Search Results (151)

Background/Objectives: Biofeedback interventions are increasingly utilized in pediatric and adult care, with evidence in treating specific medical conditions and specific symptoms. However, evidence supporting their efficacy among children and adolescents and young adults (AYAs, aged 15–39) with cancer is limited. The aims of this systematic review are to present, assess, and synthesize the existing research on biofeedback in pediatric and AYA oncology, identify gaps in biofeedback research within this population, and provide recommendations for future research and clinical implications. Methods: A systematic search for articles was conducted using six bibliographic databases—PubMed/MEDLINE (NLM), EMBASE (Elsevier), CINAHL (EBSCO), SPORTDiscus (EBSCO), PsycINFO (OVID), and PEDro (NeuRA)—with an update on 5/7/2025. Included were studies involving pediatric/AYA oncology participants (0–39 years old) and those receiving at least one biofeedback modality. The methodological quality and risk of bias among included articles were assessed using the Cochrane Risk of Bias (ROB) Tool (modified version for non-randomized studies). A narrative synthesis of included studies examined the type of cancer studied, type of biofeedback used, study designs and methodological quality, and key outcomes evaluated. Results: While the literature suggests that biofeedback may offer beneficial outcomes for managing various pediatric/AYA oncology-related symptoms, such as pain, anxiety, and fatigue, only 8 studies out of 1013 screened (<1%) met inclusion criteria. Limitations included low study quality (small sample sizes, lack of control groups, and methodological inconsistencies). Conclusions: While biofeedback shows promise as a feasible and effective intervention, there is a call to action for well-designed, methodologically rigorous studies to substantiate its effectiveness and inform evidence-based practice specifically for pediatric/AYA oncology patients and clinicians. Full article

Background: Refractory ventricular fibrillation (RVF) is a life-threatening condition characterized by the persistence of ventricular fibrillation despite multiple defibrillation attempts. It represents a critical challenge in out-of-hospital cardiac arrest management, with poor survival outcomes and limited guidance from current resuscitation guidelines. In recent years, alternative defibrillation strategies (ADSs), including dual sequential external defibrillation (DSED) and vector change defibrillation (VCD), have emerged as potential interventions to improve defibrillation success and patient outcomes. However, their clinical utility remains debated due to heterogeneous evidence and limited high-quality data. Methods: This narrative review explores the current landscape of ADSs in patients with RVF. MEDLINE, Google Scholar, the World Health Organization, LitCovid NLM, EMBASE, CINAHL Plus, and the Cochrane Library were examined from their inception to April 2025. Results: The available literature is dominated by retrospective studies and case series, with only one randomized controlled trial (DOSE-VF). This trial demonstrated improved survival to hospital discharge with ADSs compared to standard defibrillation. DSED was associated with higher rates of return of spontaneous circulation and favorable neurological outcomes. However, subsequent meta-analyses have produced inconsistent results, largely due to the heterogeneity of the included studies. The absence of sex-, gender-, and ethnicity-specific analyses further limits the generalizability of the findings. In addition, practical barriers, such as equipment availability, pose significant challenges to implementation. Conclusions: ADSs represent a promising yet still-evolving approach to the management of RVF, with DSED showing the most consistent signal of benefit. Further high-quality research is required to enhance generalizability and generate more definitive, high-level evidence. Full article

Wheelchair tennis (WT) is a Paralympic sport designed for athletes with physical impairments. Assessing physical fitness characteristics using appropriate field-based tests and standardized protocols is essential for individualized training, injury prevention, and performance monitoring. However, there is currently limited information on which field-based tests are most suitable and how they should be applied in WT athletes, resulting in inconsistency across studies and practical use. Establishing a standard operating procedure (SOP) enables replicable, cost-effective testing routines that improve data consistency and comparability. We conducted a scoping review to synthesize the existing evidence on field-based physical fitness assessment in WT athletes and to propose a structured SOP for these tests. A comprehensive search was conducted in three electronic databases—NLM PubMed, Web of Science, and Scopus—using predefined keywords and Boolean operators. The inclusion criteria were limited to peer-reviewed, English-language original articles focusing exclusively on field tests in WT athletes. Studies with other populations, reviews, and abstracts were excluded. Eleven studies met the eligibility criteria. This scoping review identified various field tests assessing key fitness components, including cardiorespiratory endurance, muscle strength, agility, and body composition. The most frequently employed tests were the 20 m sprint test, isometric handgrip test, spider test, Illinois Agility Test, and skinfold thickness. These findings highlight the lack of standardized fitness assessments in WT. The proposed SOP offers a practical step toward consistent, replicable, and relevant evaluation in these athletes. Full article

Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. It plays a crucial role in applications such as medical imaging, surveillance, and remote sensing. However, due to the ill-posed nature of the task and the inherent limitations of imaging sensors, obtaining accurate HR images remains challenging. While numerous methods have been proposed, the traditional approaches suffer from oversmoothing and limited generalization; CNN-based models lack the ability to capture long-range dependencies; and Transformer-based solutions, although effective in modeling global context, are computationally intensive and prone to texture loss. To address these issues, we propose a hybrid CNN–Transformer architecture that cascades a pixel-wise self-attention non-local means module (PSNLM) and an adaptive dual-path multi-scale fusion block (ADMFB). The PSNLM is inspired by the non-local means (NLM) algorithm. We use weighted patches to estimate the similarity between pixels centered at each patch while limiting the search region and constructing a communication mechanism across ranges. The ADMFB enhances texture reconstruction by adaptively aggregating multi-scale features through dual attention paths. The experimental results demonstrate that our method achieves superior performance on multiple benchmarks. For instance, in challenging ×4 super-resolution, our method outperforms the second-best method by 0.0201 regarding the Structural Similarity Index (SSIM) on the BSD100 dataset. On the texture-rich Urban100 dataset, our method achieves a 26.56 dB Peak Signal-to-Noise Ratio (PSNR) and 0.8133 SSIM. Full article

The modular multilevel matrix converter (M3C) can perform AC/AC conversion directly. However, M3C operation often requires a pre-charging process, which can be challenging due to the need for fast pre-charging with low inrush current. To address the issue, a closed-loop fast pre-charging strategy is proposed that utilizes an improved nearest level modulation (NLM) based on a quick-sorting algorithm for M3C. By improving the current limiting resistor and the number of Sub-Modules (SMs) inserted into the NLM, we achieve a reduction in inrush current when connected to the grid, and unlock the control algorithm, respectively. This paper presents the relationship between the current-limiting resistor, the pre-charging current, and the pre-charging time. Reactive power compensation is applied on the AC grid during the pre-charging process to ensure stability. Furthermore, the balanced control of capacitor voltage is employed to achieve synchronized and coordinated growth of capacitor voltages in SMs using a quick-sorting algorithm based on NLM. The simulation and experimental results demonstrate the effectiveness of this approach, making it suitable for M3C with a high number of SMs. Full article

Over the last decades, plenty of ancient manuscripts have been digitized all over the world, and particularly in Europe. The fruition of these huge digital archives is often limited by the bleed-through effect due to the acid nature of the inks used, resulting in very noisy images. Several authors have recently worked on bleed-through removal, using different approaches. With the aim of developing a bleed-through removal tool, capable of batch application on a large number of images, of the order of hundred thousands, we used machine learning and robust statistical methods with four different methods, and applied them to two medieval manuscripts. The methods used are (i) non-local means (NLM); (ii) Gaussian mixture models (GMMs); (iii) biweight estimation; and (iv) Gaussian blur. The application of these methods to the two quoted manuscripts shows that these methods are, in general, quite effective in bleed-through removal, but the selection of the method has to be performed according to the characteristics of the manuscript, e.g., if there is no ink fading and the difference between bleed-through pixels and the foreground text is clear, we can use a stronger model without the risk of losing important information. Conversely, if the distinction between bleed-through and foreground pixels is less pronounced, it is better to use a weaker model to preserve useful details. Full article

