Search Results (298)

Manual inspection in large-scale pigeon farms is inefficient and often misses critical targets. In addition, recognition results are difficult to link to physical cage locations in real time. Here, we develop an intelligent inspection and localization system that integrates an improved lightweight YOLO model with QR-code-based tracking. QR codes are deployed along the inspection route as spatial anchors. Base detection models are combined with the ByteTrack algorithm to establish a dynamic mapping among video frames, cage numbers and detected targets. To improve the detection of small pigeon eggs caused by interference from metal cage meshes, we further design a lightweight YOLO-PEDI (Pigeon Egg Detection Inspection) model. Ghost modules replace standard convolutions to reduce computational cost. CBAM is introduced to enhance feature extraction in complex backgrounds. The newly designed model enables simultaneous identification of egg number and egg condition, including normal and broken eggs. The proposed method achieves an mAP50 of 98.1%, with only 1.53 million parameters and an inference time of 0.8 ms. Field tests show a cumulative egg-counting accuracy of 80.0% and a broken egg detection rate of 98.0%. These results demonstrate the potential of the proposed system for intelligent inspection in pigeon farming and provide a practical route towards precise traceability and digital production management. Full article

This paper presents a surrogate-based uncertainty quantification (UQ) framework for coupled structural–acoustic systems subject to material and geometric variability. The proposed methodology integrates the Finite Element Method (FEM) with two metamodeling techniques—the Quadratic Response Surface (QRS) and Kriging—and Monte Carlo Simulations (MCS), to efficiently characterize the probabilistic behavior of the acoustic response. Two accuracy metrics (cross-validation error and prediction error) are used to validate the surrogate models. Numerical experiments demonstrate that the Kriging metamodel trained with 30 Latin Hypercube Sampling (LHS) points achieves superior predictive accuracy, with a Relative Maximum Error of 4.125 × 10⁻⁷. Monte Carlo Simulations conducted via the Kriging surrogate reduce the computational cost by more than six orders of magnitude compared to direct FEM-based MCS, while maintaining high accuracy. The proposed framework is validated on a rectangular cavity coupled with two flexible aluminum plates, and provides an efficient and accurate tool for vibro-acoustic UQ in complex engineering systems. Full article

Background: Hypertension is increasingly recognized as a complex psychophysiological condition in which psychological factors interact with autonomic regulation and cardiac electrical stability. This study aimed to investigate the associations of spiritual orientation, perceived stress, and self-efficacy with heart rate variability (HRV) and electrocardiographic (ECG) repolarization parameters in individuals with hypertension. Methods: A total of 200 participants were included, comprising 100 hypertensive patients and 100 age- and sex-matched healthy controls. HRV was assessed using time-domain indices (SDNN and RMSSD), while ECG parameters included heart rate, QRS duration, QT interval, Tp-e interval, and Tp-e/QT ratio. Psychosocial variables were evaluated using validated scales. Group comparisons, correlation analyses, and multivariate regression models were performed. Results: Compared with controls, hypertensive patients exhibited significantly lower SDNN (68.73 ± 10.74 vs. 82.85 ± 10.74 ms, p < 0.001, Cohen’s d = 1.52) and RMSSD (35.55 ± 8.36 vs. 44.17 ± 8.36 ms, p < 0.001, d = 1.18), along with higher heart rate (74.73 ± 9.12 vs. 68.72 ± 8.85 bpm, p < 0.001, d = 1.11) and increased repolarization parameters, including QT interval (407 ± 18.3 vs. 397.58 ± 17.9 ms, p < 0.001, d = −0.69), Tp-e interval (97.95 ± 10.2 vs. 90.94 ± 9.8 ms, p < 0.001, d = 0.89), and Tp-e/QT ratio (0.24 ± 0.02 vs. 0.23 ± 0.02, p < 0.001). Spiritual orientation was positively correlated with SDNN (r = 0.274, p < 0.001) and RMSSD (r = 0.242, p < 0.001) and negatively correlated with heart rate (r = −0.277, p < 0.001), Tp-e (r = −0.256, p < 0.001), and Tp-e/QT ratio (r = −0.258, p < 0.001). Perceived stress showed inverse correlations with HRV indices and positive associations with repolarization parameters. In multivariate analysis, spiritual orientation remained an independent predictor of higher HRV indices, whereas perceived stress independently predicted a longer Tp-e interval and lower HRV. Conclusions: Spiritual orientation and stress-related factors are significantly associated with both autonomic function and cardiac repolarization in hypertension. These findings support a psychophysiological model in which psychosocial resources and stress responses jointly influence cardiovascular regulation. Integrating psychosocial assessment into hypertension management may provide additional insights beyond traditional risk factors. Full article

