Search Results (11,491)

The spatial distribution of different hard magnetic phases is a key factor affecting the coercivity of dual-main-phase rare-earth permanent magnets. However, the underlying relationship between main-phase distribution and magnetization reversal behavior in Nd₂Fe₁₄B/Dy₂Fe₁₄B magnets remains unclear. In this work, micromagnetic simulations based on MuMax3 and OOMMF are performed to systematically investigate the effects of main-phase spatial arrangement on the magnetic properties and magnetization reversal mechanisms of Nd₂Fe₁₄B/Dy₂Fe₁₄B exchange-coupled magnets. First, single-phase Nd₂Fe₁₄B and Dy₂Fe₁₄B models are constructed to clarify the intrinsic magnetic characteristics of the two phases. The calculated demagnetization curves show that, although the magnetocrystalline anisotropy field H_A of Nd₂Fe₁₄B is lower than that of Dy₂Fe₁₄B, its higher saturation magnetization M_S results in a slightly larger anisotropy constant K, according to K = ${\displaystyle \frac{1}{2}}$µ₀·H_A·M_S. Nevertheless, Dy₂Fe₁₄B exhibits a stronger resistance to magnetization reversal, as reflected by its higher nucleation field H_N and coercivity H_C. This indicates that the resistance to magnetization reversal is more directly associated with H_A than with K alone. Subsequently, three types of exchange-coupled dual-main-phase Nd₂Fe₁₄B/Dy₂Fe₁₄B magnet models, including cubic, cylindrical, and sandwich structures, are constructed with identical size fractions of the two phases to investigate the influence of phase spatial distribution on magnetization reversal behavior. The calculated results demonstrate that placing the Dy₂Fe₁₄B phase in the outer region leads to higher H_Nand H_C than the reverse phase arrangement, owing to its higher H_A, which strengthens the resistance against magnetization reversal. Further analysis of the in-plane magnetic-moments and angular distributions reveals that magnetic-moment deviation is initially activated in the Nd₂Fe₁₄B region with lower H_A, followed by gradual propagation through exchange coupling at the phase interface. This effect of phase spatial distribution is not limited to Nd₂Fe₁₄B/Dy₂Fe₁₄B exchange-coupled magnets. In Nd₂Fe₁₄B/La₂Fe₁₄B and Nd₂Fe₁₄B/SmCo exchange-coupled magnets, placing the phase with the higher H_A in the outer region likewise results in higher H_Nand H_C than the reverse phase arrangement. In addition, for all the dual-main-phase exchange-coupled magnets considered above, the coercive field decreases with increasing magnet size. These findings provide theoretical insights into the regulation of coercivity through spatial phase distribution in dual-main-phase rare-earth permanent magnets. Full article

Human-occupied vehicles (HOVs) provide an ideal platform for high-resolution near-bottom magnetic anomaly detection. However, complex platform-generated magnetic interference severely limits the reliable extraction of weak magnetic signals. The conventional Tolles–Lawson (T–L) model relies on large-amplitude attitude maneuvers to estimate interference coefficients, but such maneuvers are infeasible for HOVs because of their large inertia, hydrodynamic coupling, and deep-sea safety constraints. To address this limitation, we propose a dynamic-excitation magnetic interference compensation method that exploits the inherent dynamic characteristics of the platform. By commanding the HOV to execute acceleration–deceleration cycles and horizontal S-shaped turns, the method indirectly excites pitch and roll variations through coupled vehicle dynamics and provides the attitude-dependent information required by the complete 18-term T–L model. The 18-term basis set is constructed from measured direction cosines and their time derivatives, with coefficient identifiability determined by the excitation data. Helicopter-based analog experiments showed that the proposed maneuver yielded improvement ratios of 6.6 and 12.1 on two test lines, comparable to the values of 6.3 and 12.5 obtained using conventional airborne calibration maneuvers. In an in situ trial with the Shenhai Yongshi (“Deep-Sea Warrior”) HOV, the method reduced the magnetic-field standard deviation from 0.8870 nT to 0.4157 nT, corresponding to an improvement ratio of 2.13. The compensated record exhibited a residual dynamic-field variation of 0.4157 nT under the tested maneuvering conditions. The helicopter experiment evaluated the shared excitation sequence and signal-processing workflow on a controllable airborne platform. These results demonstrate a practical engineering approach to suppressing dynamic magnetic-field variations below 1 nT in the tested HOV conditions. Full article

