Search Results (178)

The classification of Unexploded Ordnance (UXO) from magnetometer data is a critical but challenging task, frequently hindered by the data scarcity required for training robust machine learning models. To address this, we leverage a high-fidelity digital twin to generate a comprehensive dataset of magnetometer signals from both UXO and non-UXO objects, incorporating complex remanent magnetization effects. In this study, we design and evaluate a custom Convolutional Neural Network (CNN) for UXO classification and compare it against classical machine learning baseline, including Random Forest and kNN. Our CNN model achieves a balanced accuracy of 84.65%, significantly outperforming traditional models that exhibit performance collapse under slight distortions such as additive noise, drift, and time-wrapping. Additionally, we present a compact two-block CNN variant that retains competitive accuracy while reducing the number of learnable parameters by approximately 33%, making it suitable for real-time onboard classification in underwater vehicle missions. Through extensive ablation studies, we confirm that architectural components, such as residual skip connections and element-wise batch normalization, are crucial for achieving model stability and performance. The results also highlight the practical implications of underwater vehicles for survey design, emphasizing the need to mitigate signal drift and maintain constant survey speeds. This work not only provides a robust deep learning model for UXO classification, but also offers actionable suggestions for improving both model deployment and data acquisition protocols in the field. Full article

The permanent magnet electrodynamic suspension system (PMEDS) has demonstrated significant advantages in high-speed and ultra-high-speed applications due to its simple structure, low cost, and stable levitation force. However, the weak damping characteristic remains a critical issue limiting its practical implementation. This work investigates a passive damping plate utilizing the end field of onboard magnets, focusing on magnet-damping plate optimization and vehicle dynamics. Firstly, the configuration, operation principles, and electromagnetic parameters of the PMEDS vehicle are elucidated. Secondly, the dependences of magnet-conductive plate specifications on the damping force are examined. An optimization index based on the levitation-to-damping force ratio is proposed to enable collaborative optimization of magnet and conductive plate parameters. Finally, the vehicle dynamic model is developed using Simpack software to investigate payload and speed effects on dynamic responses under random track excitation, validating the effectiveness of the proposed passive damping solution. This study provides technical references for the design, engineering applications, and performance evaluation of passive damping schemes in PMEDS vehicles. Full article

Extremely powerful flares releasing energy well above ${10}^{32}$ erg are rare compared to the typical manifestations of solar activity, which are already being routinely monitored by the existing Space Weather network—with some level of predictability. However, much less is known about the mechanisms behind such rare events (like the well-documented Carrington event of 1859) or about hypothetical superflares that could exceed current energy estimates by several orders of magnitude. We propose a model based on the nonlinear suppression of turbulent diffusion with increasing magnetic field, which ultimately leads to the random occurrence of regions with a magnetic field amplitude significantly exceeding the magnetic field amplitude in a regular cycle. This is similar to the mechanism of a local “explosion of an overheated boiler”. Such regions can be correlated with flares. In our model, flares have different powers. Full article

Using the results of numerical simulations and astrophysical observations (mainly in the WMAP and Planck frequency bands), it is shown that Galactic foreground emission becomes more sensitive to the mean magnetic field with the frequency, resulting in the appearance of two levels of its randomization due to the chaotic/turbulent dynamics of a magnetized interstellar medium dominated by magnetic helicity. The galactic foreground emission is more randomized at higher frequencies. The Galactic synchrotron and polarized dust emissions have been studied in detail. It is shown that the magnetic field imposes its level of randomization on the synchrotron and dust emission. The main method for the theoretical consideration used in this study is the Kolmogorov–Iroshnikov phenomenology in the frames of distributed chaos notion. Despite the vast differences in the values of physical parameters and spatio-temporal scales between the numerical simulations and the astrophysical observations, there is a quantitative agreement between the results of the astrophysical observations and the numerical simulations in the frames of the distributed chaos notion. Full article

