Search Results (67)

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

Background/Objective: Low back pain (LBP) is a leading cause of disability worldwide, and patient-reported outcome measures such as the Roland–Morris Disability Questionnaire (RMDQ) are essential for assessing LBP-related disability. While the Modern Standard Arabic version of the RMDQ has demonstrated preliminary reliability, its structural validity has not been thoroughly evaluated. This study aimed to assess the structural validity of the Modern Standard Arabic RMDQ using confirmatory factor analysis (CFA). Methods: A cross-sectional study was conducted for 113 patients with LBP recruited from outpatient physical therapy clinics in Saudi Arabia. Participants completed the Modern Standard Arabic RMDQ, a 24-item instrument scored dichotomously. CFA was performed using the Weighted Least Squares Mean and Variance adjusted estimator to test a unidimensional model. Model fit was assessed using Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Tucker–Lewis Index (TLI), and Comparative Fit Index (CFI). Reliability was evaluated using McDonald’s omega (ω). Results: The initial one-factor CFA model showed close to acceptable fit (RMSEA = 0.044; SRMR = 0.149; TLI = 0.94; CFI = 0.93). After accounting for significant residual correlations between item pairs (items 4 & 21; 13 & 18), model fit improved (Δχ² = 22.33; Δdf = 2; p < 0.001) (RMSEA = 0.038; SRMR = 0.145; TLI = 0.95; CFI = 0.95). Most items had significant loadings on the latent construct, except item 2. McDonald’s ω was 0.91, indicating excellent internal consistency. Conclusions: The findings of this study provide supportive evidence for the structural validity and internal consistency of the Modern Standard Arabic version of the RMDQ and suggest the presence of a dominant unidimensional structure. The Arabic RMDQ may be useful for assessing LBP-related disability in Arabic-speaking patients with LBP, although further validation studies are warranted. Full article

Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score $q_{ij}$, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, $O\left(n^3+nm\right)$ per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work. Full article

In this study, we developed a leakage-free time-series machine learning framework to improve the accuracy of short-term (10 min ahead) wind speed forecasting. The measurements were obtained from real operational data collected at the Bandırma/Balıkesir wind power plant in Türkiye. The framework incorporates chronological train validation test splitting, causal missing data imputation, leakage-free feature engineering, and supervised lag-based modeling. Such a leak-proof design is crucial to avoid future information influencing the training and testing process of models, thus making the forecasting process more realistic and reliable in practice. We tested several models, including persistence, Support Vector Regression (SVR), Least-Squares Gradient Boosting (LSBoost), Random Forest (RF), Elastic Net (ELASTIC), and a stacking ensemble, and evaluated their performance using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-Squared (R²), bias measures, and skill scores, complemented by diagnostic analyses including residual distribution, autocorrelation, regime-based evaluation, Bland–Altman plots, and Quantile Quantile (Q-Q) plots. Our analyses showed that the Elastic Net model achieved balanced and statistically consistent performance, with a test RMSE of 0.6325 m/s, R² = 0.977, and negligible bias. Residual analysis indicated that errors were centered around zero, exhibited weak temporal dependence, and followed an approximately normal distribution in the central quantiles. Regime-based evaluation revealed that the model performed strongly in medium- and high-wind-speed conditions, while accuracy decreased under low wind speeds due to measurement uncertainty and low signal-to-noise ratios. Feature importance analysis indicated that previous wind speed was the dominant predictor, with solar irradiation and air temperature also contributing significantly. Forecast error decomposition showed that most prediction errors arose from natural atmospheric variability, with minimal systematic bias. The Diebold–Mariano test confirmed that ELASTIC statistically outperformed conventional machine learning models such as SVR and Random Forest. The proposed framework demonstrates statistically consistent short-term forecasting behavior that may support operational wind energy management and grid balancing applications. Full article

Soybean protein content is a key indicator of nutritional value and quality grade, and its determination is important for quality evaluation and cultivar selection. To overcome the time-consuming and costly limitations of conventional chemical assays, this study proposed a multiple linear learner ensemble importance-score wavelength selection (MLLEISWS) method to identify informative wavelengths from soybean near-infrared spectra and establish a partial least squares (PLS) model. MLLEISWS was compared with competitive adaptive reweighted sampling, successive projections algorithm, and uninformative variable elimination. Shapley additive exPlanations (SHAP) were applied to the MLLEISWS algorithm to interpret the selected wavelengths. Results showed that the PLS model developed using MLLEISWS achieved the best performance. With only 29 selected wavelengths, the coefficients of determination for the training and test sets reached 0.941 and 0.933, respectively. Root mean square errors were 0.490% and 0.514%, relative root mean square errors were 1.32% and 1.37%, and residual predictive deviation was 3.863, indicating predictive accuracy and stability. SHAP analysis showed that the selected wavelengths were located in protein-related spectral regions and corresponded to overtone and combination bands information from functional groups. MLLEISWS effectively reduced variable dimensionality while maintaining model performance. Full article