Class imbalance among arrhythmia types and electrocardiogram (ECG) signal complexity present significant challenges for automated ECG-based arrhythmia detection. This research proposes an innovative approach that combines Genetic Algorithm (GA) optimization of Convolutional Neural Network (CNN) hyperparameters with morphological skeletonization of ECG images. The MIT-BIH Arrhythmia Database served as the primary data source, with the ECG signal converted to skeletonized representations emphasizing QRS complex geometry. A GA-optimized model was compared against a heuristic (manual design) baseline to determine optimal kernel and filter configurations. Evaluation emphasized not only overall accuracy but also robust metrics for minority classes. The optimized model achieved 97.26% accuracy, with macro recall improving substantially from 77.36% to 83.10% (+5.74%). These results demonstrate that evolutionary optimization enhances detection sensitivity to subtle geometric patterns, effectively mitigating class imbalance without artificial oversampling techniques. Full article

The QR distribution, recently introduced for modeling lifetime data under Type-II censoring, offers a flexible framework for survival and reliability analysis. This study provides the first comprehensive evaluation of multiple modern estimation techniques for the QR distribution under Type-II censoring. We systematically compare classical maximum likelihood estimation with stochastic gradient descent variants (Momentum and Adam), Bayesian approaches including Maximum A Posteriori estimation, Markov Chain Monte Carlo, and Variational Inference, as well as machine learning-integrated methods such as amortized neural network inference. Using both synthetic and the real Veterans’ Administration Lung Cancer dataset, we evaluate these methods in terms of parameter estimation accuracy, computational efficiency, and convergence behavior. The results demonstrate the strengths of optimization-based, Bayesian, and neural approaches, highlighting their practical utility in handling complex censored survival data. This research validates the distribution’s effectiveness in capturing survival dynamics, offering valuable insights for clinical applications and highlighting areas for methodological improvement. Full article

Background: Cardiac resynchronization therapy (CRT) improves outcomes in heart failure (HF) patients with reduced left ventricular ejection fraction (LVEF) and a wide QRS complex. However, up to 30–50% of patients fail to respond. The QRS Index, which quantifies QRS shortening after CRT, has emerged as a potential predictor of response. We aimed to perform a systematic review and meta-analysis to evaluate the association between QRS Index and CRT response. Methods: We searched PubMed, Scopus and Cochrane for studies reporting QRS Index values in CRT responders and non-responders. Studies defining response based on clinical, echocardiographic, or combined criteria were included. Heterogeneity was assessed using the I² statistic, and a random-effects model was applied. A meta-regression analysis explored the relationship between baseline echocardiographic parameters and QRS Index. Results: Nine studies with 1274 patients met the inclusion criteria, with 760 (59%) classified as responders and 514 (41%) as non-responders. The weighted mean ± standard deviation was 16.14 ± 13.19 in responders and 7.22 ± 14.96 in non-responders. The QRS Index was significantly higher in the responder group compared to non-responders (mean difference: 8.76; 95% CI: 6.45–11.06; I² = 45%; p < 0.00001). Meta-regression revealed that lower left ventricular end-systolic volume (LVESV) values were associated with even higher QRS Index in responders compared to non-responders (β = −0.0483; 95% CI: −0.0938; −0.0029, p = 0.0372). Conclusions: QRS Index is significantly higher in CRT responders, supporting its role as a predictor of response. Further studies are needed to standardize its clinical use and assess its prognostic impact. Full article

