Comparative evaluation of radiofrequency-treated and naturally aged rice (Var. Jhelum): Physicochemical, rheological, thermal, microstructural and glycemic characteristics.

Non-invasive radio frequency (RF)-driven in-process monitoring and control for enhancing micro-electrical discharge machining stability and efficiency

Radiofrequency therapy attenuates hypertrophic scar formation in rabbit ears by modulating the TGF-β1/Smad3 signaling pathway.

Radio frequency converter (RFC) is widely used in semiconductor manufacturing, demanding high power levels, high efficiencies, and wide power regulation ranges. However, existing solutions face issues like discrete regulation ranges, low efficiencies at low power levels due to high reactive power and hard switching, or complex auxiliary circuits for balancing the power regulation range and efficiency. This paper proposes an efficient modular power amplifier architecture with multiple RFC modules using full-bridge class-D power amplifiers (PAs). Its individual modules' constant-current characteristics and system-level power transmission are analyzed. A hybrid modulation strategy combining module number adjustment and DC voltage control enables wide-range power regulation without auxiliary circuits. A 3-kW four-module RFC prototype tested at 400 kHz shows output power levels from 100 W to 2.8 kW with a peak efficiency of 93.7% and low current harmonics, validating its superior performance in practice.

Effect of radio frequency power and vacuum heat treatment on the microstructure and corrosion performance of AlCrTiV films

Background: Lichen sclerosus (LS) is a chronic inflammatory dermatosis affecting anogenital skin, often associated with vulvar pain, sexual dysfunction, and increased risk of malignancy. Despite its significant impact on quality of life, there is no definitive cure. This case series explores a multimodal regenerative approach with promising potential to improve symptoms and patient well-being. Objective: To evaluate the clinical benefits of combining energy-based modalities with either exosomes or platelet-rich plasma (PRP) in the treatment of LS, aiming to achieve synergistic regenerative effects. Methods: In this prospective, single-center case series, ten patients (n=10, 9 women, 1 man, 39-88 years) were enrolled. Each patient received an individualized treatment protocol consisting of monopolar radiofrequency (RF), fractional RF, and either exosomes or PRP. Up to three treatment sessions were administered per patient, spaced 14 days apart. Results: Patients reported significant symptomatic improvement following treatment. Distress related to inability to orgasm dropped by 100%. Relief from itching and burning was reported by 88% of patients, 80% were able to discontinue steroid use, and 75% experienced improvement in vaginal dryness. Conclusion: This case series demonstrates notable clinical improvements across a range of patient-reported outcomes following combination regenerative therapy for LS. Observed outcomes show reductions or complete resolution of symptoms such as vaginal dryness, atrophy, itching, burning, and interference with daily activities. Additionally, several patients reported improvements in sexual health, contributing to an overall enhancement in quality of life.

High power and high isolation transmit/receive switch for ultra-high field whole-body magnetic resonance imaging.

Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator.

Background/Objectives: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health crisis, but current diagnostics are limited. Liver biopsy is invasive, magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is expensive, and quantitative ultrasound methods are low-accuracy, especially in patients with a high body mass index (BMI). This study introduces a novel thermo-acoustic (TA) method that generates ultrasound signals based on tissue electrical conductivity, where lean tissue (high in water and electrolytes) absorbs more radio-frequency (RF) energy than fatty tissue, providing a direct molecular contrast for fat. Methods: A prospective, cross-sectional feasibility study compared a new thermo-acoustic fat fraction (TAFF) score with the reference standard MRI-PDFF in 40 subjects with suspected fatty liver disease. Bland-Altman analysis, Deming regression, and Binary classification performance were tested. To establish system stability, a dedicated Repeatability and Reproducibility (R&R) study (N = 14) evaluated inter-operator and intra-operator consistency using an Intraclass Correlation Coefficient (ICC) derived from a two-way random-effects ANOVA model. Results: TAFF estimates demonstrated a substantial correlation (r = 0.89) with MRI-PDFF and an average absolute error of 3.04% fat fraction. Classification performance was high, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 at the 12% fat fraction threshold and 0.99 at the 20% fat fraction threshold. The R&R study confirmed robust stability (intraclass correlation = 0.89) and a negligible mean inter-operator difference of 0.36%. Estimation errors showed no statistically significant correlation with BMI or other body habitus measurements. Conclusions: These findings support thermoacoustics' potential as an accurate, non-invasive, point-of-care solution that can serve as a new imaging biomarker. By providing predictive values closely aligned with MRI-PDFF across the full MASLD spectrum, TAFF can complement currently available ultrasound methods to address the cost and access constraints of MRI for the assessment, diagnosis, and monitoring of MASLD.

