Search Results (8,064)

Magnetite (Fe₃O₄) is an essential material for enhancing microwave absorption performance and is widespread and abundant as a solid solution in natural minerals and metallurgical slags. In this work, the effect of Mg²⁺ on the structure, stabilization, and microwave absorption performance of magnesium-containing magnetite (Mg_xFe_3−xO₄) was investigated. On the basis of experiments on the reactions of Fe₂O₃ and MgO under different levels of p_CO/(p_CO + p_CO2), Mg_xFe_3−xO_{4 (x=0.0,0.2,0.4,0.6,1.0)} was synthesized, and Mg²⁺ was found to inhibit the re-oxidation of magnetite. On this basis, the microwave absorption performance of various synthesized Mg_xFe_3−xO₄ samples was measured and analyzed, where Mg²⁺ was found to enhance the microwave absorption performance of Fe₃O₄, and the RL_min value of Mg_0.2Fe_2.8O₄ increased to −50.43 dB compared to that of −19.20 dB for Fe₃O₄. Furthermore, the enhancement mechanism of Mg²⁺ was revealed through impedance matching, dielectric and magnetic loss tangents, and magnetization curves, where the Mg²⁺ ions were found to accelerate the hopping of electrons and change the impedance matching of Mg_xFe_3−xO₄ to a more ideal state. Full article

This study presents the design and application of an electrochemical sensor for selective detection of lead ions (Pb²⁺) based on ionophore-modified raspberry-like Fe₃O₄–Au nanostructures. The material was engineered with a magnetic Fe₃O₄ core, coated with polyethyleneimine (PEI) to facilitate nucleation, and subsequently decorated with Au nanoparticles, providing a raspberry-like (Fe₃O₄@PEI@AuNPs) nanostructure with high surface area and excellent electrochemical conductivity. Surface functionalization with Lead Ionophore IV (ionophore thiol) introduced Pb²⁺-selective binding sites, whose presence and structural evolution were verified by TEM and Raman spectroscopy. The Fe₃O₄ core endowed strong magnetic properties, enabling facile manipulation and immobilization onto screen-printed carbon electrodes (SPCEs) via physical adsorption, while the Au nanoparticles enhanced electron transfer, supplied thiol-binding sites for stable ionophore anchoring, and increased the effective electroactive surface area. Operational conditions were systematically optimized, with acetate buffer (HAc/NaAc, pH 5.7) and chronoamperometric preconcentration (CA) at −1.0 V for 175 s identified as optimal for differential pulse voltammetry (DPV) measurements. Under these conditions, the sensor exhibited a linear response toward Pb²⁺ from 0.025 mM to 2.00 mM with superior sensitivity and reproducibility compared to conventional AuNP-modified SPCEs. Furthermore, the ionophore-modified Fe₃O₄–Au nanostructure-based sensor demonstrated outstanding selectivity for Pb²⁺ over competing heavy metal ions (Cd²⁺, Hg²⁺, Cr³⁺), owing to the specific coordination interaction of Lead Ionophore IV with target ions. These findings highlight the potential of raspberry-like Fe₃O₄@PEI@AuNP nanostructures as a robust and efficient electrochemical platform for the sensitive and selective detection of toxic heavy metal ions. Full article

In extremely harsh high-temperature environments in aerospace, industrial manufacturing and other fields, traditional ultrasonic temperature measurement technology has certain limitations. This paper proposes a differential single crystal sapphire ultrasonic temperature measurement method based on the magnetostrictive effect. This method abandons the traditional sensitive flexural structure and uses two single-crystal sapphire waveguides of the same material, same diameter, and slightly different lengths as sensing elements. By measuring the time delay difference between their end-face echoes, the sound velocity is inverted and the temperature is measured. COMSOL multi-physics v6.1 simulation was used to optimize the bias magnetic field design of the magnetostrictive transducer, which improved the system’s energy conversion efficiency and high-temperature stability. Experimental results show that in the range of 300–1200 °C, the sensor delay increases monotonically with increasing temperature, the sound speed shows a downward trend, and the repeatability error is less than 5%; the differential processing method effectively suppresses common mode noise in the range of 300–700 °C, and still shows high sensitivity above 800 °C. This research offers a technical solution with high reliability and accuracy for temperature monitoring in extreme environments such as those characterized by high temperatures and high pressures. Full article