A water-soluble, low-molecular-weight polysaccharide fraction, designated OJP-2, was extracted from the roots of Ophiopogon japonicus; its structural characteristics and immunomodulatory activity in macrophages were subsequently investigated. OJP-2 exhibited a narrow apparent molecular weight distribution with an average molecular weight (Mw) of 3568 Da and was primarily composed of fructose (0.784) and glucose (0.208). Structural analysis—integrating UV spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and 1D/2D nuclear magnetic resonance (NMR) spectroscopy—revealed that OJP-2 is an inulin-type fructan. Its structure is characterized predominantly by β-(2→1)-linked Fruf chains and terminal α-D-Glcp residues, with potential signals indicating C-6-substituted Fruf units. In vitro immunological studies demonstrated that OJP-2 promoted nitric oxide (NO) production and enhanced the secretion of IL-6, IL-1β, and TNF-α in RAW264.7 macrophages. Under LPS/IFN-γ stimulation, OJP-2 induced non-monotonic changes in the CD86/CD206 macrophage phenotype, with the most pronounced effects observed at a concentration of 50 μg/mL. Furthermore, Western blot analysis showed that, compared to the Model group, treatment with OJP-2 resulted in a downward trend in the relative levels of p-p65/p65 and p-IκBα/IκBα across the tested concentration range. Collectively, these findings indicate that OJP-2 modulates macrophage activation phenotypes and influences NF-κB-related signaling pathways. Thus, OJP-2 is a candidate fructan for further investigation of immunomodulatory activity. Full article

To address the limitations of traditional analytical models in characterising multi-core coupling effects and the excessive computational cost of finite element simulations, this paper presents a high-precision analytical model for a novel T-II composite-core eddy current testing (ECT) probe using the Truncated Region Eigenfunction Expansion (TREE) method. The analytical expressions of coil impedance are derived by partitioning the solution domain into ten subdomains under an axisymmetric cylindrical coordinate system, with rigorous satisfaction of electromagnetic continuity at all material interfaces. Numerical cross-validation against 2D and 3D Finite Element Method (FEM) simulations under idealised modelling assumptions across the frequency range of 100 Hz to 10 kHz shows that the proposed TREE model yields relative errors below 2% for both coil resistance and reactance. Notably, the proposed approach requires significantly less computation time than 2D and 3D FEM. Further parametric analysis confirms that the proposed T-II composite-core probe delivers superior electromagnetic performance compared to conventional single-core probes, including intensified subsurface eddy current densities and improved magnetic field redistribution. This work overcomes the inherent limitations of single-core ECT analytical models, establishes a robust theoretical paradigm to interpret the distinctive electromagnetic field advantages of composite-core probes, and provides solid support for the structural optimisation of multi-core ECT sensors. Full article

Topology optimization is an effective method for obtaining high-performance material distributions with novel configurations. However, its application to complex electromagnetic devices remains challenging because of the difficulty of deriving sensitivities and the low computational efficiency associated with repeated finite element method (FEM) evaluations for nonlinear materials. This paper proposes an efficient gradient-free topology optimization method that integrates superellipse curves with a multilayer perceptron (MLP) surrogate model while accounting for nonlinearity. First, based on the general superellipse curve, an improved expression is introduced, in which the size, shape, and position can be flexibly controlled by only seven parameters. Then, a parameterized superellipse-curve-based gradient-free topology optimization framework is established, which can be applied to complex electromagnetic devices with nonlinear materials and complex objective functions. Moreover, a lightweight MLP-based surrogate model is constructed using limited training samples generated by Latin hypercube sampling and FEM, and it can replace FEM for evaluating nonlinear material behavior with negligible computational cost. Finally, the proposed topology optimization framework is applied to the design of a magnetic actuator, both with and without considering nonlinear B–H characteristics, demonstrating its effectiveness. Full article

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in traction and industrial drive systems because they combine high efficiency, high power density, and excellent performance over a wide speed range. Rotor eccentricity, however, remains one of the most significant faults affecting their reliable operation; diagnosing it early is crucial for avoiding unexpected breakdowns. To achieve this, the analysis utilizes a simulation-based fault diagnosis framework that combines the Finite Element Method (FEM) with machine learning. A 550 W, 220 V IPMSM was modeled in ANSYS Maxwell to simulate dynamic eccentricity faults at three severity levels: 10%, 20%, and 40%. A fixed-length, non-overlapping window segmentation approach was used to pull statistical features from the stator current and radial air-gap flux density signals. These features were then fed into several supervised machine learning algorithms, evaluated using a consistent 5-fold cross-validation protocol across all investigated classifiers, with the Ensemble Bagged Trees classifier achieving validation accuracies of 93.12% for radial air-gap flux density and 87.86% for stator current. By integrating finite-element analysis, statistical feature extraction, and comparative machine learning, the proposed framework demonstrates the feasibility of simulation-based dynamic eccentricity severity classification in IPMSMs. The results indicate that Ensemble Bagged Trees provide the best classification performance among the evaluated classifiers, establishing a foundation for future experimental validation and real-time condition-monitoring applications. Full article