Mitral regurgitation (MR) is the most common valvular disease in developed countries. Many patients with functional MR (FMR) are inoperable due to high surgical risk, and effective transcatheter options are limited. Radiofrequency (RF) ablation can contract connective tissue. To assess the feasibility, safety, and efficacy of Biorefine, a novel transcatheter RF ablation system for mitral annulus remodeling without implants, in a preclinical ovine model. Twelve sheep underwent transseptal RF ablation of the posterior mitral annulus under 3D echocardiographic and fluoroscopic guidance. Mitral geometry and coaptation were evaluated acutely (n = 4), at 58days (n = 4), and 178days (n = 4). Histology assessed tissue effects. All procedures were complication-free. Eleven of 12 animals showed ≥ 15% annular area reduction (mean 21.8%), ≥ 10% A-P diameter reduction (mean 15%), and 71.9% mean coaptation increase. Histology confirmed localized fibrosis (≤ 3mm) without off-target injury. Biorefine appears safe, durable, and implant-free for FMR.

Abstract K2-18 b, a sub-Neptune exoplanet located in the habitable zone of its host star, has emerged as an important target for atmospheric characterization, and assessments of potential habitability. Motivated by recent interpretations of JWST observations suggesting a hydrogen-rich atmosphere consistent with Hycean-world scenarios, we conducted a coordinated, multiepoch search for narrowband radio technosignatures using the Karl G. Jansky Very Large Array equipped with the COSMIC backend and the MeerKAT telescope with the BLUSE backend. Our observations span frequencies from 544 MHz to 9.8 GHz and include multiple epochs that cover at least one full orbital period of the planet. In this work we outline, create, and apply a comprehensive postprocessing framework that incorporates observatory-informed radio frequency interference masking, drift-rate filtering based on the expected dynamics of the K2-18 system, multibeam spatial discrimination, primary and secondary transit filtering (when applicable), and signal-to-noise-ratio-based excision of weak and strong spurious signals. Across all bands and epochs, no signals consistent with an astrophysical or artificial origin were identified at a limit of 10 12 –10 13 W. These nondetections allow us to place upper limits on the presence of persistent, isotropic narrowband transmitters within the K2-18 system, providing the first interferometric technosignature constraints for a Hycean-planet candidate. Our results demonstrate the efficacy of coordinated multiepoch interferometric searches and establish a methodological framework for future technosignature studies of nearby potentially habitable exoplanets.

Abstract This study examined the gamma and neutron radiation shielding performance of AISI 304, 310, 316L, and 430 stainless steels, which were coated with boron carbide using the radio frequency magnetron sputtering technique. Coating durations were varied (7, 12, and 14 h) to determine optimal film growth conditions. Structural, morphological, and compositional analyses of the coatings were conducted using energy-dispersive X-ray (EDX), X-ray diffraction (XRD), and scanning electron microscope (SEM) techniques. Gamma attenuation properties were evaluated both theoretically, using WinXCom software, and experimentally via narrow-beam transmission measurements using 241 Am, 133 Ba, and 137 Cs radioactive sources. Key shielding parameters, including mass attenuation coefficient, effective atomic number (Z eff ), effective electron density (N eff ), half-value layer, and mean free path were calculated. Neutron shielding effectiveness was experimentally assessed using a 241 Am-Be neutron source and theoretically evaluated using MRCsC software. Results showed that B 4 C coatings improved neutron absorption by 4% to 12%, with AISI 316L and 304 exhibiting the highest macroscopic removal cross sections. The findings demonstrate that B 4 C coatings significantly enhance the radiation shielding performance of stainless steels without compromising their mechanical integrity, offering a viable, lead-free solution for nuclear and medical radiation protection applications.

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to 88.52% average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a 5% performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of 96.71% is achieved on experimentally collected test data from an unseen location.

Orbital angular momentum (OAM) is a fundamental property of light, with widespread applications across various fields, from quantum mechanics to advanced imaging and telecommunications. The inherent orthogonality of OAM beams allows information multiplexing in unique, high-dimensional states. However, conventional OAM-based systems encounter long-standing integration and scalability challenges due to the heavy reliance on complex optical components and redundant radio frequency chains for multi-channel transmission. Here, we propose a dual-polarized asynchronous space-time-coding metasurface (DASM) for producing coaxial OAM beams in multiple physical domains through a single aperture. By synergistically combining OAM, polarization, and frequency division multiplexing, DASM constructs a high-dimensional communication framework, dramatically increasing the number of supported channels. Notably, DASM further streamlines the framework by directly modulating the information carried by OAM beams, eliminating the need for complicated external modulators. The high-dimensional multiplexing framework offers a simplified, versatile, and efficient solution for substantial development in wireless communications capacity and scalability.

