Search Results (90)

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

Background: Whole-body electromyostimulation (WB-EMS) is a training technology that enables the stimulation of all the main muscle groups with dedicated intensity, attracting many sportspeople and athletes of various disciplines. The aim of this systematic review and meta-analysis was to determine the effect of WB-EMS on maximum jump, sprint, and agility performance in exercising cohorts. Methods: Systematic literature research of five electronic databases up to March 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme and including interventional trials with at least one WB-EMS and one active or inactive control group that focus on maximum jump, sprint, and agility performance in sportspeople and athletes. Applying a random-effect model that includes the inverse heterogeneity model (IVhet), effects sizes (SMD), and calculates 95% confidence intervals (95%-CIs). Subgroup analyses addressed superimposed WB-EMS application vs. underlying voluntary exercise. Results: Twelve studies with 145 participants in the WB-EMS and 148 participants in the control group were included. Most trials on jumping (10 of 12) and all trials on sprinting and agility performance applied superimposed WB-EMS protocols compared with underlying voluntary exercise. We observed no significant positive effects of WB-EMS on maximum jump (12 studies, SMD: 0.34, 95%-CI: −0.35 to 1.03), sprint (8 studies, SMD: 0.07, 95%-CI: −0.66 to 0.80), and agility performance (5 studies, SMD: −0.11, 95%-CI: −1.28 to 1.06). Heterogeneity between the trial results was considerable (I² > 80%) in all cases. Conclusions: Superimposed WB-EMS compared to the underlying predominately near-maximum to maximum intensity voluntary exercise provides only limited additional effects on jumping, sprinting, and ability performance. Full article

Magnetic field measurements are essential for space science missions but are often contaminated by transient stray fields from spacecraft subsystems such as electrical and control units. Traditional mitigation approaches—including strict magnetic cleanliness programs, deployable long booms, and dual-sensor gradient systems—suffer from inherent limitations, as cleanliness programs and long booms impose high cost and system complexity. To overcome these challenges, we propose Multi-Source Adaptive Gradiometry (MSAG), an enhanced gradiometry technique that integrates a neural network-based interference classification framework. The trained network identifies interference types and applies adaptive correction coefficients, enabling accurate multi-source disturbance correction without requiring manual segmentation of long-duration data. We validate MSAG using realistic synthetic data, generated by superimposing key transient interference types—modeled from SMILE ground tests—onto actual THEMIS satellite measurements, and test it through 220 Monte Carlo simulations. MSAG reduces the median RMSE from 0.457 nT to 0.014 nT and achieves a median correlation coefficient of 0.999994 with the ground truth. This improved accuracy could alleviate constraints on magnetic cleanliness and boom length in future missions, highlighting the advantage of MSAG over conventional methods and underscoring the potential of combining machine learning with gradiometry for high-fidelity magnetic field recovery. Full article

Second-order damping oscillation models are incapable of precisely predicting superimposed and multi-fruit collision-induced oscillations. In view of this problem, an ARIMA-EEMD-LSTM hybrid model for predicting the oscillation trajectories of trellised tomatoes was proposed in this study. First, the oscillation trajectories of trellised tomatoes under different picking forces were captured with the aid of the Nokov motion capture system, and then the collected oscillation trajectory datasets were then divided into training and test subsets. Afterwards, the ensemble empirical mode decomposition (EEMD) method was employed to decompose oscillation signals into multiple intrinsic mode function (IMF) components, of which different components were predicted by different models. Specifically, high-frequency components were predicted by the long short-term memory (LSTM) model while low-frequency components were predicted by the autoregressive integrated moving average (ARIMA) model. The final oscillation trajectory prediction model for trellised tomatoes was constructed by integrating these components. Finally, the constructed model was experimentally validated and applied to an analysis of single-fruit oscillations and multi-fruit oscillations (including collision oscillations and superposition oscillations). The following experimental results were yielded: Under single-fruit oscillation conditions, the prediction accuracy reached an RMSE of 0.1008–0.2429 mm, an MAE of 0.0751–0.1840 mm, and an MAPE of 0.01–0.06%. Under multi-fruit oscillation conditions, the prediction accuracy yielded an RMSE of 0.1521–0.6740 mm, an MAE of 0.1084–0.5323 mm, and an MAPE of 0.01–0.27%. The research results serve as a reference for the dynamic harvesting prediction of tomato-picking robots and contribute to improvement of harvesting efficiency and success rates. Full article