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments. Full article

Quasi-resonant (QR) flyback inverters suffer from significant performance degradation under varying thermal conditions. This is because the thermal drift of passive components’ parameters deviates the switching instants from their optimal valley points, leading to increased switching losses and higher grid current distortion. To address this challenge, we propose an online self-tuning control strategy based on a Recurrent Neural Network (RNN) designed for embedded implementation. The RNN model continuously observes a sequence of non-intrusive operational data, including input voltage, input current, and grid current, and directly predicts the optimal time-delay compensation for the valley-switching logic. This end-to-end approach eliminates the need for online parameter identification, complex physical model calculations, or dedicated thermal sensors. The proposed framework was validated through comprehensive MATLAB/Simulink simulations. The results demonstrate that when operating across a wide temperature range (e.g., from 25 °C to 85 °C), the self-tuning control scheme enhances conversion efficiency by over 3.0% and reduces the grid’s current Total Harmonic Distortion (THD) from 5.8% to below 2.0%, thereby significantly improving the inverter’s lifetime performance and reliability. Full article

Thedeep score-based diffusion (DeScoD) model performs well in electrocardiogram (ECG) denoising tasks. However, due to the theoretical error lower bound in approximating functions with linear transformations, it often lacks flexibility when fitting non-stationary noise, baseline wander, or morphologically variable features such as QRS complexes in ECG signals. In this paper, we propose a Kolmogorov–Arnold network enhanced deep score-based diffusion (KAN-DeScoD) model, which is the first to integrate Kolmogorov–Arnold network (KAN) layers into an ECG denoising diffusion model. By leveraging KAN’s adaptive activation functions, which more finely capture the complex structures within ECG signals, the model’s robustness in high-noise environments, as well as the accuracy and stability of signal reconstruction, are improved. We validate the effectiveness of the proposed method on the QT Database and the MIT-BIH Noise Stress Test Database (NSTDB). Experimental results show that under different shots and noise intensities, ours outperforms the DeScoD model across multiple metrics. The research results demonstrate the effectiveness of introducing KAN, which improves the model’s robustness in high-noise environments and the accuracy of signal reconstruction. Full article

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

The fetal electrocardiogram (FECG) is critical for assessing fetal cardiac electrophysiology and detecting fetal distress and arrhythmias. Single-channel abdominal electrocardiogram (AECG) enables home-based monitoring but faces challenges posed by weak fetal signals, maternal interference, and the lack of spatial information. Ensemble Empirical Mode Decomposition (EEMD) is suitable for nonstationary AECG signals but relies on accurate selection of intrinsic mode functions (IMFs). In this study, a deep learning-guided method was proposed: a one-dimensional convolutional neural network (1D CNN) scored and selected EEMD-derived IMFs, followed by maternal QRS template subtraction and secondary EEMD purification to achieve automatic FECG extraction. Leave-one-subject-out (LOSO) cross-validation was performed on 15 simulated cases and 5 ADFECGDB records, yielding a mean AUC of 0.9282 ± 0.0189 for the IMF classifier. On the independent DaISy and NIFEA arrhythmia datasets, the proposed CNN-2×EEMD method achieved correlation coefficients of 0.94–0.96, F1-scores of 0.8372–0.9565 for fetal R-peak detection, and SNR improvements of 13.39–15.88 dB. This method outperformed conventional automatic selection methods and matched the performance of manual selection. Ablation studies validated the optimal network design and IMF selection strategy, while complexity analysis (0.08 GFLOPs, 2.24 ms latency) confirmed its suitability for real-time wearable deployment. Full article

Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with physiological temporal constraints (PTC) to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted within the framework, which encodes physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records demonstrated robust R-peak detection across rhythm types. In AF-related rhythms, where organized P waves are physiologically absent, the framework appropriately suppressed P-peak detections, with false-positive rates remaining below 0.31%. Qualitative application to ECG recordings from the PTB-XL database further demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers within the proposed framework, without reliance on complex deep learning architectures. Full article