Background/Objectives: Mitral annular disorders constitute a heterogeneous group of structural abnormalities that can significantly influence morbidity and mortality in both adult and pediatric populations. Advances in cardiac magnetic resonance (CMR) imaging have refined the ability to characterize these disorders with high spatial resolution and reproducibility. Among them, mitral annular disjunction (MAD) and mitral valve prolapse (MVP) have emerged as interrelated entities implicated in valvular dysfunction, arrhythmogenesis, and myocardial remodeling. This study aimed to determine the prevalence of MAD in a pediatric cohort, explore its association with MVP, and delineate related CMR findings, including myocardial fibrosis. Methods: A retrospective review was conducted in 295 pediatric patients who underwent clinically indicated CMR between September 2022 and June 2025. Echocardiographic and CMR data were systematically compared for the detection of MAD, MVP, and mitral regurgitation (MR). MAD length and mitral annular measurements were obtained from two-chamber and left ventricular outflow tract (LVOT) cine sequences. Late gadolinium enhancement (LGE) was evaluated to identify myocardial fibrosis. Results: MAD was detected more frequently by means of CMR than echocardiography (23.2% vs. 9.3%), as was MVP (34.2% vs. 22.4%), whereas MR was more often observed on echocardiography (31.2% vs. 15.2%). Inter-modality agreement was moderate for MAD, moderate-to-substantial for MVP, and fair for MR. LGE was identified only in patients with concomitant MAD and MVP, suggesting limited myocardial involvement in isolated MAD. Conclusions: CMR demonstrates superior sensitivity in detecting MAD and MVP compared with echocardiography and allows for early recognition of systolic–diastolic annular dissociation before advanced myocardial remodeling occurs. These findings underscore the clinical utility of CMR as a complementary modality for comprehensive assessment, risk stratification, and follow-up of pediatric patients with suspected mitral annular abnormalities. Full article

Magnetic Particle Imaging (MPI) is a new type of tracer-based imaging that has great spatial and temporal resolution, does not require ionizing radiation, and can see deep into tissues by directly measuring the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs). Unlike Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), MPI has very high contrast and quantitative accuracy, which makes it perfect for use in dynamic cardiovascular applications. This study presents a full picture of the most recent changes in cardiac MPI, such as the physics behind Field-Free Point (FFP) and Field-Free Line (FFL) encoding, new ideas for tracer design, and important steps in the evolution of scanner hardware. We discuss the clinical relevance of cardiac MPI in visualizing myocardial perfusion, quantifying blood flow, and guiding real-time interventions. A hybrid imaging workflow, which improves anatomical detail and functional assessment, is utilized to explore the integration of MPI with complementary modalities, particularly MRI. By consolidating recent preclinical breakthroughs and highlighting the roadmap toward human-scale implementation, this article underscores the transformative potential of MPI in cardiac diagnostics and image-guided therapy. Full article

In this study, a modified Curie–Weiss model was established for the magnetic susceptibility of high-purity molybdenum and Mo–La alloy powders. The elemental composition was analyzed by GDMS, and combined with the M–T and M–H data measured by SQUID, the temperature-independent contributions of weakly magnetic elements such as La and the paramagnetic contributions of impurity ions such as Fe, Co, and Ni were distinguished. Based on the parameters obtained from the nonlinear least squares fitting, the deviation between the magnetic susceptibility at room temperature calculated by the model and the experimental value was within 5%. The results show that this model can reasonably describe the influence of trace impurities on the magnetic susceptibility of the system and provides an effective method for the magnetic prediction of high-purity metal powders. Full article

Permanent Magnet Synchronous Motors (PMSMs) have been widely applied across various electrical systems due to their significant advantages, including high power density, high-efficiency conversion, and easy controllability. However, the issue of ‘parameter asymmetry’ (a mismatch between the controller’s preset parameters and the actual system parameters) in PMSMs can lead to performance problems, such as delayed speed response and increased overshoot. The destruction of symmetry, including the asymmetric weight distribution between new and old data in the moment-of-inertia identification algorithm and the asymmetry between “measured values and true values” caused by sampling delay, is the core factor limiting the system’s control performance. All these factors significantly affect the accuracy of parameter identification and the system’s stability. To address this, this study focuses on the mechanical parameter identification of PMSMs with the core goal of “symmetric matching between set values and true values”. Firstly, a current-speed dual closed-loop vector control system model is constructed. The PI parameters are tuned to meet the symmetric tracking requirements of “set value-feedback” in the dual loops, and the influence of the PMSM’s moment of inertia on the loop symmetry is analyzed. Secondly, the symmetry defects of traditional algorithms are highlighted, such as the imbalance between “data weight and working condition characteristics” in the least-squares method and the mismatch between “set inertia and true inertia” caused by data saturation. Finally, a Forgetting Factor Recursive Least Squares (FFRLS) scheme is proposed: the timing asymmetry of signals is corrected via a first-order inertial link, a forgetting factor λ is introduced to balance data weights, and a recursive structure is adopted to avoid data saturation. Simulation results show that when λ = 0.92, the identification accuracy reaches +5% with a convergence time of 0.39 s. Moreover, dynamic symmetry can still be maintained under multiple multiples of inertia, thereby improving identification performance and ensuring symmetry in servo control. Full article