Native to South Africa, Bush tea is a plant that thrives in various climates. Cultural practices such as mineral nutrition, fertigation, pruning, and harvesting have been shown to influence bush tea’s quality, growth, and yield. This study set out to determine the effects of mulching and deficit irrigation on the growth, yield, and quality of bush tea. Three deficit irrigation treatments (0%, 30%, and 100% Crop evapotranspiration (ETc) on field capacity) and three mulch treatments (sawdust, black plastic mulch, and no mulch) were included in a two-factor experiment, which was set up in a randomized complete block design (RCBD) with three replications. Physiological and growth parameters were taken every two weeks. The number of branches was counted, and measurements of chlorophyll content and the proportion of radiation intercepted by the canopy were recorded. Yield and secondary metabolites such as sugar residuals, fatty acids, and phenols of bush tea were determined after harvest. Growing bush tea under various water regimes showed that a 30% water regime significantly enhanced plant growth characteristics, including the proportion of intercepted radiation, plant height, and both fresh and dry weight. Furthermore, under different water regimes, sawdust improved plant growth in bush tea grown in the field. Black plastic mulch and a 0% water regime produced more compounds beneficial to health than tea treated with half or full irrigation. The extraction of data for Proton Nuclear Magnetic Resonance (NMR) and Mass Spectrometry analyses was conducted for quality components. Our study did not show any distinct structural differences in the tea under different water regimes or mulching. Flavones, phenols, diterpenes, and gardoside were some of the most abundant compounds found in bush tea using mass spectrometry. Principal Component Analysis was performed on the NMR spectral data across 27 samples of bush tea. Full article

Background/Objectives: The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person’s lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person’s life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists’ experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. Methods: In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. Results: It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. Conclusions: Our innovative classifier’s evaluation underlined our model’s potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors. Full article

The contact process is a nonequilibrium Hamiltonian model that, even in one dimension, lacks an exact solution and has been extensively studied via Monte Carlo simulations, both in steady-state and time-dependent scenarios. Although the effects of particle mobility and diffusion on criticality have been preliminarily explored, they remain poorly understood in many aspects. In this work, we examine how the critical rate of the model varies with the probability of particle mobility. By analyzing different stochastic evolutions of the system, we employ two modern approaches: (1) Random Matrix Theory (RMT): By building on the success of RMT, particularly Wishart-like matrices, in studying statistical physics of systems with up-down symmetry via magnetization dynamics [R. da Silva, IJMPC 2022], we demonstrate its applicability to models with an absorbing state; (2) Optimized Temporal Power Laws: By using short-time dynamics, we optimize power laws derived from ensemble-averaged evolutions of the system. Both methods consistently reveal that the critical rate decays with mobility according to a simple Belehradek function. Additionally, a straightforward mean-field analysis supports the decay of the critical parameter with mobility, although it predicts a simpler linear dependence. We also demonstrate that the more sophisticated pair approximation mean-field model developed by ben-Avraham and Köhler aligns closely with the Belehradek function, precisely matching our lattice simulation results. Full article

Although various magnetic inversion techniques have been developed in geophysics, traditional methods are often constrained by inherent limitations such as low computational efficiency and pronounced non-uniqueness. In 3D magnetic inversion, multi-dimensional deep learning methods have shown promise in numerical simulations; however, their generalization capabilities and practical effectiveness in real-world geological applications, particularly in complex settings like gold exploration, remain underexplored. This study introduces MAGNETPRO, a residual convolutional neural network based on an encoder–decoder architecture, designed to accurately invert 2D magnetic field data into 3D magnetic susceptibility structures. To enhance the model’s generalization ability and inversion accuracy, an innovative data construction strategy was implemented to create a highly randomized training dataset incorporating complex geological features. Theoretical model tests demonstrate that MAGNETPRO achieves inversion accuracies of 97% across the entire region and 80% within magnetic structure areas, highlighting its excellent spatial resolution and structural recognition capabilities. To further validate its practical effectiveness, the method was applied to real exploration data from a gold mining area in Fujian Province. The results show a high degree of consistency between the inversion outcomes and drilling data, confirming the method’s reliability and practical value under real geological conditions. Full article