Electrocardiogram waveform delineation is a fundamental task for quantitative cardiac analysis, yet accurate and consistent estimation of waveform boundaries remains challenging due to heart rate variability, inter-subject morphological differences, and nonlinear temporal distortions across cardiac cycles. Conventional rule-based methods and pointwise Dynamic Time Warping approaches are sensitive to amplitude variations and baseline fluctuations, while deep learning–based models require large annotated datasets and often suffer from limited interpretability and generalization. In this study, we propose a morphology-oriented ECG waveform alignment framework based on Pearson correlation–based Dynamic Time Warping (PCDTW). By integrating window-level matching with a correlation-driven cost function, the proposed method explicitly emphasizes local morphological similarity rather than absolute amplitude differences. Each ECG record is aligned using a subject-specific reference cycle constructed from normalized RR intervals, enabling stable correspondence of waveform boundaries without any training process. The proposed method was evaluated on two publicly available databases, the QT Database (QTDB) and the Lobachevsky University Electrocardiography Database (LUDB). Experimental results show that PCDTW significantly reduces QT and QTcB estimation errors compared with conventional DTW variants, demonstrating improved temporal consistency and lower bias across cardiac cycles. In particular, the mean QTcB error was reduced to 28.14 ms, compared with 124.54 ms obtained using conventional DTW. In addition, on LUDB, the overall mean delineation error for the P wave, QRS complex, and T wave boundaries was 10.68 ms, showing comparable or superior performance to state-of-the-art deep learning–based methods despite requiring no external training data. These findings indicate that morphology-aware, correlation-based temporal alignment provides a robust and interpretable alternative for ECG waveform boundary detection under realistic physiological variability. Full article

Background/Objectives: Dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) are common cardiomyopathies associated with heart failure. Electrocardiogram (ECG) screening before an echocardiogram could help streamline diagnosis, particularly in rural areas. Prior ECG–machine learning (ML) studies do not use open-source data when studying cardiomyopathy, and very few proprietary studies directly compare HCM and DCM or address ECG differences within obstructive (HOCM) and non-obstructive HCM (HNCM). Methods: Standard and vectorcardiogram-derived (VCG) ECG features were extracted from the MIMIC-IV-ECG database. The final cohort comprised 599 patients (HCM = 208 [HOCM = 99, HNCM = 53, unknown = 56]; DCM = 391 [ischemic cardiomyopathy with left ventricular dilation = 250, non-ischemic = 141]). Logistic regression (LR) and extreme gradient boosting (XGBoost) with five-fold cross-validation separated HCM from ischemic cardiomyopathy with left ventricular dilation (DCM-I) and non-ischemic DCM (DCM-NI), and HOCM from HNCM. Results: Using the area under the receiver-operating-characteristic curve (AUC-ROC) as the performance metric, LR achieved high discrimination of HCM from DCM-I (0.92) and DCM-NI (0.90). However, differentiating HOCM from HNCM proved more difficult (XGBoost = 0.81; LR = 0.75). Both DCM subtypes (especially ischemic) showed lower QRS amplitudes and right-posterior ventricular gradient orientation; HCM displayed higher amplitudes and larger, more complex T-loops. Within HCM, HOCM had stronger leftward electrical activity and more dipolar to non-dipolar QRS energy after singular value decomposition. Conclusions: Using only open-access data, we demonstrate an interpretable ECG-based pipeline that discriminates cardiomyopathy and highlights distinct features. While detecting obstruction remains difficult, ECG features provide measurable separation, supporting possible diagnostic screening and offering a reproducible framework for future studies. Full article

This study develops a hybrid deep learning framework for point and interval prediction of Total Nitrogen (TN) concentrations in the Pearl River Delta, China. To address the inherent stochasticity of water quality systems, Bidirectional Long Short-Term Memory (BiLSTM) networks are integrated with residual learning blocks (Res) and Bayesian Optimization (BO). The resulting BiLSTM-Res-BO framework is evaluated within a comparative analysis of eight forecasting models that combine BiLSTM and BiGRU architectures with two uncertainty quantification approaches: Quantile Regression (QR) and Monte Carlo Dropout (MCD). Results from 37 monitoring stations demonstrate that the effectiveness of residual learning is highly context-dependent. For point forecasting, BiLSTM-Res achieves substantial performance gains (12.5–15% RMSE reduction) at complexity-sensitive sites, while providing negligible or slightly degraded performance under hydrologically stable conditions. For interval forecasting, QR-based residual models—particularly Q-BiLSTM-Res—produce notably narrower prediction intervals, with interval width reductions of 16.7–27.3% relative to the baseline BiLSTM model, under comparable levels of empirical coverage. In contrast, MC-dropout-based methods tend to yield wider intervals with different coverage–width trade-offs, reflecting distinct uncertainty propagation behaviors across modeling frameworks. Full article