This study investigates the factors affecting surface integrity during the machining of near-β Ti-5553, a critical material in the aerospace and defense industries. Considering this alloy as a difficult-to-machine material, the turning process was examined by analyzing the effects of cutting speed, feed rate, and cooling strategy (dry, conventional, and 30 MPa/High-Pressure cooling) on cutting force, temperature, surface roughness, and residual stress. The primary novelty of this research lies in its integrated approach: rather than evaluating surface integrity metrics in isolation, it simultaneously models interrelated responses to residual stress, cutting temperature, cutting force, and surface roughness under high-pressure coolant (HPC) conditions. Furthermore, it introduces a robust machine learning framework that uniquely applies data augmentation (Gaussian jittering and interpolation) to overcome the conventional constraints of limited experimental machining data, providing a highly accurate predictive tool. The experimental data were expanded using data augmentation methods (Gaussian jittering and interpolation) and modeled using five different machine learning algorithms (Extra Trees, Random Forest, Gradient Boosting, KNN, and AdaBoost). The results revealed that cooling pressure plays a dominant role, particularly in residual stress (importance score: 0.926) and cutting temperature (0.657). It was observed that high-pressure cooling (HPC) reduces thermal gradients, thereby lowering tensile stresses and improving surface integrity. When algorithm performances were compared, the Extra Trees and Random Forest models achieved the most accurate predictions after hyperparameter optimization. Specifically, the optimized Extra Trees regressor demonstrated exceptional predictive capability for residual stress, achieving an accuracy of 98.47%, a remarkably high coefficient of determination (R² = 0.9997), and a minimal Mean Squared Error (MSE = 6.8289). These quantitative results confirm that the proposed machine learning framework provides a highly reliable and precise tool for controlling surface quality in HPC- assisted machining. Full article

Systematically characterizing the time series deformation evolution of open-pit mine slopes is key to revealing their potential instability development and supporting subsequent deformation-level classification. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale approximately every ten days, may hold the key to those interactions. However, atmospheric propagation delays still have a significant impact on deformation calculations, and open-pit mine slopes monitored by InSAR often suffer from low coherence. This noise can obscure nonlinear and transient precursory signatures in deformation time series, reducing the identifiability of key temporal patterns required for automated interpretation. Here, we present a Coherence-conditioned Encoder–Decoder Long Short-Term Memory (CED-LSTM) denoising network for deformation time series. We generate a physics-aware synthetic dataset by modeling coherence-dependent measurement noise and temporally correlated atmospheric delays. The network jointly models deformation time series and coherence, using residual learning and adaptive gated composite loss to preserve deformation trends. It is designed to autonomously extract ground deformation signals from noise in InSAR time series without prior knowledge of where deformation occurs or how it evolves. On the synthetic validation set, the network achieved a root mean square error (RMSE) of 2.2 mm across the validation sequences. Applied to three InSAR datasets over an open-pit mine from March 2019 to March 2022, denoising suppresses noise and stabilizes deformation boundaries, enabling extraction of trend and transient indicators and a data-driven deformation-level score. Using quantile-based thresholds, these scores are then used to produce multi-year deformation-level classification maps. Full article

Human Pegivirus (HPgV) is an understudied flavivirus that is highly prevalent and often persists in the blood and tissues of humans. HPgV-infected brain tissue from individuals with Parkinson’s disease has shown significant transcriptomic and immune signaling differences compared to non-infected Parkinson’s brains. The HPgV genome is similar to Hepatitis C Virus (HCV), a well-characterized flavivirus with multiple approved small-molecule therapeutics. Here, we used HCV crystal structures to create homology models for two HPgV non-structural (NS) proteins, the serine protease (NS3) and the RNA-dependent RNA polymerase (NS5B), and performed molecular dynamic simulations. HCV and HPgV proteins had minimal structural differences, as seen by the Root Mean Square Deviation (RMSD) difference between NS3 (1.00 Å) and NS5B (1.26 Å). FDA-approved small molecules were then docked in silico to the NS3 and NS5B subunits of HCV and HPgV. HCV had weak to moderate correlated docking scores with HPgV NS3 (R² = 0.21, p < 0.001) and NS5B (R² = 0.58, p < 0.001). The predicted protein–ligand interactions showed potential binding between HCV antivirals and conserved residues of HPgV, including the catalytic triad for NS3 or the GDD motif for NS5B. Together, these results provide structural insights for key HPgV proteins and highlight possibilities for therapeutic repurposing of HCV antivirals. Full article

Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-threshold denoising methods and improve adaptability across varying density conditions, this study proposes a dual-stage denoising framework for ICESat-2 ATL03 photon data. In the first stage, local photon densities are estimated within a reliable radius, log-transformed, and stratified into multiple levels. Adaptive thresholds are then applied at each level to suppress low-density noise while minimizing over-filtering in sparse regions. In the second stage, residual feedback-driven adaptive fitting strategy is applied along the ground track, where polynomial fitting was performed in sliding windows, with the window size dynamically adjusted based on residuals to refine local structures and eliminate outliers. The experiment was conducted in South Holland and Friesland, across 84 ICESat-2 tracks, where quantitative evaluations under varying day/night and beam conditions confirmed the effectiveness of the proposed framework. For denoising, the proposed method achieved high denoising accuracy, with F1-scores exceeding 0.97 in most cases, outperforming previous methods. Furthermore, building heights derived from footprint buffering and elevation differencing are validated against airborne LiDAR, yielding coefficient of determination (R²) values of 0.7235 and 0.9487 for the two regions, with root mean square error (RMSE) values of 1.5045 m and 1.8849 m, respectively. This study confirms the effectiveness and robustness of the proposed dual-stage framework, demonstrating its strong capability for both noise suppression in ICESat-2 ATL03 photon data and the subsequent accurate estimation of building heights. Full article

Aiming to address insufficient feature extraction, vanishing gradients, and low prediction accuracy in tool wear prediction, this paper proposes a hybrid deep neural network based on a Convolutional Neural Network (CNN), Residual Network (ResNet) residual connections, the Convolutional Block Attention Module (CBAM), and a Bidirectional Gated Recurrent Unit (BiGRU). First, a 34-dimensional multi-domain feature set covering the time domain, frequency domain, and time–frequency domain is constructed, and multi-sensor signals are standardized using z-score normalization. A CNN–BiGRU backbone is then established, where ResNet-style residual connections are introduced to alleviate training degradation and mitigate vanishing-gradient issues in deep networks. Meanwhile, CBAM is integrated into the feature extraction module to adaptively reweight informative features in both channel and spatial dimensions. In addition, a BiGRU layer is embedded for temporal modeling to capture bidirectional dependencies throughout the wear evolution process. Finally, a fully connected layer is used as a regressor to map high-dimensional representations to tool wear values. Experiments on the PHM2010 dataset demonstrate that the proposed hybrid architecture is more stable and achieves better predictive performance than several mainstream deep learning baselines. Systematic ablation studies further quantify the contribution of each component: compared with the baseline CNN model, the mean absolute error (MAE) is reduced by 47.5%, the root mean square error (RMSE) is reduced by 68.5%, and the coefficient of determination (R²) increases by 14.5%, enabling accurate tool wear prediction. Full article

Background/Objectives: Cochlear implantation is the most widely used treatment option for patients with severe to profound hearing loss. Despite being a relatively standardized surgical procedure, cochlear implant (CI) outcomes vary considerably among patients. Several studies have attempted to develop predictive models for CI outcomes but achieving accurate and generalizable predictions remains challenging. The present study aimed to evaluate whether simple and complex statistical and machine learning models could outperform the Null model based on various pre-CI implantation variables. Methods: We conducted a retrospective analysis of 236 ears with postlingual profound sensorineural hearing loss (SNHL) and measurable residual hearing (WRS_max > 0%) at the time of implantation. The median postoperative word recognition score with CI (WRS₆₅(CI)) was 75% [Q1: 55%, Q3: 80%]. The dataset was divided using a 70:15:15 split into training (n = 165), validation (n = 35) and test (n = 36) cohorts. We evaluated multiple modeling approaches: different Generalized Linear Model (GLM) approaches, Elastic Net, XGBoost, Random Forest, ensemble methods, and a Null model baseline. Results: All models demonstrated similar predictive performance, with root mean squared errors ranging from 26.28 percentage points (pp) to 30.74 and mean absolute errors ranging from 20.62 pp to 23.75 pp. Coefficients of determination (R²) ranged from −0.468 to −0.073. Bland–Altman analyses revealed wide limits of agreement and consistent negative bias, while Passing–Bablok regression indicated calibration errors. Nonetheless, all models incorporating predictors significantly outperformed the Null model. Conclusions: Increasing model complexity yielded only marginal improvements in predictive accuracy compared with simpler statistical models. Pre-implantation clinical variables showed limited evidence of predictive validity for CI outcomes, although further research is needed. Full article

