Search Results (36,687)

The flywheel energy storage system (FESS) of a mechanical bearing is utilized in electric vehicles, railways, power grid frequency modulation, due to its high instantaneous power and fast response. However, the lifetime of FESS is limited because of significant frictional losses in mechanical bearings and challenges associated with passing the critical speed. To suppress the unbalanced response of FESS at critical speed, a damping ring (DR) device is designed for a hybrid supported FESS with mechanical bearing and axial active magnetic bearing (AMB). Initially, the dynamic model of the FESS with DR is established using Lagrange’s equation. Moreover, the dynamic parameters of the DR are obtained by experimental measurements using the method of free vibration attenuation. Finally, the influence of the DR device on the critical speed and unbalanced response of FESS is analyzed. The results show that the designed DR device can effectively reduce the critical speed of FESS, and increase the first and second mode damping ratio. The critical speed is reduced from 13,860 rpm to 5280 rpm. Compared with FESS of the mechanical bearing, the unbalanced response amplitude of the FESS with DR is reduced by more than 87.8%, offering promising technical support for the design of active and passive control systems in FESS. Full article

To improve the flux regulation range of the Axial–Radial Flux Hybrid Excitation Machine (ARFHEM) and the utilization rate of permanent magnets (PMs), the effects of different length–diameter ratios (LDRs) on the ARFHEM performance are studied. Firstly, the principle of the flux regulation of the ARFHEM is introduced by means of the structure and equivalent magnetic circuit method. Then, based on the principle of the bypass effect, the analytical formulas of LDRs, the number of pole-pairs, and the flux regulation ability are derived, and then the restrictive relationship between the air-gap magnetic field, LDR, and the number of pole-pairs is revealed. On this basis, the influence of an electric LDR on motor performance is studied. By comparing and analyzing the air-gap magnetic density and no-load back electromotive force (EMF) of motors with different LDRs, the variation in the magnetic flux regulation ability of motors with different LDRs is obtained and its influence mechanism is revealed. In addition, the torque regulation ability and loss of motors with different LDRs are compared and analyzed, and the influence mechanism of the LDR on torque and loss is determined. Finally, the above analysis is verified by experiments. Full article

Cryptomeria japonica is a commercially important tree native to Japan. The tree belongs to the ancient genus Cryptomeria and has found important uses as a medicinal plant, as well as a main source of timber in Japan. In recent years, there has been an increased interest in discovering extended uses of C. japonica as a source of novel bioactive natural products with potential applications as lead compounds for active principles of future drugs. The compounds were isolated by a combination of two-phase extraction, XAD-7 Amberlite column chromatography, Sephadex LH-20 column chromatography and preparative High Performance Liquid Chromatography (HPLC). The structures were determined by a combination of several 1D and 2D Nuclear Magnetic Resonance (NMR) experiments and high-resolution mass spectrometry. Here, we report on the isolation and characterization of the novel biflavone glucoside hinokiflavone 7″-O-β-glucopyranoside, in addition to sixteen known compounds including the flavonols quercetin, quercetin 3-O-α-rhamnopyranoside and quercetin 3-O-β-galactopyranoside, the dihydroflavonols taxifolin 3-O-β-glucopyranoside, taxifolin 7-O-β-glucopyranoside, the flavanones naringenin, naringenin 7-O-β-galactopyranoside and eriodictyol 4′-O-β-glucopyranoside, the flavanol catechin, the biflavonoid amentoflavone, the dihydrochalcone phloretin 2′-O-β-glucopyranoside, the sesquiterpenoid roseoside, the polyphenolic compounds chlorogenic acid, methyl chlorogenate and the flavanocoumarins catechin-(7,8)-7″-(3,4 dihydroxyphenyl)-dihydro-8″(3H)-pyranone, and mururin A. The compounds exhibited low-to-moderate cytotoxic activity against MOLM-13 leukemia cells. Full article

