classification, whereas the accuracy of MLP is approximately 70% that shows poor classification in noisy conditions. When there is random amplitude, frequency and phase variations in the waveforms, the accuracy of the 1D-CNN and MLP increases with SNR, and 1D-CNN superior to MLP. However, the LSTM accuracy fails to improve with SNR, resulting in poor classification performance in such a scenario. The results provide an insight into the suitability of different neural architectures for waveform classification tasks in noisy communication or other time series applications and highlight the advantages of convolutional feature extraction for robust signal recognition. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

This work aims to determine whether pre-seismic ground dislocations extracted from interferometric satellite synthetic aperture radar (InSAR) imagery contain predictive information for distinguishing between two magnitude classes of an upcoming earthquake, assuming that an earthquake’s occurrence is already imminent. For this reason, twenty-three earthquakes of various magnitudes that occurred in Greece during the year 2020 were analyzed using SAR data to construct a time-series of five six-day InSAR images for each earthquake, spanning a total 24-day period before the earthquake. For each earthquake, four ground dislocation images covering the area around each earthquake were derived from the interferograms, each showing the dislocation during a six-day time interval. Images showing the total ground dislocation during the entire 24-day period before the earthquake were also produced by fusing the four images. Three machine learning classifiers were used to relate the earthquake magnitude class to pre-seismic ground dislocations. High accuracies were obtained with both support vector machine (SVM) and random forest (RF), yet they were highly dependent on the type of images used. In a subsequent analysis, five regression models were applied to estimate the earthquakes’ epicenters from dislocation images. The results reveal that the proposed approach is able to achieve well-localized epicentral area prediction, indicating the potential predictive value of this tool for seismic hazard assessment and emergency planning. Full article

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline ($64.5\%$), a Persistent Positive Anomaly ($20.7\%$) indicative of unmodeled inundation, and a Transient Negative Anomaly ($14.8\%$) representing rapid drainage. Consequently, $35.5\%$ of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters. Full article

This paper extends a previous classification study by examining clustering methods on the same synthetic datasets and comparing their behavior with the previously obtained classification results. This study investigates the performance of selected time series clustering methods under controlled changes in noise level and class complexity. Six clustering methods representing distance-based, feature-based, and deep learning approaches were evaluated on 82 balanced synthetic datasets. The datasets contained from two to six classes, different levels of additive Gaussian noise, 200 time series per dataset, and 1000 observations per time series. The analysis focused on clustering quality, comparative behavior with classification models, and computational cost in terms of training time and peak memory usage. Clustering quality was assessed mainly using Adjusted Rand Index and V-measure, while accuracy after Hungarian label matching was used as an auxiliary measure for comparison with classification models. The results show that distance-based methods, and particularly TimeSeriesKMedoids, achieved the most robust and consistent clustering performance across the considered settings. Clustering quality decreased with both the number of classes and the noise level, but the effect of noise was clearly stronger. Feature-based and deep learning-based clustering methods were generally more sensitive to noise, while deep models were also associated with substantially higher computational cost. In terms of memory usage, classical clustering methods remained below 50 MiB, whereas deep learning-based clustering methods required substantially more memory. This study further shows that accuracy computed after Hungarian label matching may provide an overly optimistic view of clustering quality. Accuracy after Hungarian label matching is reported only as an auxiliary metric, while the main interpretation of clustering quality is based on structure-sensitive measures such as Adjusted Rand Index and V-measure. Overall, the findings highlight the importance of robust distance-based approaches and of using structure-sensitive evaluation measures when analyzing time series clustering. Full article

Mixed ground conditions cause sudden fluctuations in the tunnel boring machine (TBM) advance rate (AR). Accurate forecasting is necessary for tunneling safety. Existing data-driven models, however, often treat the excavation process as an isolated event. They ignore the physical memory effect of rock–machine interactions. They also lack the ability to diagnose abnormal AR drops. To address these issues, an interpretable forecasting framework is proposed. First, a Selection–Processing (SP) system is established to standardize data handling and quantify geological heterogeneity. Second, a Time-Series Structure (TSS) network is developed to construct a one-ring-ahead input block using the current completed-ring state and CCF/PACF-guided historical windows. The framework is validated on the Shenzhen–Dayawan Intercity Line. The optimized GWO-LSTM model achieves high accuracy (R² = 0.977, MAE = 2.15, RMSE = 3.07). Compared with the no-TSS reference scheme, the MAE and RMSE decrease from 2.7081 and 3.6045 to 2.1496 and 3.0724, respectively. Furthermore, Shapley Additive Explanations (SHAP) are applied for ring-by-ring anomaly diagnosis. Local SHAP analysis indicates that both current-state variables and selected lagged variables provide diagnostic information for AR fluctuations. The identified lags are interpreted as project-specific memory indicators rather than universal physical delay constants. This method provides model-based diagnostic clues for associating sudden AR drops with specific operational or geological factors. The proposed framework provides a transparent and practical tool for TBM performance prediction and field diagnosis. Full article