Port berthing sites are regions of high interest for monitoring and optimizing port operations. Data sourced from the Automatic Identification System (AIS) can be superimposed on berths, enabling their real-time monitoring and revealing long-term utilization patterns. Ultimately, insights from multiple berths can uncover bottlenecks, and lead to the optimization of the underlying supply chain of the port and beyond. However, publicly available documentation of port berths, even when available, is frequently incomplete—e.g., there may be missing berths or inaccuracies such as incorrect boundary boxes—necessitating a more robust, data-driven approach to port berth localization. In this context, we propose an unsupervised spatial modeling method that leverages AIS data clustering and hyperparameter optimization to localize berthing sites. Trained on one month of freely available AIS data and evaluated across ports of varying sizes, our models significantly outperform competing methods, achieving a mean Bhattacharyya distance of 0.85 when comparing Gaussian Mixture Models trained on separate data splits, compared to 13.56 for the best existing method. Qualitative comparison with satellite images and existing berth labels further supports the superiority of our method, revealing more precise berth boundaries and improved spatial resolution across diverse port environments. Full article

The present study aimed to determine the acute effects of high-intensity dynamic resistance training (HI-DRT) and whole-body electromyostimulation (WB-EMS) on markers of bone formation and resorption in young healthy women. Using a crossover design, 17 students of dentistry (26.5 ± 4.0 years, 21.5 ± 2.5 kg/m²) were randomly assigned to begin either with HI-DRT (five exercises, three sets to repetition maximum) or 20 min of non-superimposed, low-frequency (85 Hz), intermitted (6 s impulse/4 s impulse break) WB-EMS. The study outcome parameters were total Procollagen Type-1 N-Terminal Propeptide (P1NP) and Type-I Collagen Cross-Linked C-Telopeptide (CTX), which were sampled immediately prior to and 15 min post intervention. ANCOVA was applied to determine the main effects, i.e., differences in pre–post changes in CTX and P1NP between the interventions. No participant was lost to follow-up or reported adverse effects related to the exercises. Briefly, we observed significant differences (p = 0.019, d′ = 1.19) for changes in P1NP that were maintained in the HI-DRT (p = 0.446) and decreased in the WB-EMS group (p = 0.002). In contrast, we did not observe differences for HI-DRT- vs. WB-EMS-induced CTX changes (p = 0.509; d′ = 0.134). In summary, while HI-DRT provides significantly more favorable effects on bone formation markers compared to WB-EMS, the clinical significance of this finding in predicting the general effectiveness of an exercise protocol on bone strength remains to be determined. (Clinical trials.gov; registration date: 2025-02-06; ID: NCT06813092.) Full article

Considering the limited availability of meteorological data in practice, this paper investigates the short-term photovoltaic output prediction problem based on data decomposition and error correction to further improve prediction accuracy. Firstly, according to the analysis of the variation characteristics of photovoltaic output data, the Seasonal and Trend decomposition using Loess (STL) method is used to decompose the original data into three components: seasonal term, trend term, and residual term. Considering that the variation patterns of different components are different, based on the division of the dataset, temporal convolutional network (TCN)-based prediction models for each component are constructed separately, and the prediction results are superimposed to obtain the predicted value of the photovoltaic output. Secondly, an error dataset is constructed based on the prediction errors of the training set and validation set, and a TCN error prediction model is established. The error prediction value is used as compensation to correct the photovoltaic output prediction value, and the final photovoltaic output prediction value is obtained. Finally, based on the measured photovoltaic output data of a certain region in China, the effectiveness and advancement of the proposed method are demonstrated through the ablation and comparative experiments. Full article

