Search Results (208)

Cancer is considered today as a prevalent research direction due to the fact that, by 2050, more than 30 million cases will occur, followed by about 19 million deaths. It is expected that scholars will search for new, innovative, and localized therapies to ensure a much more targeted treatment with reduced side effects. Magnetic hydrogels overcome the disadvantages of classical magnetic nanoparticles in various oncological domains, including magnetic hyperthermia, theragnostic, immunotherapy, and, notably, regenerative medicine and contrast substances. We will review the magnetic hydrogel topics that may be involved as a potential application for cancer. Firstly, we present the international context and subject importance in the framework of statistics estimated by some researchers. Then, the magnetic hydrogel synthesis method will be briefly described with examples extracted from the literature. Supplementary, we will emphasize the main attributes of an ideal magnetic hydrogel, and last but not least, we will review some of the latest in vitro and in vivo studies in a direct relationship with magnetic hyperthermia, chemotherapeutic drug release dynamics, and immunotherapy used as single strategies or in combination, by underling the magnetic properties of the hydrogels and importance of application of magnetic fields. We will conclude our review paper by discussing toxicity issues, future trends, limitations, and proposed new approaches to address them. Full article

In the field of rotating machinery, such as marine propulsion shafting, magnetic bearing-supported propulsion systems have garnered significant attention due to their non-mechanical contact advantages. To address the problem that the design of magnetic bearing controllers, based on theoretical models, neglects the dynamic characteristics of practical components like power amplifiers and displacement sensors, making it difficult to achieve ideal performance in practical applications, this paper proposes a control method for Hybrid Magnetic Bearings (HMBs) that combines a time-domain identification model with robust control. The method first models the power amplifier, HMB, and displacement sensor as an equivalent single system and obtains its high-precision transfer function model by performing system identification on its time-domain data using the least squares method. Based on this foundation, a PID controller is designed using the loop-shaping method to enhance the system’s robustness and control performance. Both simulations and experiments on an HMB test rig confirmed the controller’s effectiveness. The system showed excellent levitation, dynamic stability, and disturbance rejection, with experimental results closely matching simulations. The experimental results are consistent with the simulation results. This method provides a practical and feasible technical approach for enhancing the control performance of magnetic bearing-supported propulsion shafting. Full article

Teotihuacan, one of the most significant urban and ceremonial centers of ancient Mesoamerica, was abruptly abandoned in the mid-1st millennium AD. The cause and timing of its collapse—commonly placed between 600 and 650 AD—remain major questions in Mesoamerican archaeology. In this study, we present a new archaeomagnetic investigation of six burned structures distributed along the Street of the Dead, including sites at the Square of the Moon, the Room of Columns, the Northwest Complex of the San Juan River, the Superimposed Buildings, and the West Plaza. Magnetic analyses revealed pseudo-single-domain magnetite as the main remanence carrier and produced well-grouped paleodirections (site-mean declinations ranging from 341.1° to 1.7°, α₉₅ ≤ 3.6°) and reliable absolute paleointensities (ranging from 39.4 ± 3.4 μT to 52.5 ± 5.4 μT), obtained using the Thellier-type double-heating method. Archaeomagnetic dating using both global geomagnetic models (SHAWQ.2k) and regional secular variation curves suggests that the last heating events at these sites occurred between ~400 and 500 AD—well before the traditionally cited Metepec phase (550–650 AD) and the so-called “Great Fire.” These findings challenge the prevailing chronological framework and provide compelling evidence that major episodes of destruction and depopulation may have begun earlier than previously recognized. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

Dynamic behavior of coaxial axisymmetric planar cracks in the transversely isotropic magneto-electro-elastic (MEE) material in transient in-plane magneto-electro-mechanical loading is studied. Magneto-electrically impermeable as well as permeable cracks are assumed for crack surfaces. In the first step, considering prismatic and radial dynamic dislocations, electric and magnetic jumps are obtained through Laplace and Hankel transforms. These solutions are utilized to derive singular integral equations in the Laplace domain for the axisymmetric penny-shaped and annular cracks. Derived Cauchy singular type integral equations are solved to obtain the density of dislocation on the crack surfaces. Dislocation densities are utilized in computation of the dynamic stress intensity factors, electric displacement, and magnetic induction in the vicinity tips of crack tips. Finally, some numerical case studies of single and multiple cracks are presented. The effect of system parameters on the results is then discussed. Full article