In this paper, we apply several latest results from statistical physics on the probability and energy of knotting to study the knotted field lines in solar corona. Since the solar magnetic field in small scale can be seen as nearly random, by assuming that the magnetic field lines behave similarly to random loops, we find the probability P of certain knot type K for the field line knotting as a function to the distance L between the foot-points of sunspots, which is $P_K\left(L\right)=C_K{L}^{2{\alpha }_K}exp\left({\displaystyle \frac{-L^2}{\beta }}\right)$. From the equation, we find that the variety of knot type increases with the distance. Since knotting is the topological resemblance to magnetic helicity, which is an invariant for ideal MHD, our result enriches the understanding of the probability of magnetic helicity as well as field line structure in active regions. Based on the relation between knotting and magnetic energy, we provide support to the high variety of field line knot types. Full article

Cardiac magnetic resonance imaging (MRI) is increasingly recognized as a promising tool for tissue characterization in atrial fibrillation (AF), providing detailed insights into anatomy, fibrosis, and scarring. While MRI cannot directly guide ablation lesions, its ability to identify arrhythmogenic substrates could improve patient stratification and procedural planning. Despite these theoretical advantages, the clinical utility of MRI in guiding substrate-based ablation strategies remains a matter of debate. Methods: Our review evaluates the current evidence supporting the integration of MRI into the workflow of AF ablation. Specifically, we examine findings from randomized trials and prospective studies that have investigated the predictive value of MRI-derived fibrosis quantification for procedural outcomes and arrhythmia recurrence. We aim to assess whether MRI can enhance the personalization of ablation strategies and predict treatment success. Challenges such as variability in imaging protocols, lack of standardization in fibrosis quantification, and limited large-scale validation are also addressed. This review provides a comprehensive overview of the current status and potential of MRI in the evolving field of AF ablation. Full article

Magnetic random-access memory, recognized as a breakthrough in spintronics, demonstrates substantial potential for next-generation nonvolatile memory and logic devices due to its unique magnetization-switching mechanism. However, realizing reliable perpendicular magnetization switching via spin–orbit torque necessitates an externally applied in-plane magnetic bias, a requirement that complicates integration in high-density device architectures. This study proposes a novel device architecture where geometric asymmetry engineering in an interlayer design generates an intrinsic equivalent in-plane magnetic field. By strategically introducing a non-symmetrical spacer between the heavy metal and ferromagnetic layers, we establish deterministic magnetization reversal while eliminating external field dependency. Furthermore, the energy barrier during magnetization switching is dynamically adjusted by applying a voltage across a perpendicular-anisotropy magnetic tunnel junction, leveraging the voltage-controlled magnetic anisotropy effect. We established a physics-driven compact model to assess the design and performance of voltage-controlled spin–orbit torque magnetic tunnel junction (VCSOT-MTJ) devices. Simulations reveal that the introduction of a minimally asymmetric light metal layer effectively resolves the issue of incomplete switching in field-free spin-orbit torque systems. Full article

Here, we study the Navier–Stokes equation for the motion of a passive particle based on the Fokker–Planck equation in an incompressible conducting fluid induced by a magnetic field subject to an exponentially correlated Gaussian force in three-time domains. For the hydro-magnetic case of velocity and the time-dependent magnetic field, the mean squared velocity for the joint probability density of velocity and the magnetic field has a super-diffusive form that scales as $\sim t^3$ in $t>>\tau$, while the mean squared displacement for the joint probability density of velocity and the magnetic field reduces to time $\sim t^4$ in $t<<\tau$. The motion of a passive particle for $\tau =0$ and $t>>\tau$ behaves as a normal diffusion with the mean squared magnetic field being $<h^2(t)>\sim t$. In a short-time domain $t<<\tau$, the moment in the magnetic field of the incompressible conducting fluid undergoes super-diffusion with ${\mu }_{2,0,2}^h\sim t^6$, in agreement with our research outcome. Particularly, the combined entropy $H(v,h,t)$ ($H(h,v,t)$) for an active particle with the perturbative force has a minimum value of $\sim \ln t^2$ ($\sim \ln t^2$) in $t>>\tau$ ($\tau =0$), while the largest displacement entropy value is proportional to $\ln t^4$ in $t<<\tau$ and $\tau =0$. Full article