We report the implementation of replica-averaged molecular dynamics in the UNRES coarse-grained model of polypeptide chains, with application to the restraints determined by nuclear magnetic resonance. The analytical ESCASA algorithm is used to estimate interproton distances from coarse-grained geometry. With synthetic restraints derived from two selected conformations of the L129–L153 loop of the Slr1183 protein from Synechocystis sp. (2KW5), the replica-averaged extension of UNRES retrieved the ensemble of conformations close to the parent structures, with residual content of those not similar to any of them, and comparable populations of both families. Tests with a small putatively multistate protein (PDB: 2LWA) and two proteins with disordered regions (2KW5 and 2KZN, respectively) run in multiplexed temperature replica exchange mode with replica averaging resulted in conformational ensembles that had fewer distance-restraint violations than those deposited in the Protein Data Bank. The ensembles obtained with replica averaging also had fewer distance-restraint violations than those obtained in our previous work, in which time-averaged restraints were implemented. The upgraded UNRES can be used in data-assisted simulations of multistate and intrinsically-disordered proteins and proteins with intrinsically disordered regions. Full article

A temporally periodic model is presented to describe the vertical profile of internal gravity waves in the F region of the Earth’s ionosphere where the waves are subject to a magnetic force due to the high concentration of ions. The configuration studied is representative of the situation where the geomagnetic field is approximately constant and is so strong that the angular gyrofrequency of the ions is very large compared with the ion-neutral collision frequency, which is in turn larger than the angular frequency of the gravity waves. We examine the situation where the gravity wave amplitude is small enough that the equations for the neutral fluid flow can be linearized. This allows for the description of wave propagation in terms of a system of coupled equations that include the effects of ion drag on waves for any orientation of the magnetic field. It is assumed that the background neutral fluid flow is nonzero and horizontal, but there is no vertical shear, and that the wave amplitude depends on altitude only, and an exact analytical solution is readily found. This dynamical model captures some essential features of ionospheric gravity waves that are consistent with observational measurements. In particular, the ion drag acts to damp the waves in the direction of vertical propagation and increase their vertical wavelength relative to the corresponding wavelength in the neutral atmosphere. The vertical damping rate and the vertical wavelength both depend on the dip angle of the magnetic field. When the magnetic field acts in the direction of the gravity lines of constant phase, there is no damping, and the vertical wavelength is the same as that of the corresponding waves in the neutral atmosphere. The dip angles that produce stronger damping also result in waves with greater wavelengths. Full article

Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

Magnetic guides facilitate frictionless movement in machine tools, allowing for high position accuracy and high feed rate dynamics. However, achieving a stable state while subjected to machining forces requires a high magnetic force, which will cause deformation of the surrounding construction. This deformation leads to the contraction of the air gap between the slide and guideways. The air gap has a quadratic effect on the magnetic force, which poses a challenge to the control system. In addition, the effect of elastic deformation on magnetic guides is difficult to detect in real time due to the interference of magnetic fields in sensors and installation space constraints. In order to quantitatively analyze the effects of magnetic guide deformations, particularly of electrical steels, a combined approach of numerical analysis and experimental evaluation was conducted. As a result, the deformation of electrical steels, which is typically overlooked in conventional applications, is identified and analyzed. Force stabilization is achieved through adaptive set point correction and current adjustment, ensuring reliable and accurate operation of the magnetic guide system. Full article