This study investigates the elemental composition of magnetic nanoparticles (MNPs) in eleven wildland–urban interface (WUI) fire ashes, including one vegetation, six structural, and four vehicle ashes, along with three fire-impacted soil samples. The WUI fire ash samples were collected following the 2020 North Complex (NC) Fire and Sonoma–Lake–Napa unit (LNU) Lightning Complex Fire in California. Efficiency of magnetic separation was confirmed via Time-Domain Nuclear Magnetic Resonance (TD-NMR); the relaxometry showed that the transverse relaxation rate R₂ decreased from 2.02 s⁻¹ before separation to 0.29 s⁻¹ after separation (ΔR₂ = −1.73 s⁻¹; −86%), due to the removal of magnetic particles. The particle number concentrations, size distributions, and elemental compositions (and ratios) of MNPs were determined using single particle-inductively coupled plasma–time-of-flight-mass spectrometry (SP-ICP-TOF-MS). The major types of nanoparticles (NPs) detected in the magnetically separated MNPs were Fe-, Ti-, Cr-, Pb-, Mn-, and Zn-bearing NPs. The iron-bearing NPs accounted for 3.2 to 83.5% of the magnetically separated MNPs, and decreased following the order vegetation ash (77.4%) > soil (63.2–69.9%) > structural (3.2–83.5%) ash. The titanium-bearing NPs accounted for 3.3 to 66.1% of the magnetically separated MNPs, and decreased following the order vehicle (14.1–66.1%) > structural (3.5–36.4%) > vegetation (3.3%) ash. The majority of the detected NPs in the fire ashes occurred in the form of multi-metal (mm) NPs, attributed to the presence of NPs as heteroaggregates and/or due to the sorption of metals on the surfaces of NPs during combustion. However, a notable fraction (3–91%) of the detected NPs occurred as single-metal (sm) NPs, particularly smFe-bearing NPs, which accounted for 48 to 91% of all the Fe-bearing particles in the magnetically separated MNPs. The elemental ratios (e.g., Al/Fe, Ti/Fe, Cr/Fe, and Zn/Fe) in the magnetically separated MNPs from structural and vehicle ashes were higher than those in the soil samples and vegetation ashes, indicating enrichment of metals in magnetically separated NPs from vehicle and structural ashes compared to vegetation ash. Overall, this study demonstrates that the MNPs generated by WUI fire ash are associated with potentially toxic elements (e.g., Cr and Zn), exacerbating the environmental and human health risks of WUI fires. This study also highlights the need for further research into the properties, environmental fate, transport, and interactions of MNPs with biological systems during and following WUI fires. Full article

In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, and external factors. Therefore, preset control parameters may not achieve the desired optimal performance. Many scholars use intelligent algorithms, such as neural networks, to adaptively tune control parameters. However, the offline pre-training of neural networks is often time- and resource-consuming. Due to the lack of a model pre-training process in the neural network online self-tuning process, randomly setting the initial network weight seriously affects the position tracking performance of the servo control system in the start-up phase. In this paper, the physical model and the traditional frequency domain-tuning method of the three-closed-loop permanent magnet synchronous servo system are analyzed. Combined with the neural network PI control parameter self-tuning method and physical symmetry, a physical information-driven optimization framework is proposed. To demonstrate its superiority, the neural network PI controller and the proposed optimization framework are used to control the single-axis sine wave trajectory. The results show that the optimization framework proposed can effectively improve the position tracking control performance of the servo control system in the start-up phase by setting the threshold of the servo control parameters, reduce the position tracking control error to 0.75 rads in the start-up phase, and reduce the position tracking drop caused by a sudden load by 25%. This method achieves the independent optimization adjustment of control parameters under position tracking control, providing a reference for the intelligent control of permanent magnet synchronous servo motors. Full article