We demonstrate a method to characterize the beam energy, transverse profile, charge, and dose of a pulsed electron beam generated by a 1 kHz TW laser-plasma accelerator. The method is based on imaging with a scintillating screen in an inhomogeneous, orthogonal magnetic field produced by a wide-gap magnetic dipole. Numerical simulations were developed to reconstruct the electron beam parameters accurately. The method has been experimentally verified and calibrated using a medical LINAC. The energy measurement accuracy in the 6–20 MeV range is proven to be better than 10%. The radiation dose has been calibrated by a water-equivalent phantom, RW3, showing a linear response of the method within 2% in the 0.05–0.5 mGy/pulse range. Full article

Wearable communication technologies necessitate antenna designs that harmonize ergonomic compatibility, reliable performance, and minimal interaction with human tissues. However, high specific absorption rate (SAR) levels, limited radiation efficiency, and challenges in integration with flexible materials have significantly constrained widespread deployment. To address these limitations, this manuscript introduces a novel wearable cavity-backed substrate-integrated waveguide (SIW) antenna augmented with artificial magnetic conductor (AMC) structures. The proposed architecture is meticulously engineered using diverse textile substrates, including cotton, jeans, and jute, to synergistically integrate SIW and AMC technologies, mitigating body-induced performance degradation while ensuring safety and high radiation efficiency. The proposed design demonstrates significant performance enhancements, achieving SAR reductions to 0.672 W/kg on the spine and 0.341 W/kg on the forelimb for the cotton substrate. Furthermore, the AMC-backed implementation attains ultra-low reflection coefficients, as low as −26.56 dB, alongside a gain improvement of up to 1.37 dB, culminating in a total gain of 7.09 dBi. The impedance bandwidth exceeds the ISM band specifications, spanning 150 MHz (2.3–2.45 GHz). The design maintains remarkable resilience and operational stability under varying conditions, including dynamic bending and proximity to human body models. By substantially suppressing back radiation, enhancing directional gain, and preserving impedance matching, the AMC integration optimally adapts the antenna to body-centric communication scenarios. This study uniquely investigates the dielectric and mechanical properties of textile substrates within the AMC-SIW configuration, emphasizing their practicality for wearable applications. This research sets a precedent for wearable antenna innovation, achieving an unprecedented balance of flexibility, safety, and electromagnetic performance while establishing a foundation for next-generation wearable systems. Full article

The composition of TBFS is complex. It is categorized into low (W_(TiO2) < 5%), medium (5% < W_(TiO2) < 20%), and high-titanium slag (W_(TiO2) > 20%) based on Ti content. The titanium in the slag is underutilized, causing it to accumulate and contribute to environmental pollution. Current methods for extracting titanium from TBFS include acid leaching, alkali fusion roasting, high-temperature carbonation–low-temperature chlorination, electrochemical molten salt electrolysis, and selective enrichment. However, these methods still face challenges such as environmental impact, high costs, low Ti recovery, and low Ti grade. This paper summarizes the mechanisms and characteristics of the above methods. Future research should focus on integrating pyrometallurgy with beneficiation processes, followed by further purification of titanium-rich phases through hydrometallurgy. Additionally, combining this with novel separation technologies (such as microwave and superconducting magnetic separation) will optimize the dissociation of titanium-bearing phases after enrichment. Full article

Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data. Full article

In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient’s own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. The model uses wavelet transform to extract different components of the image and performs fusion and enhancement processing as the input to the model. The model performs multiple registrations from local to global regions. Then, we propose a novel shared pyramid registration network that can accurately extract features from different modalities, optimizing the predicted deformation field through progressive refinement. In order to improve the registration performance, we also propose a deep learning similarity measurement method combined with bistructural morphology. On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. The model was verified by the actual clinical data of cervical cancer patients. After a large number of experiments, our proposed model achieved the highest dice similarity coefficient (DSC) metric compared with the state-of-the-art registration methods. The DSC index of the MTEF algorithm is 5.64% higher than that of the TransMorph algorithm. It will effectively integrate multi-modal image information, improve the accuracy of tumor localization, and benefit more cervical cancer patients. Full article