Background/Objectives: Anxiety before ophthalmic surgery under local anesthesia may hinder patient cooperation and surgical outcomes. Nurse-led auditory interventions offer a promising non-pharmacological approach to perioperative anxiety management. This study evaluated the effectiveness of superimposed binaural beats (SBBs)—classical music layered with frequency differentials—in reducing anxiety during pterygium surgery with conjunctival autografting. Methods: In this randomized controlled trial, 111 adult patients scheduled for elective pterygium excision with conjunctival autografting under local anesthesia were allocated to one of three groups: SBBs, plain music (PM), or silence (control). A trained perioperative nurse administered all auditory interventions. The patients’ anxiety was assessed using the State–Trait Anxiety Inventory—State (STAI-S), and physiological parameters (blood pressure, heart rate, respiratory rate, and oxygen saturation) were recorded before and after surgery. Results: The SBB group showed significantly greater reductions in their STAI-S scores (p < 0.001), systolic blood pressure (p = 0.011), heart rate (p = 0.003), and respiratory rate (p = 0.009) compared to the PM and control groups. No adverse events occurred. Conclusions: SBBs are a safe, nurse-delivered auditory intervention that significantly reduces perioperative anxiety and supports physiological stability. Their integration into routine nursing care for minor ophthalmic surgeries is both feasible and beneficial. Trial Registration: This study was registered with the Thai Clinical Trials Registry (TCTR) under registration number TCTR20250125002 on 25 January 2025. Full article

It has been demonstrated that needle guidance systems can enhance the precision and safety of ultrasound-guided punctures in human medicine. Systems that permit the utilization of commercially available standard needles, instead of those that necessitate the acquisition of costly, proprietary needles, are of particular interest. The objective of this phantom study is to evaluate the reliability and accuracy of magnet-based ultrasound needle guidance systems, which superimpose the position of the needle tip and a predictive trajectory line on the live ultrasound image. We conducted fine-needle aspiration cytology of thyroid nodules. The needles utilized in these procedures are of a slender gauge (21–27G), with lengths ranging from 40 to 80 mm. A dedicated training workstation with integrated software-based analyses of the movement of the needle tip was utilized in 240 standardized phantom punctures (angle: 45°; target depth: 20 mm). No system failures occurred, and the target achieved its aim in all cases. The analysis of the software revealed stable procedural parameters with minor relative deviations from the predefined reference values regarding the distance of needle tip movement (−4.2% to +6.7%), needle tilt (−6.4% to +9.6%), and penetration depth (−7.5% to +4.5%). These deviations appeared to increase with the use of thin needles and, to a lesser extent, long needles. They are attributed to the slight bending of the needle inside the (phantom) tissue. The training workstation we employed is thus suitable for use in educational settings. Nevertheless, in intricate clinical puncture scenarios—for instance, in the case of unfavorable localized small lesions near critical anatomical structures, particularly those involving thin needles—caution is advised, and the system should not be relied upon exclusively. Full article

This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). To resolve this issue, an intelligent spiral maneuver trajectory generation method utilizing a virtual trajectory framework is proposed. Initially, a relative motion model between the UAV and the virtual center of mass (VCM) is established based on the geometric principles of the Archimedean spiral. Subsequently, the interaction dynamics between the VCM and the target are formulated as a Markov decision process (MDP). A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes. Full article

Training using instability devices is common; however, for highly trained athletes, a single device may not provide sufficient challenge. This study examines the effect of superimposed vibration in suspended kneeling rollout. Seventeen physically active participants performed the exercise with non-vibration, vibration at 25 Hz, and vibration at 40 Hz. Muscle activation of the pectoralis clavicularis, pectoralis sternalis, anterior deltoid, serratus anterior, infraspinatus, and latissimus dorsi was recorded during exercise, and the perception of effort was recorded after exercise (OMNI-Res scale). One-way repeated-measures analysis of variance (ANOVA) showed significant differences for the kneeling rollout (p < 0.05). Friedman’s test showed significant differences in the OMNI-Res (p = 0.003). Pairwise comparison showed significant differences in the anterior deltoid (p = 0.004), latissimus dorsi (p < 0.001), infraspinatus (p = 0.001), and global activity (p < 0.001) between the 25 Hz and non-vibration conditions. It also showed significant differences between the 40 Hz and non-vibration conditions for pectoralis sternalis (p = 0.021), anterior deltoid (p = 0.005), latissimus dorsi (p < 0.001), infraspinatus (p = 0.027), and global activity (p < 0.001). The post hoc Conover pairwise comparison showed significant differences in the OMNI-Res only between the non-vibration and vibration at 40 Hz conditions (p = 0.011). Superimposed vibration increases the muscle activation of the upper limbs when performing the suspended kneeling rollout. Full article