There are two ways of linearizing the Klein–Gordon equation: Dirac’s choice, which introduces a matter–antimatter pair, and a second approach using a bivector, which Dirac did not consider. In this paper, we show that a bivector provides the classical origin of quantum spin. At high precessional frequencies, a symmetry transformation occurs in which classical reflection becomes quantum parity. We identify a classical spin-1 boson and demonstrate how bosons deliver energy, matter, and torque to a surface. The correspondence between classical and quantum domains allows spin to be identified as a quantum bivector, $i\sigma$. Using geometric algebra, we show that a classical boson has two blades, corresponding to magnetic quantum number states $m=\pm 1$. We conclude that fermions are the blades of bosons, thereby unifying both into a single particle theory. We compare and contrast the Standard Model, which uses chiral vectors as fundamental, with the Bivector Standard Model, which uses bivectors, with two hands, as fundamental. Full article

Background: Chronic musculoskeletal pain (CMP) is a multidimensional condition involving both peripheral and central mechanisms, with increasing evidence supporting an interplay between subjective pain perception and autonomic nervous system (ANS) function. However, few studies have explored whether a single non-invasive intervention can concurrently modulate both domains. Objectives: To evaluate the short-term effects of a single session of Pulsed Electromagnetic Field (PEMF) therapy—administered via the PAP Ion Magnetic Induction (PAPIMI™) device—on subjective pain intensity and heart rate variability (HRV) parameters in individuals with CMP. The relationship between perceived pain relief and physiological autonomic adaptations was also explored. Methods: Thirty adults with CMP underwent a single PAPIMI™ session. Subjective pain intensity was measured using the Numeric Pain Rating Scale (NPRS), while autonomic function was assessed via HRV. Pre- to post-intervention changes were analyzed using the Wilcoxon Signed-Rank test, while Spearman’s correlation was computed to assess associations between post-intervention changes in subjective perceived pain and HRV parameters. Results: A significant reduction in NPRS scores (p < 0.001) was found after PAPIMI intervention. Also, a significant increase in specific parasympathetic-related HRV indices, namely, RMSSD (p = 0.015) and HF power (p = 0.029), was observed. No significant correlations were found between post-intervention changes in pain perception and HRV metrics. Conclusions: A single PAPIMI session induced both analgesic effects and improvements in autonomic balance in individuals with CMP. These findings underscore the potential of PAPIMI as a non-pharmacological approach for rapid pain modulation and systemic rebalancing. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

Background: Parkinson’s disease (PD) presents a significant challenge due to its wide range of motor, non-motor, and treatment-related symptoms. Non-invasive interventions like transcranial magnetic stimulation (TMS) are being explored for potential therapeutic benefits. This study aimed to assess if a high-frequency repetitive TMS protocol (HF-rTMS) consisting of 10 trains of 100 pulses of rTMS at 25 Hz over the motor cortex (M1) at 80% of the resting motor threshold could be effective in treating motor or non-motor symptoms in patients with PD with levodopa-induced dyskinesias. Methods: A randomized, single-blinded, placebo-controlled pilot trial was conducted with eleven PD patients. Nine patients received HF-rTMS, while two received sham stimulation. Patients were exhaustively evaluated using validated clinical scales to assess motor and non-motor symptoms. The study followed a rigorous protocol to avoid bias, with assessments conducted by a neurologist specialized in single-blinded movement disorder. Results: The HF-rTMS group experienced a statistically significant slight worsening in both motor and non-motor symptoms, particularly in the mood/cognition and gastrointestinal domains. However, positive effects were observed in some non-motor symptoms, specifically reduced excessive sweating and weight. No adverse effects were reported. Conclusions: Although HF-rTMS did not produce significant motor improvements, its potential benefit on specific non-motor symptoms, such as autonomic regulation, warrants further investigation. Full article