In this work, we investigate the magnetization switching behavior of IrMn/NiFe bilayers as a function of NiFe thickness, temperature, and cooling-field direction. An unusual double-loop hysteresis behavior is observed in IrMn 10 nm/NiFe 40 nm bilayers, whereas a conventional single-loop hysteresis behavior is obtained for thinner NiFe layers. The observed double-loop feature exhibits temperature- and cooling-field-dependent asymmetry, suggesting a spatially nonuniform exchange interaction at the IrMn/NiFe interface. The opposite evolution of switching fields under positive and negative cooling fields indicates competing exchange contributions within the bilayer system. These results are consistent with a model involving distinct interfacial exchange regions or nonuniform uncompensated spin configurations in the IrMn layer. Our findings provide insight into the complex interfacial exchange behavior responsible for double-loop hysteresis in IrMn/NiFe bilayers and highlight the importance of interfacial magnetic inhomogeneity in exchange-biased spintronic structures. Full article

Organic-rich shale contains chemically heterogeneous mineral–organic interfaces that produce strong and spatially variable proton surface relaxation, complicating the identification of clay-bound water and the conversion of NMR relaxation time into pore size. Conventional interpretations commonly treat surface relaxivity as a constant, while the respective contributions of water-retaining surface chemistry, molecular restriction, and paramagnetic centers remain insufficiently separated. In this study, Wufeng–Longmaxi shale samples from the Yongchuan Block were investigated using mineralogical and pore structural characterization, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, cation exchange capacity, zeta potential, electron paramagnetic resonance, controlled hydration, one- and two-dimensional low-field time domain NMR, and molecular dynamics simulations. Under the present fluid and acquisition conditions, strongly surface-associated water was operationally identified mainly at T₂ < 1.6 ms and T₁ < 85 ms, while the effective transverse surface relaxivity ranged from 3.6 to 9.1 μm/s. Water retention was more closely associated with cation exchange capacity and surface –OH/O–C environments, whereas relaxation efficiency was controlled more directly by EPR-detectable paramagnetic centers and restricted molecular motion of interfacial water. Simulations of Na-smectite, chlorite, illite, and kerogen-covered illite revealed systematic differences in water density layering, adsorption strength, hydrogen bond persistence, molecular residence, translational diffusion, rotational reorientation, and proton–proton dipolar correlation. A chemistry-informed model combining the EPR-derived paramagnetic center density with a surface area-weighted molecular restriction index explained 86% of the measured relaxivity variation, with an adjusted R² of 0.83 and leave-one-out cross-validation RMSE and MAE values of 0.71 and 0.57 μm/s, respectively. At the core scale, the variable relaxivity interpretation reduced the mean absolute percentage error of characteristic pore diameter from 21.9% to 4.5% and the RMSE of the clay-bound water fraction from 3.4 to 0.4 percentage points relative to the fixed relaxivity method. Transfer to NMR logging further reduced lithology-dependent biases in pore size conversion and clay-bound water partitioning. These results define shale surface relaxivity as an emergent interfacial property arising from coupled magnetic and molecular controls and provide a mechanistic basis for NMR analysis of chemically heterogeneous shale materials. Full article

This work introduces a novel self-sustainable wireless communication architecture that combines RIS and NOMA with a hybrid acoustic–magnetic energy harvesting framework. The source node operates under strict energy constraints and is powered by harvesting ambient acoustic vibrations and surrounding magnetic fields, enabling autonomous transmission to multiple NOMA users. To enhance propagation conditions, an RIS is deployed to intelligently control the wireless channel by optimizing its phase response, thereby strengthening desired signals and suppressing interference.The performance of the proposed system is analytically characterized under the hybrid energy harvesting model. The derived expressions provide insight into the interaction between harvested energy dynamics, RIS configuration, and NOMA transmission. Simulation results confirm that the proposed RIS-NOMA architecture significantly outperforms conventional orthogonal and non-orthogonal access schemes in both spectral and energy efficiency. In addition, the hybrid harvesting mechanism ensures more stable energy availability, improving reliability in highly energy-constrained environments. Full article