Rapidly growing wildfires have recently devastated societal assets, exposing a critical need for early warning systems to expedite relief efforts. Smoke detection using camera-based Deep Neural Networks (DNNs) offers a promising solution for wildfire prediction. However, the rarity of smoke across time and space limits training data, raising model overfitting and bias concerns. Current DNNs, primarily Convolutional Neural Networks (CNNs) and transformers, complicate robustness evaluation due to architectural differences. To address these challenges, we introduce WARP (Wildfire Adversarial Robustness Procedure), the first model-agnostic framework for evaluating wildfire detection models’ adversarial robustness. WARP addresses inherent limitations in data diversity by generating adversarial examples through image-global and -local perturbations. Global and local attacks superimpose Gaussian noise and PNG patches onto image inputs, respectively; this suits both CNNs and transformers while generating realistic adversarial scenarios. Using WARP, we assessed real-time CNNs and Transformers, uncovering key vulnerabilities. At times, transformers exhibited over 70% precision degradation under global attacks, while both models generally struggled to differentiate cloud-like PNG patches from real smoke during local attacks. To enhance model robustness, we proposed four wildfire-oriented data augmentation techniques based on WARP’s methodology and results, which diversify smoke image data and improve model precision and robustness. These advancements represent a substantial step toward developing a reliable early wildfire warning system, which may be our first safeguard against wildfire destruction. Full article

Objectives: Increasing exercise intensity and performance output with superimposed vibration gains interest, especially in high-performance training. However, the additional benefit of vibration in passive stretching exercises and its mechanisms remain unclarified. Methods: Passive stretching with (ST+V) and without (ST) vibration (20 Hz) was performed in male Olympic youth skiing athletes (n = 8, age: 17.9 ± 1.0 years) using a single-blinded randomized cross-over design. Acute hip abduction, hip anteversion, knee extension, and hamstrings (stand and reach straight leg raise) range of motion (ROM) were assessed using a digital goniometer, while stiffness was examined via MyotonPRO. The skin temperature of the whole leg was captured with infrared thermography and analyzed in different segments. Results: Both stretching interventions increased ROM compared to the control group (CG) (p < 0.001–0.033, d = 1.0–1.6) without differences between ST+V and ST (p = 0.202–0.999). While skin temperature decreased in the CG and ST, ST+V maintained a constant temperature in the lower legs. Stiffness was not affected by both stretching interventions. Conclusions: The stretching intervention leads to significant increases in flexibility, while additional vibration did not further enhance the ROM. Full article

Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 imagery. Our method involves three key aspects: color-distortion simulation, model design, and post-processing refinement. First, we investigate the causes of color distortion and propose algorithms to simulate this phenomenon. By superimposing simulated color-distortion patterns onto clean images, we construct color-distortion datasets comprising a large number of paired images (distorted–clean) for model training. Next, we analyze the principles behind a denoising model and explore its feasibility for color-distortion correction. Based on this analysis, we train the denoising model from scratch using the color-distortion datasets and successfully adapt it to the task of color-distortion correction in Jilin-1 KF01 imagery. Finally, we propose a novel post-processing algorithm to remove boundary artifacts caused by block-wise image processing, ensuring consistency and quality across the entire image. Experimental results show that the proposed method significantly eliminates color distortion and enhances the radiometric quality of Jilin-1 KF01 series satellite imagery, offering a solution for improving its usability in remote sensing applications. Full article

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of −528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions. Full article