Canine functional magnetic resonance imaging (fMRI) neurocognitive studies represent an emerging field that is advancing more gradually compared to progress in human fMRI research. Given the potential benefits of canine fMRI for veterinary, comparative, and translational research, this systematic review highlights significant findings, focusing on specific brain areas activated during task-related and resting state conditions in dogs. The review addresses the following question: “What brain areas in dogs are activated in response to various stimuli?”. Following PRISMA 2020 guidelines, a comprehensive search of PUBMED, Scopus, and Web of Knowledge databases identified 1833 studies, of which 46 met the inclusion criteria. The studies were categorized into themes concerning resting state networks and visual, auditory, olfactory, somatosensory, and multi-stimulations studies. In dogs, resting state networks and stimulus-specific functional patterns were confirmed as vital for brain function. These findings reveal both similarities and differences in the neurological mechanisms underlying canine and human cognition, enhance the understanding of neural activation pathways in dogs, expand the knowledge of social bonding patterns, and highlight the potential use of fMRI in predicting the suitability of dogs for assistance roles. Further studies are needed to further map human–canine similarities and identify the unique features of canine brain function. Additionally, implementing innovative human methods, such as combined fMRI–magnetic resonance spectroscopy (MRS), into canine neurocognitive research could significantly advance the field. Full article

Recent advancements have led to a growing prevalence of studies examining ionospheric and atmospheric anomalies as potential precursors to earthquakes. In this context, the study involved analyzing variations in ionospheric total electron content (TEC), investigating anomalies, assessing space weather conditions, and examining changes in atmospheric parameters to evaluate potential precursors and post-seismic effects related to the Mw 7.7 and Mw 7.6 earthquakes that struck Kahramanmaraş consecutively in 2023. To compute the total electron content (TEC) values, data from 29 GNSS receivers covering a period of approximately 49 days were processed. In addition, since identical code signals were not available among all receiver stations, the study conducted an analysis of TEC estimations applying different GPS codes. To analyze space weather conditions, which are considered the main source of changes in the ionosphere, variations in sunspot number, solar activity index, magnetic activity indices (Kp and Dst), and geomagnetic field components were examined across the relevant period. To assess the potential presence of a distinct relationship between seismic activity at the Earth’s surface and ionospheric conditions, atmospheric parameters including temperature, relative humidity, and pressure were meticulously monitored and evaluated. As a result of the study, it was determined that TEC anomalies that could be evaluated as earthquake precursors independent of space weather conditions were observed starting from the 3rd day before the earthquake, and high positive TEC anomalies occurred immediately after the earthquakes. In atmospheric parameters, the change in behavior, particularly in temperature value, 10 days before the earthquake, is noteworthy. Full article

The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training. Full article

The detection and assessment of atherosclerosis and cardiovascular calcification can inform risk stratification and therapies to reduce cardiovascular morbidity and mortality. In this review, we provide an overview of current and emerging imaging techniques for assessing atherosclerosis and cardiovascular calcification in animal models. Traditional imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), offer non-invasive approaches of visualizing atherosclerotic calcification in vivo; integration of these techniques with positron emission tomography (PET) imaging adds molecular imaging capabilities, such as detection of metabolically active microcalcifications with ¹⁸F-sodium fluoride. Photoacoustic imaging provides high contrast that enables in vivo evaluation of plaque composition, yet this method is limited by optical penetration depth. Light-sheet fluorescence microscopy provides high-resolution, three-dimensional imaging of cardiovascular structures and has been used for ex vivo assessment of atherosclerotic calcification, but its limited tissue penetration and requisite complex sample preparation preclude its use in vivo to evaluate cardiac tissue. Overall, with these evolving imaging tools, our understanding of cardiovascular calcification development in animal models is improving, and the combination of traditional imaging techniques with emerging molecular imaging modalities will enhance our ability to investigate therapeutic strategies for atherosclerotic calcification. Full article