Given that the fault information of motor bearing is submerged due to strong noise under variable load conditions, a fault diagnosis method of motor bearing based on parameter optimization variational mode decomposition (VMD) and normalized least mean square (NLMS) is proposed. Firstly, VMD’s modal number K and α penalty factor are optimized by symbolic dynamic entropy (SDE). Then, the VMD algorithm with optimized parameters is used to extract the fault signals of bearing inner and outer rings under different load conditions. Then, the appropriate intrinsic mode decomposition (IMF) is selected, according to the weighted kurtosis index to reconstruct the fault feature signals. Finally, the NLMS algorithm reduces noise in the reconstructed signal and highlights the fault characteristics. The fault characteristics are analyzed by envelope demodulation. The RMSE and SNR of the simulated signal are calculated by filtering the improved method. It is found that the RMSE of the filtered signal is reduced 60%, and the signal-to-noise ratio is increased by about 119.87%. Compared to the sparrow search algorithm (SSA)-optimized VMD method, the proposed approach shows significant improvements in fault feature extraction. This study provides an effective solution for motor bearing fault diagnosis in noisy and variable load environments. Full article

Magnetic sensors are highly relevant in clinical and industrial applications such as localization tasks and geological investigations. The spatial behavior of these sensors is of great interest for accurate forward modeling and the consequential possibilities for sophisticated applications, e.g., solutions to inverse problems. In this contribution, we present a novel characterization approach using adaptive system identification approaches. We utilize a gradient-based algorithm for estimating impulse and corresponding frequency responses for a directivity analysis in 1D, 2D, and 3D. For this, we built a triaxial Helmholtz coil setup to generate a 3D directive field. This is controlled by an algorithm that exploits similarities in sensor behavior with respect to small differences in excitation field angles. We found advantages for a controlled adaptation, with faster convergence and a smaller system distance between estimations and measurements with a proposed control based on the contraction–expansion approach (CEA). With runtimes averaging less than 1.5 s per direction for full impulse response estimation, this proof of concept shows the potential of the proposed algorithm for enabling a feasible frequency and directivity characterization method. Full article

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems. Full article

To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM–Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems. Full article

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). Full article

The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field. Full article

Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis. Full article

Clopidogrel, a prescription drug to reduce ischemic events in cardiovascular patients, has been extensively studied in mostly European individuals but not among Caribbean Hispanics. This study evaluated the low abundance and reduced activity of paraoxonase-1 (PON1) in clopidogrel-resistant patients as a predictive risk biomarker of poor responders and disease severity in this population. Thirty-six patients on clopidogrel (cases divided into poor and normal responders) were enrolled, along with 11 cardiovascular patients with no clopidogrel indications (positive control) and 13 healthy volunteers (negative control). Residual on-treatment platelet reactivity unit (PRU), PON1 abundance by Western blotting, and PON1 activity by enzymatic assays were measured. PON1 genotyping and computational haplotype phasing were performed on 512 DNA specimens for two genetic loci (rs662 and rs854560). No statistical differences in mean relative PON1 abundance were found among the groups (p > 0.05). However, a significantly lower enzymatic activity was found in poor responders (10.57 ± 6.79 µU/mL) when compared to controls (22.66 ± 8.30 µU/mL and 22.21 ± 9.66 µU/mL; p = 0.004). PON1 activity among carriers of the most prevalent PON1 haplotype (AA|AA) was significantly lower than in wild types (7.90 µU/mL vs. 22.03 µU/mL; p = 0.005). Our findings suggested that PON1 is a potential biomarker of cardiovascular disease severity in Caribbean Hispanics. Full article

Digital holography is actively used for the characterization of objects and 3D-scenes, tracking changes in medium parameters, 3D shape reconstruction, detection of micro-object positions, etc. To obtain high-quality images of objects, it is often necessary to register a set of holograms or to select a noise suppression method for specific experimental conditions. In this paper, we propose a method to improve filtering in digital holography. The method requires a single hologram only. It utilizes interpolation upscaling of the reconstructed image size, filtering (e.g., median, BM3D, or NLM), and interpolation to the original image size. The method is validated on computer-generated and experimentally registered digital holograms. Interpolation methods coefficients and filter parameters were analyzed. The quality is improved in comparison with digital image filtering up to 1.4 times in speckle contrast on the registered holograms and up to 17% and 29% in SSIM and NSTD values on the computer-generated holograms. The proposed method is convenient in practice since its realization requires small changes of standard filters, improving the quality of the reconstructed image. Full article