Search Results (99)

Recent applications of low-field NMR in alkali-activated materials (AAMs) often adopt interpretation models developed for Portland cement systems, overlooking the distinct influences of paramagnetic/ferrimagnetic components and free-water redistribution. This study investigates how paramagnetic or ferrimagnetic component and free water distribution influence low-field nuclear magnetic resonance (LF-NMR) and proton density magnetic resonance imaging (PD-MRI) characterization of alkali-activated materials (AAMs). Blast furnace slag, fly ash, and steel slag were activated with NaOH solution at liquid-to-solid ratios of 0.45 and 0.5, and analyzed across top, middle, and bottom layers. Slurries prepared with less mixing water and CaO-rich raw materials exhibited negligible settling and uniform relaxation behavior, whereas those with higher water content and CaO-deficient raw materials showed pronounced stratification, resulting in distinct gradients in signal intensity. The results indicate that the LF-NMR data interpretation of relatively dilute system may be unreliable as the relaxation time of protons will be extended after they transfer from bottom to the top of the slurry. A preliminary method for assessing slurry suitability for LF-NMR characterization is proposed for future validation. Full article

Diffusion models have recently emerged as a new paradigm to solve inverse problems in magnetic resonance imaging (MRI). This paper systematically categorizes forty representative studies that use diffusion models for MRI reconstruction. The studies are systematically divided into three dimensional categories: (1) model type distribution that includes standard, score-based diffusion, hybrid and physics-informed diffusion, latent diffusion and cold diffusion; (2) application domain distribution that includes accelerated, motion-corrected, dynamic, quantitative and cross-modal MRI; and (3) domain of reconstruction distribution that includes image-space, k-space, latent-space and hybrid approaches. In this context, diffusion-driven methods represent a significant methodological shift, evolving from purely data-driven denoising toward unified, physics-aligned reconstruction frameworks. Hybrid and cold diffusion approaches further integrate probabilistic generative priors with explicit MRI acquisition physics, which enables improved fidelity, artifact suppression and enhanced generalization across sampling regimes and anatomical domains. This study reviews current advances in the field of MRI reconstruction methods and gives future research directions, emphasizing the need for interpretable, data-efficient and multi-domain generative models that are capable of robustly addressing the diverse challenges of MRI reconstruction. Full article

Background: Research on the impact of 5G mobile phone electromagnetic exposure on corticospinal excitability and intracortical mechanisms is still poorly understood. Objective: This randomized controlled pilot study explored the effects of 5G mobile phone exposure at 3.6 GHz (power density: 0.0030 W/m²) on corticospinal excitability and intracortical mechanisms in healthy adults. Methods: Nineteen healthy participants (mean age: 36.5 years) were exposed to 5G mobile phone exposure for 5 and 20 min, approximating the typical duration of a phone call. Corticospinal excitability, intracortical facilitation, short intracortical inhibition, and long intracortical inhibition using single- and paired-pulse transcranial magnetic stimulation assessed before and immediately after exposure were performed. Results: A two-way repeated-measures ANOVA revealed no significant interactions between exposure condition (5 min, 20 min, sham) and time (pre vs. post) for CSE, ICF, SICI, or LICI (all p > 0.15). Bayesian analyses yielded Bayes factors close to 1, indicating inconclusive evidence for both the null and alternative hypotheses. Conclusion: Short-term exposure to 5G mobile phone electromagnetic fields did not produce detectable changes in corticospinal or intracortical excitability. Bayesian evidence was similarly inconclusive (Bayes factors ≈ 1), suggesting that the data provide limited support for either the presence or absence of a detectable effect. Any potential influence of 5G exposure on neural function is therefore likely to be subtle with the present methods. As a pilot study, these findings should be interpreted cautiously and underscore the need for further research using more sensitive outcome measures, extended exposure durations, and vulnerable populations. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Nowadays, most of the heavy oil fields around the world have entered difficult exploiting stages, with problems regarding high viscosity and poor fluidity. However, there has been little previous research on the accurate identification and distribution of remaining oil with different levels of steam dryness. Therefore, this paper proposes a new nuclear magnetic resonance (NMR) interpretation method, as well as a new samples analysis method for remaining oil in the core. We conducted core displacement experiments using different methods. The nuclear magnetic resonance (NMR) tests and analysis of core thin sections after steam flooding were used to study the effect of different steam dryness levels on the migration and sedimentation mechanisms of heavy oil components. The results showed that the viscosity of crude oil and the permeability of rock cores are both sensitive to steam dryness; therefore, the improvement of steam dryness is beneficial for improving oil recovery. Heavy oil is mainly distributed in the medium pores of 10–50 μm and the small pores of 1–10 μm. However, with the decrease in steam dryness, the dynamic amount of crude oil in both medium and small pores decreases, and the bitumen in crude oil stays in the pores in the form of stars, patches, and envelopes, which leads to a decline in oil displacement efficiency. Thus, our study provides a micro-level understanding of remaining oil which lays the foundation for the further enhancement of oil recovery in heavy oilfields. Full article