Salvia elegans Vahl. is used to treat inflammatory conditions; however, its direct anti-inflammatory activity has been scarcely investigated. This study evaluated the ethyl acetate extract (SeAcOEt) in vivo and in vitro, using the 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mouse ear edema model, and the effects of selected isolated metabolites on inflammatory signaling by measuring NF-κB/AP-1-dependent SEAP reporter activity in LPS-stimulated RAW-Blue™ macrophages. Phytochemical characterization was performed using thin-layer chromatography, column chromatography, high-performance liquid chromatography (HPLC), mass spectrometry, and nuclear magnetic resonance (NMR). SeAcOEt yielded the following three fractions: SeFA, containing the terpene mixture rosifoliol (1)/agarospirol (2); SeFB, ursolic acid (3) and 7α-hydroxysandaracopimaric acid (4); and SeFC, jaceosidin (5) and isosakuranetin-5-O-rutinoside (6). SeAcOEt inhibited TPA-induced ear edema by 87.3%, whereas SeFB showed the highest activity (97.4%), comparable to indomethacin. The terpene mixture and 7α-hydroxysandaracopimaric acid reduced NF-κB/AP-1-dependent SEAP reporter activity in LPS-stimulated RAW-Blue™ macrophages. The anti-inflammatory activity of S. elegans could be associated with the combined contribution of phytochemicals enriched in the bioactive fraction SeFB rather than with a single active constituent. This study expands the phytochemical knowledge of S. elegans by reporting 7α-hydroxysandaracopimaric acid and provides pharmacological evidence supporting the traditional use of this species for inflammatory conditions. Full article

To mitigate global warming induced by excessive carbon dioxide emissions, carbon dioxide displacement technology for carbon utilization and storage has attracted growing attention. In this paper, laboratory displacement experiments combined with Nuclear Magnetic Resonance (NMR), constant-rate mercury intrusion, and X-ray diffraction (XRD) tests are adopted to investigate the residual storage characteristics of carbon dioxide in tight sandstone reservoirs. This study evaluates the effect of depletion pressure on carbon dioxide storage efficiency. Combined with the pore-throat fractal dimension (D_f) obtained from constant-rate mercury intrusion and the capillary tortuosity fractal dimension (D_T) calculated via models, the relevant controlling mechanisms are illustrated. The results show that the residual storage efficiency can exceed 62.00% when the depletion pressure is higher than the supercritical pressure of carbon dioxide. Storage efficiency exhibits a significant correlation with the average pore-throat ratio, indicating that pore-throat matching characteristics play a vital role in carbon dioxide retention. Both the pore-throat fractal dimension D_f and capillary tortuosity fractal dimension D_T are positively correlated with the average pore-throat ratio and negatively correlated with the pore-throat radius, reflecting the impacts of pore structure complexity and fluid channel tortuosity on carbon dioxide migration and storage processes. X-ray diffraction test results further verify that quartz and clay minerals indirectly affect carbon dioxide storage performance by altering the preservation status and connectivity of pore throats. The innovation of this study lies in establishing a coupled analysis system integrating pore-throat heterogeneity, fluid channel complexity, and carbon dioxide phase evolution, which reveals the residual storage mechanism of carbon dioxide in tight sandstone reservoirs. Relevant research findings provide new insights for studies on carbon dioxide storage laws at the pore scale, and offer theoretical support for optimizing geological carbon dioxide storage schemes in tight reservoirs. Full article

Intuitive response and multi-objective control are well-known features of Finite Control Set Predictive Torque Control (FCSPTC). However, its implementation difficulty is a result of limitations such as high computations, strong model parameter dependency in control, and time-consuming tuning efforts for weighting factors in a cost function. Particularly, when FCSPTC is applied to a Multi-Level Inverter (MLI)-supplied motor drive, computation complexity is predominant. The proposed research work in this paper offers a new FCSPTC to dual two-level VSIs supplied for an Open Winding Permanent Magnet Synchronous Motor (OWPMSM) drive. The contributions of the proposed FCSPTC for the OWPMSM drive are the following. (i) Simplicity: There are fewer operational steps in control and limited evaluations with five prediction voltage vectors (VVs) out of 19, and a flux weighting-factor-less design is used by adopting a voltage cost function. (ii) Robustness: There is less parameter dependency in control through the involvement of stator resistance only. (iii) Switching frequency reduction: The application of an optimal switching state from the redundant switching states of optimal VVs is used to generate fewer switching state transitions, where the process is independent from the switching frequency weighting factor. According to theoretical and experimental findings, it is evident that the proposed FCSPTC technique operates effectively without the weighting factor and provides the above-stated features, resulting in an optimal response in flux, torque and current, switching frequency reduction, and low-complexity operation. The proficiency of the proposed FCSPTC technique is experimentally verified by comparing it with existing control schemes. Full article