The presence of island grains in the initial finished sheets of grain-oriented electrical steel is inevitable in the preparation of ultra-thin strips. Owing to their distinctive shape and size effects, their deformation behavior during rolling differs from that of grain-oriented electrical steels of conventional thickness. This study focuses on the orientation evolution and deformation heterogeneity of island grains during rolling. Four types of island grains with orientations of {210}<001>, {110}<112>, {114}<481>, and {100}<021> were selected and modeled within the Goss-oriented matrix using full-field crystal plasticity finite element (CPFEM) simulation under plane strain compression. The results are then compared with corresponding experimental measurements. The results reveal that orientation rotation and grain fragmentation vary among the island grains of different orientations, with the first two orientations exhibiting more significant deformation heterogeneity compared to the latter two. Additionally, the orientations of the island grains significantly affect the distribution of residual Goss orientations within the surrounding matrix. Pancake-like island grains exhibit a higher degree of orientation scatter and greater deformation heterogeneity in the central layer compared to their spherical counterparts. The initial {210}<001> island grains can form a cube orientation, which can be optimized by subsequent process control to enhance magnetic properties. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) are neurodevelopmental disorders marked by challenges in social interaction, communication, and repetitive behaviors. People with ASD may exhibit repetitive behaviors, unique ways of learning, and different ways of interacting with the world. The term “spectrum” reflects the wide variability in how ASD manifests in individuals, including differences in abilities, symptoms, and support needs, and conditions characterized by difficulties in social interactions, communication, restricted interests, and repetitive behaviors. Inflammation plays a crucial role in the pathophysiology, with increased pro-inflammatory cytokines in cerebrospinal fluid. Previous studies with transcranial magnetic stimulation have shown promising results, suggesting nervous system susceptibility to electromagnetic fields, with evidence indicating that extremely low-frequency electromagnetic field (ELF-EMF) treatment may modulate inflammatory responses through multiple pathways, including the reduction of pro-inflammatory cytokines like IL-6 and TNF-α, and the enhancement of anti-inflammatory mediators. Methods: This pilot study included 20 children (ages 2–13) with a confirmed diagnosis of ASD. A 15-week protocol involved ELF-EMF treatments using the SEQEX device, with specific day and night programs. Assessment was conducted through standardized pre- and post-treatment tests: Achenbach Child Behavior Checklist, Peabody Picture Vocabulary Test-4, Expressive One Word Picture Vocabulary Test-4, and Conner’s 3GI. Results: Statistically significant improvements were observed in receptive language (PPVT-4: from 74.07 to 90.40, p = 0.002) and expressive language (EOWPVT-4: from 84.17 to 90.50, p = 0.041). Notable reductions, with statistical significance, were found in externalizing problems across both age groups (1.5–5 years: p = 0.028; 6–18 years: p = 0.027), with particular improvement in attention and behavioral problems. The results were observed over a short period of 15 weeks, therefore excluding the possibility of coincidental age-related gains, that would typically occur during a normal developmental timeframe. Parent evaluations showed significant reduction in ASD symptoms, particularly in the 1.5–5 years group (p = 0.046). Conclusions: ELF-EMF treatment demonstrated a high safety profile and efficacy in mitigating ASD-related symptoms. The observed improvements suggest both direct effects on central and autonomic nervous systems and indirect effects through inflammatory response modulation. Further studies are needed to confirm these promising results through broader demographics and randomized control designs. Full article

This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis (FEA) were employed to analyze the magnetic circuit coupling phenomena and their effects on the magnetic flux density distribution for each configuration. Subsequently, the air gap flux density and electromagnetic force were quantified under rotor eccentricity caused by unbalanced disturbances, and the dynamic performances of the ARMBs were evaluated for eccentricity along the x-axis and at 45°. Finally, experiments measured the electromagnetic forces acting on the rotor under the NNSS and NSNS configurations during eccentric conditions. The results indicate that the NNSS configuration significantly reduces magnetic circuit coupling, improves the uniformity of electromagnetic force distribution, and offers superior stability and control efficiency under asymmetric conditions. Experimental results deviated by less than 10% from the simulations, confirming the reliability and practicality of the proposed design. These findings provide valuable insights for optimizing ARMB pole configurations and promote their application in high-speed, high-precision industrial fields such as aerospace and power engineering. Full article