Background/Objectives: Structural magnetic resonance imaging (MRI) and 18-fluoro-deoxy-glucose positron emission tomography (PET) reveal the structural and functional information of the brain from different dimensions, demonstrating considerable clinical and practical value in the computer-aided diagnosis of Alzheimer’s disease (AD). However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. Methods: In this study, we propose a multi-modal deep domain adaptation (MM-DDA) model that integrates MRI and PET modal data, which aims to maximize the utilization of the complementarity of the multi-modal data and narrow the differences in domain distribution to boost the accuracy of AD classification. Specifically, MM-DDA comprises three primary modules: (1) the feature encoding module, which employs convolutional neural networks (CNNs) to capture detailed and abstract feature representations from MRI and PET images; (2) the multi-head attention feature fusion module, which is used to fuse MRI and PET features, that is, to capture rich semantic information between modes from multiple angles by dynamically adjusting weights, so as to achieve more flexible and efficient feature fusion; and (3) the domain transfer module, which reduces the distributional discrepancies between the source and target domains by employing adversarial learning training. Results: We selected 639 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and considered two transfer learning settings. In ADNI1→ADNI2, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 92.40%, 81.81%, 81.13%, and 85.45%, respectively. In ADNI2→ADNI1, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 94.73%, 81.48%, 85.48%, and 81.69%, respectively. Conclusions: MM-DDA is compared with other deep learning methods on two kinds of transfer learning, and the performance comparison results confirmed the superiority of the proposed method in AD prediction tasks. Full article

The Finite Difference Time-Domain (FDTD) method is a powerful tool for electromagnetic field analysis. In this work, we develop a variation of the algorithm to accurately calculate antenna, microwave circuit, and target scattering problems. To improve efficiency, a single-field (SF) FDTD method is proposed as a numerical solution to the time-domain Helmholtz equations. New formulas incorporating resistors and voltage sources are derived for the SF-FDTD algorithm, including hybrid implicit–explicit and weakly conditionally stable SF-FDTD methods. The correctness of these formulas is verified through numerical simulations of a newly designed dual-band wearable antenna with an artificial magnetic conductor (AMC) structure. A novel antenna fed by a coplanar waveguide with a compact size of 15.6 × 20 mm² has been obtained after being optimized through an artificial intelligent method. A double-layer, dual-frequency AMC structure is designed to improve the isolation between the antenna and the human body. The simulation and experiment results with different bending degrees show that the antenna with the AMC structure can cover two frequency bands, 2.4 GHz–2.48 GHz and 5.725 GHz–5.875 GHz. The gain at 2.45 GHz and 5.8 GHz reaches 5.3 dBi and 8.9 dBi, respectively. The specific absorption rate has been reduced to the international standard range. In particular, this proposed SF-FDTD method can be extended to analyze other electromagnetic problems with fine details in one or two directions. Full article

Air pollution plays a key role in sleep disorders and neurodegeneration. Alzheimer’s disease (AD), Parkinson’s disease (PD), and/or transactive response DNA-binding protein TDP-43 neuropathology have been documented in children and young adult forensic autopsies in the metropolitan area of Mexico City (MMC), along with sleep disorders, cognitive deficits, and MRI brain atrophy in seemingly healthy young populations. Ultrafine particulate matter (UFPM) and industrial nanoparticles (NPs) reach urbanites’ brains through nasal/olfactory, lung, gastrointestinal tract, and placental barriers. We documented Fe UFPM/NPs in neurovascular units, as well as lateral hypothalamic nucleus orexinergic neurons, thalamus, medullary, pontine, and mesencephalic reticular formation, and in pinealocytes. We quantified ferromagnetic materials in sleep and arousal brain hubs and examined their motion behavior to low magnetic fields in MMC brain autopsy samples from nine children and 25 adults with AD, PD, and TDP-43 neuropathology. Saturated isothermal remanent magnetization curves at 50–300 mT were associated with UFPM/NP accumulation in sleep/awake hubs and their motion associated with 30–50 µT (DC magnetic fields) exposure. Brain samples exposed to anthropogenic PM pollution were found to be sensitive to low magnetic fields, with motion behaviors that were potentially linked to the early development and progression of fatal neurodegenerative diseases and sleep disorders. Single-domain magnetic UFPM/NPs in the orexin system, as well as arousal, sleep, and autonomic regions, are key to neurodegeneration, behavioral and cognitive impairment, and sleep disorders. We need to identify children at higher risk and monitor environmental UFPM and NP emissions and exposures to magnetic fields. Ubiquitous ferrimagnetic particles and low magnetic field exposures are a threat to global brain health. Full article