Background/Objectives: Poor sleep quality is a prevalent and burdensome concern among breast cancer survivors. However, its assessment relies heavily on the Pittsburgh Sleep Quality Index (PSQI), whose latent structure has shown inconsistent support across populations. This study aimed to examine the underlying factor structure and reliability of the PSQI among Korean breast cancer survivors using confirmatory factor analysis. Methods: A cross-sectional survey was conducted with 386 non-metastatic breast cancer survivors recruited from a university cancer center in South Korea. Ten competing one-, two-, and three-factor models were identified in previous studies and tested using confirmatory factor analysis with maximum likelihood estimation. Model fit was assessed with χ²/df, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), and model parsimony was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results: The mean global PSQI score was 7.46 (SD = 3.95), and 72.8% of participants were classified as poor sleepers. Among the tested model, a three-factor solution provided the best fit (χ²/df = 0.795, CFI ≈ 1.000, TLI ≈ 1.000, RMSEA ≈ 0.000, SRMR = 0.017) and achieved the lowest AIC and BIC values. This finding indicates the most favorable balance between fit and parsimony. This three-factor model delineates three distinct but related domains: perceived sleep quality, sleep efficiency, and daily disturbances. The global PSQI demonstrates acceptable reliability. Conclusions: These findings support the three-factor structure of the PSQI as the most valid representation of sleep quality among Korean breast cancer survivors. These results underscore the importance of population-specific validation of sleep measures and confirm the clinical utility of this measure as a multidimensional tool for assessing sleep in survivorship care. Full article

To address the challenge of anomaly detection in vibration signals from high-speed electric multiple unit (EMU) motor bearings, characterized by strong non-stationarity and multi-component coupling, this study proposes a synergistic approach integrating variational mode decomposition (VMD) and deep learning. Unlike datasets focused on fault diagnosis (identifying known fault types), anomaly detection identifies deviations into unknown states. The method utilizes real-world, non-real-time vibration data from ground monitoring systems to detect anomalies from early signs to significant deviations. Firstly, adaptive VMD parameter selection, guided by power spectral density (PSD), optimizes the number of modes and penalty factors to overcome mode mixing and bandwidth constraints. Secondly, a hybrid deep learning model integrates convolutional neural networks (CNNs), bidirectional long- and short-term memory (BiLSTM), and residual network (ResNet), enabling precise modal component prediction and signal reconstruction through multi-scale feature extraction and temporal modeling. Finally, the root mean square (RMS) features of prediction errors from normal operational data train a one-class support vector machine (OC-SVM), establishing a normal-state decision boundary for anomaly identification. Validation using CR400AF EMU motor bearing data demonstrates exceptional performance: under normal conditions, root mean square error $(RMSE=0.005)$, Mean Absolute Error $(MAE=0.002)$, and Coefficient of Determination $(R^2=0.999)$; for anomaly detection, accuracy = 95.2% and F1-score = 0.909, significantly outperforming traditional methods like Isolation Forest (F1-score = 0.389). This provides a reliable technical solution for intelligent operation and maintenance of EMU motor bearings in complex conditions. Full article

Background: Hydroxycarboxylic acid receptor 3 (HCAR3) is a receptor that is mainly expressed in human adipose tissue. It can inhibit lipolysis through the inhibition of adenylyl cyclase; thus, it is closely related to the regulation of lipids in the human body. This makes HCAR3 a compelling target for developing drugs against dyslipidemia. Notably, the reported active compounds for HCAR3 are all carboxylic acids. This observation is in line with the fact that ARG111 has been reported as the key residue to anchor the active compound in a closely related homologous protein—HCAR2. Methods: In this study, we aim to discover new chemicals, through virtual screening, that may bind with HCAR3. As there are several choices for the receptor conformation, cross-docking was conducted and the root-mean-square deviation of the docking pose from the conformation of the crystal ligand was employed to determine the best receptor conformation for screening. Ligands from the ZINC20 database were screened through molecular docking, and 30 candidates were subjected to 100 ns MD simulations. Six stable complexes were further assessed by umbrella sampling to estimate binding affinity. Results: The homology model (HCAR3_homology) was selected as the receptor. Following the protocol determined by the retrospective docking process, prospective docking was conducted to screen the ligands from the ZINC20 database. Subsequently, the top 30 compounds with a good docking score and a good interaction with ARG111 were subjected to 100 ns molecular dynamics (MD) simulations, and their binding stability was analyzed based on the resulting trajectories. Finally, six compounds were chosen for binding free energy calculation using umbrella sampling; all showed negative binding affinities. Conclusions: All six compounds selected for umbrella sampling showed negative binding affinities, suggesting their potential as novel HCAR3 ligands for the development of drugs against dyslipidemia. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article