The extension direction of welding defects is random and uncontrollable, while magneto-optical imaging detection has a good imaging effect on defects perpendicular to the magnetic field direction. At present, magneto-optical detection methods may fail to detect small weld defects parallel to the direction of the magnetic field. To overcome this problem, a non-destructive testing method based on magneto-optical imaging under a vertical combined magnetic field (VCMF) is proposed. The paper first establishes a simulation model to compare and analyze the magnetic leakage characteristics of cross grooves under a constant magnetic field (CMF), an alternating magnetic field (AMF), a rotating magnetic field (RMF), a parallel combined magnetic field (PCMF), and VCMF excitation, proving that detection does not easily fail under VCMF. Secondly, by changing the size of the CMF in the VCMF simulation model, it was found that, as the CMF intensity increases, a new maximum value will appear on the side of the defect contour close to the sample area. This maximum value increases with the increase of the CMF intensity, which can lead to misjudgment of the defect contour, that is, false contours. Finally, magneto-optical imaging was used to verify the imaging effect of weld defects under VCMFs. The results indicate that more comprehensive defect information can be detected under VCMFs. When the maximum value of the excitation current of the AMF is at least 12 times the excitation current of the CMF, there will be no false contour defects. Full article

Background: Strokes are a major public health concern, responsible for high mortality and long-term disability rates. Rehabilitation techniques aim to harness neuroplasticity—brain self-repair mechanisms that restore lost functions. Beyond traditional methods, therapies like Repetitive Transcranial Magnetic Stimulation (rTMS) and Extremely Low-Frequency Magnetic Fields (ELF-MFs) show promise in enhancing neuroplasticity. This pilot study explored the feasibility and safety of ELF-MFs in stroke rehabilitation. Methods: The study involved 44 patients randomized into three groups: magnetotherapy applied to the head (MT1), pelvis (MT2), or standard rehabilitation (control). Assessments included functional measures (FIM, Barthel Index, Tinetti Scale, SPPB, and Berg Balance Scale) and inflammatory markers (CRP, PCT). Results: All groups showed functional improvement, with CRP and PCT reductions highlighting potential benefits of ELF-MFs. No adverse effects or changes in blood or organ function were observed. Conclusions: ELF-MFs could be safely conducted in this group allowing for further research to confirm their efficacy in larger studies. Full article

Preclinical studies have shown that the blood from female mice exposed weekly to magnetic fields inhibited breast cancer growth. This double-blind randomized controlled trial investigated whether analogous magnetic therapy could produce similar anticancer sera from human subjects. Twenty-six healthy adult females (ages 30–45) were assigned to either a magnetic therapy group, receiving twice weekly 1 mT magnetic exposures (10 min/session) for 4 weeks, or a control group, who underwent identical sham exposure. Blood sera were evaluated for their capacity to modulate breast cancer-related cellular responses and epithelial–mesenchymal transition. The sera from the magnetic therapy group subjects exhibited significant anticancer effects that were strongest one month after the last magnetic exposure, whereas the sera from unexposed females or unexposed or exposed males showed no effect. Female sera from the magnetic therapy group (n = 12) reduced breast cancer cell proliferation (16.1%), migration (11.8%) and invasion (28.2%) and reduced the levels of key EMT markers relative to the control sera (n = 14). Magnetic therapy modulated the serum levels of angiogenic and myogenic biomarkers in a manner consistent with improved cancer management. Muscle-targeted magnetic therapy holds the potential to enhance the anticancer properties of human blood via an adaptive process, akin to exercise training. Full article