Objective: Obesity increases the risk of endometrial cancer (EC). In this study, we aimed to investigate the prognostic effect of sarcopenia, sarcopenic obesity and sarcopenic visceral obesity, calculated with the help of cross-sectional imaging methods of muscle and visceral adipose tissue from body composition parameters, in EC. Methods: Patients diagnosed with EC were identified between January 2014 and June 2024. The combination of radiological markers and patient outcomes can predict prognosis. The skeletal muscle index (SMI) and visceral fat index (VFI) were calculated from computed tomography (CT) and/or abdominal magnetic resonance (MR) scans taken at the time of diagnosis at the Lumbal 3 (L3) vertebra level. The findings of these analyses demonstrate the strongest correlation with the ratio of muscle and visceral fat tissue throughout the body. The loss of muscle and fat is an unfavourable indicator in patients with EC. The present study analysed the prognostic values of sarcopenia, sarcopenic obesity, sarcopenic visceral obesity, and the visceral fat index in EC. The total skeletal muscle area was calculated in square centimetres. Body surface area (m²) was calculated using the Mosteller formula: ((height (cm) × weight (kg))/3600)^1/2. To normalize body composition components, the skeletal muscle index was calculated as cm²/m². Results: The study comprised a total of 236 EC patients. The prevalence of sarcopenia, sarcopenic obesity, and sarcopenic visceral obesity were found to be 48.31%, 33.47%, and 22.88%, respectively. The presence of sarcopenia, high VFI levels, sarcopenic obesity, and sarcopenic visceral obesity did not demonstrate statistical significance in the survival analysis. However, stage increase (p = 0.001), primary tumour localization in the lower uterine segment (p = 0.001), serous carcinoma (p = 0.001), increased grade in endometrioid carcinoma (p = 0.023), and lymphovascular invasion (p = 0.001) were significantly associated with increased mortality risk. The presence of sarcopenia was found to be significant in patients with obesity (p = 0.008) and those aged ≥ 65 years (p = 0.001). Conclusions: In EC survival, established prognostic factors such as serous histopathology, LVI positivity, and the extent of surgical staging are prioritised. The presence of these well-established markers means the potential effect of BMI-based observations, such as the ‘obesity paradox’, and even body composition measurements, such as sarcopenic obesity, are now statistically insignificant. Our findings suggest that aggressive tumour biology (serous type, LVI) and surgery, rather than metabolic variables such as sarcopenia, sarcopenic obesity and sarcopenic visceral obesity, are the direct reason for the survival difference. This is due to the tumour’s aggressive nature and clinical characteristics (e.g., age at diagnosis, operability, stage, primary tumour localization in the lower uterine segment, serous carcinoma, grade, and LVI positivity) rather than metabolic variables. Full article

Variograms are a cornerstone of spatial analysis in geostatistics, traditionally applied to real-valued variables under the intrinsic hypothesis. Many soil properties, particularly when integrating magnetic and geochemical measurements, can be expressed as complex-valued variables that capture both magnitude and phase information. In the case of magnetic susceptibility, the imaginary component reflects energy losses associated with viscous magnetization, which in soils can indicate the presence of pedogenic ferrimagnetic minerals, while its relative increase may also reveal anthropogenic magnetite contamination. This study examines the formulation and application of variograms for such complex-valued variables in the context of soil research. Two complementary definitions are considered: an intrinsic-based approach, which directly estimates the variogram from increments and is applicable under the intrinsic hypothesis, and a covariance-based approach, which requires stronger second-order stationarity. Simulated complex-valued soil property data with controlled spatial structures were used to compare the behaviour of these formulations with their real-valued counterparts. The findings indicate that complex-valued variograms preserve additional spatial information, particularly related to local phase shifts, while maintaining compatibility with conventional variographic modelling. Full article

Double perovskites are represented by the formula A₂BB’O₆ and AA’BB’O₆. These materials have been synthesized using the solid-state reaction, sol–gel, Pechini, and hydrothermal methods. X-ray fluorescence, X-ray diffraction, magnetic measurements, transmission electron microscopy, X-ray photoelectron spectroscopy, temperature-programmed reduction, synchrotron X-ray diffraction, neutron powder diffraction, extended X-ray absorption fine structure, and Raman spectroscopy have been used for the characterization of double perovskites. X-ray diffraction, synchrotron X-ray diffraction, and neutron powder diffraction coupled with the Rietveld method determine the crystal structure of a sample. These materials present various properties and applications. The present review aims (i) to report a process to determine the symmetry, apparent size, and apparent strain using the Rietveld method; (ii) show how experimental characterization techniques complement each other in the investigation of double perovskites; (iii) describe how the synthesis method can help in the uncovering of double perovskites with improved properties; and (iv) exemplify some of the main applications of double perovskites. Full article