Ensuring food authenticity, traceability, and quality has become a critical challenge in increasingly complex and globalized food supply chains. Conventional post-harvest analytical approaches, while powerful, often operate in isolation and fail to fully capture the influence of pre-harvest conditions on food composition. In parallel, the emergence of smart farming technologies has enabled the collection of high-resolution environmental and agronomic data, offering new opportunities to establish baseline signatures linked to geographical origin and production practices. This review explores the integration of pre-harvest data from precision agriculture with advanced post-harvest analytical techniques, focusing on spectroscopic and isotopic methods for food authentication. Recent advances in vibrational spectroscopy, including near- and mid-infrared, Fourier-transform infrared, and Raman techniques, alongside complementary methods such as nuclear magnetic resonance and fluorescence spectroscopy, have enabled rapid and non-destructive food fingerprinting. In parallel, isotope ratio mass spectrometry and compound-specific isotope analysis provide robust markers of origin, climate conditions, and agricultural inputs through the analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen, and sulfur. The combination of these analytical approaches with chemometric and machine learning tools facilitates the extraction of meaningful patterns from complex datasets. A central focus of this review is the development of integrated farm-to-fork frameworks that use multi-source data, including field sensor technologies, spectral fingerprints, and isotopic signatures, to enhance traceability and authentication. Applications across a wide range of food systems, including edible oils, beverages, plant-based products, and animal-derived foods, are critically evaluated to highlight the strengths and limitations of current methodologies. Key challenges related to data standardization, system interoperability, cost, portability, miniaturization and regulatory acceptance are discussed, alongside emerging solutions such as artificial intelligence-driven models, digital twins, and blockchain-enabled traceability systems. The review underscores a paradigm shift from reactive testing toward predictive and real-time food authentication systems, driven by the convergence of smart agriculture and advanced analytical chemistry. This integrated approach has the potential to significantly enhance transparency, trust, and sustainability in the global food system. Full article

Magnetic integration couplers offer many advantages in wireless power transfer (WPT) system design, but additional coupling will lead to some drawbacks. This paper proposes a new magnetic integration scheme for an LCC–LCC compensation network in WPT systems. First, separate mutual inductance equivalent models are established for each type of additional coupling between the transmitting and receiving coils and the compensation coils. The effects of these additional couplings on the power-transfer characteristics are then analyzed. Second, finite element simulations are conducted to evaluate the decoupling performance of three magnetic integration schemes under aligned and misaligned conditions. The scheme employing DD-type transmitting and receiving coils arranged orthogonally with double-layer DD-type compensation coils demonstrates superior decoupling capability. Third, an integrated magnetic coupling mechanism is fabricated, and the coupling coefficients are measured under aligned and misaligned conditions. The proposed integration scheme effectively decouples multiple additional couplings, achieving about a 65% reduction in the compensation-coil additional coupling coefficient. Finally, an experimental prototype is implemented. The experimental results show a maximum transfer efficiency of 95.2%. Full article

Magnetic resonance imaging (MRI)-based prediction of multiple sclerosis (MS) progression depends on how imaging data are prepared, represented, modelled, and evaluated. This systematic review synthesized artificial intelligence (AI) methods across the MRI-to-prediction pipeline. PubMed, Scopus, Web of Science, and Google Scholar were searched for studies published from 2010 to March 2026. Eligible studies included patients with MS or clinically isolated syndrome, used brain MRI directly or as the source of predictors, evaluated future progression-related outcomes, and reported quantitative predictive performance. Thirty-seven studies were included and synthesized narratively because of methodological and outcome heterogeneity. Prediction targets included CIS-to-MS/CDMS conversion, future MRI disease activity, PIRA/PIRMA, disability worsening or confirmed progression, future EDSS or disability status, and RRMS-to-SPMS conversion. Lesion morphology and location, radiomics, tissue atrophy, longitudinal imaging changes, and multimodal variables provided useful prognostic information. Classical machine learning (ML) remained effective for structured biomarkers, while deep learning (DL) enabled direct image-based modelling; neither approach was uniformly superior. PROBAST + AI assessment rated model Development as High concern in 35 of 37 studies and Unclear in two, Evaluation as High risk of bias in all 37 studies, and Applicability as Low concern in 32, Unclear in two, and High in three. Overall, MRI-based AI shows promising prognostic potential, while stronger data separation, calibration, transparent reporting, and independent validation are needed to support reproducible clinical translation. Full article