Method for classification of UAV flight control RF signals based on multi-scale divergence entropy and optimized neural networks.

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar's environmental robustness alongside UWB's precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system's potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety.

This study presents a comprehensive investigation of the transmission characteristics and filtering performance of a tailored one-dimensional rectangular waveguide structure (1D-RWS). The research employs a combined analytical-numerical approach. The system is first modeled theoretically using the Green's function method (GFM) to elucidate the underlying physical mechanisms and then validated through rigorous finite element method (FEM) simulations. A key focus of the work is to explore the influence of critical geometric parameters, including resonator length, lateral position, and width, on the filter's spectral response. The results demonstrate that the transmission spectra and resonant frequencies are highly tunable, exhibiting phenomena such as resonant frequency shifts, critical coupling, and variations in bandwidth with geometric modifications. These findings confirm that the proposed 1D structure provides a versatile platform for controlling electromagnetic wave propagation. Consequently, this waveguide can be considered a bandpass filter for applications in the radio-frequency spectrum, offering a design principle for integrated radio-wave circuits and communication systems.

Quantum networks will rely on photons entangled to robust, local quantum registers for computation and error correction. We demonstrate control of and entanglement in a fully connected three-qubit 13C nuclear spin register in diamond. The register is coupled to a quasi-free electron spin-1/2 of a silicon-vacancy center (SiV). High strain decouples the SiVs electron spin from spin-orbit interaction reducing the susceptibility to phonons at liquid helium temperature. As a result, the electron spin lifetime of hundreds of milli seconds enables sensing of nuclear-nuclear couplings down to few hertz. To detect and control the register we leverage continuous decoupling using shaped, low-power microwave and direct radio frequency driving. Furthermore, we implement a nuclear spin conditional phase-gate on the electron spin to mediate bipartite entanglement. This approach presents an alternative to dynamically decoupled nuclear spin entanglement, not limited by the electron spin-1/2's nature, opening up new avenues to an optically-accessible, solid-state quantum register.

Ultrashort electron bunches with precise pump laser synchronization and adequate charge are fundamental for time-resolved techniques like ultrafast electron diffraction (UED) and advanced light sources. Here, we achieve electron chopping and compression through a single terahertz (THz)-driven device, producing 50-femtosecond, near-femtocoulomb (fC) electron pulses with 1.4-femtosecond arrival time jitter sustained over an hour. This achieves a 35-fold pulse duration reduction and 10-fold jitter improvement compared to cases without the THz device, marking the shortest directly measured pulse duration and smallest timing jitter reported for multielectron pulses (near-fC level) at the kilo-electron volt level. High-quality electron diffraction confirms this technique's suitability for UED. We further introduce a beam tilting correction via a tilted antenna, enabling bunch compression down to 6 femtoseconds. Furthermore, we have demonstrated its capability of reducing extended timing jitter from several hundred femtoseconds to the few-femtosecond level, offering a promising solution to the long-standing synchronization challenge between radio frequency accelerators and optical lasers. This advancement paves the way for jitter-free UED techniques with attosecond temporal resolution, thereby notably enhancing modern ultrafast applications.

The automatic modulation recognition (AMR) of low probability intercept (LPI) signals has received a considerable amount of interest from many researchers who have done much work on electronic reconnaissance. This recognition technology aims to design a classifier that enables the identification of signals with different modulation types. Based on deep learning models such as a convolutional neural network (CNN), the time-frequency images (TFIs) of the signal can be input to further extract features for classification. To improve recognition accuracy, especially under low signal-to-noise ratios (SNRs), we propose an AMR method for radio frequency proximity sensor signals based on a TFI enhancement network. The TFIs are denoised based on a per-pixel kernel prediction network (KPN), which can improve the quality of TFIs and achieves comparable denoising performance to traditional TFI reconstruction methods (e.g., sparse representation-based methods and low-rank approximation methods), while requiring significantly less computational overhead. The denoised TFIs, with enhanced signal quality and reduced noise, are then fed into the RetinalNet-based classifier as high-quality input features. This enhancement is crucial for the subsequent recognition stage, as it significantly improves the modulation recognition accuracy, particularly under challenging low SNR conditions. Simulation results show that the proposed method can accurately identify the modulation types of different radio frequency proximity sensors that are aliased in the time-frequency domain under low SNRs, and the average recognition accuracy rate of the signal remains above 97% when the signal-to-noise ratio is above -10 dB.