Background: The accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is pivotal for timely clinical intervention, yet remains challenged by tumor heterogeneity, morphological variability, and imaging artifacts. Methods: This paper presents a novel hybrid approach for improved brain tumor classification and proposes a novel hybrid deep learning framework that amalgamates the hierarchical feature extraction capabilities of VGG-16, a convolutional neural network (CNN), with the global contextual modeling of FTVT-b16, a fine-tuned vision transformer (ViT), to advance the precision of brain tumor classification. To evaluate the recommended method’s efficacy, two widely known MRI datasets were utilized in the experiments. The first dataset consisted of 7.023 MRI scans categorized into four classes gliomas, meningiomas, pituitary tumors, and no tumor. The second dataset was obtained from Kaggle, which consisted of 3000 scans categorized into two classes, consisting of healthy brains and brain tumors. Results: The proposed framework addresses critical limitations of conventional CNNs (local receptive fields) and pure ViTs (data inefficiency), offering a robust, interpretable solution aligned with clinical workflows. These findings underscore the transformative potential of hybrid architectures in neuro-oncology, paving the way for AI-assisted precision diagnostics. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.46% and 99.90%, respectively. Conclusions: Future work will focus on multi-institutional validation and computational optimization to ensure scalability in diverse clinical settings. 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

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

The aging of asphalt mixtures has a significant impact on the service life of asphalt pavements. Currently, the commonly employed method for assessing aging involves the extraction of asphalt from asphalt mixtures using the Abson method. However, this method is known to be detrimental to the extracted asphalt samples, time-consuming, and environmentally unfriendly. This study explored a novel non-destructive method for assessing asphalt aging, known as low-field nuclear magnetic resonance (LF-NMR). It primarily investigated the influence of asphalt content in asphalt mixtures on the patterns of LF-NMR spectra. Specifically, it examined the effect of asphalt content on LF-NMR spectra in asphalt mixtures with varying particle sizes and aging levels at the same detection temperature. Additionally, machine learning was used to establish predictive models linking NMR spectral features to asphalt mixture aging levels, enhancing interpretation accuracy. The research results revealed the following: (1) Spectral parameters such as peak height, normalized peak area, and normalized total peak area had a significant impact on the first principal component of LF-NMR spectra. (2) Asphalt content in the mixture increased as particle size decreased, leading to corresponding changes in the LF-NMR spectra. (3) There was a strong correlation between the aging degree of asphalt and asphalt mixtures and the normalized total peak area of their LF-NMR spectra. The study provides a non-destructive method to assess asphalt mixture aging, enabling timely maintenance decisions and improving pavement durability. Full article

Three-dimensional (3-D) magnetic inversion is an essential technique for revealing the distribution of subsurface magnetization structures. Conventional methods are often time-consuming and suffer from ambiguity due to limited observations and non-uniqueness. To address these limitations, we propose a novel inversion method under the machine learning framework. First, we design a training sample generation space by extracting the horizontal positions of magnetic sources from the analytic signal amplitude and the reduced-to-the-pole anomalies of magnetic field data. We then employ coordinate transformation to achieve data augmentation within the designed space. Subsequently, we utilize a broad learning network to map the magnetic anomalies to 3-D magnetization structures, reducing the magnetic inversion time. The efficiency of the proposed method is validated through both synthetic and field data. Synthetic examples indicate that compared to the traditional inversion method, the proposed method approximates the true model more closely. It also outperforms traditional and deep learning methods in terms of computational efficiency. In the field example of the Danzhukeng Pb-Zn-Ag deposit in South China, the inversion result is consistent with drilling and controlled-source audio frequency magnetotelluric survey data, providing valuable insights for subsequent exploration. This study provides a new practical tool for processing and interpreting magnetic anomaly data. Full article

We report on the arc melt syntheses of HoB₂ and Nb-substituted HoB₂ polycrystalline ingots and their magnetocaloric and microstructural properties. XRD data and microstructural analysis reveal that a nominal 10% Nb addition during synthesis results in changes to unit cell parameters and grain morphology. Interpretation of the refined cell parameters using Vegard’s law shows that Nb substitutes into HoB₂ with stoichiometry Ho_0.93Nb_0.07B₂. Arc-melted products are polycrystalline bulk samples containing minor phases such as Ho₂O₃, Ho, and HoB₄. Nb substitution results in a smaller grain size (~sub-micron) and a higher Curie temperature, T_C, compared to HoB₂. With a 10 T applied field, the maximum magnetic entropy, ΔS_M, for HoB₂ and for Ho_0.93Nb_0.07B₂, is 46.8 Jkg⁻¹K⁻¹ and 38.2 Jkg⁻¹K⁻¹ at 18 K and 21 K, respectively. Both samples show second-order phase transitions. Despite high totals of minor phases (e.g., ~10 wt.% and ~25 wt.%), the calculated relative cooling powers are greater than 1300 Jkg⁻¹ and 600 Jkg⁻¹ at 10 T and 5 T, respectively. The magnetocaloric properties of both samples are consistent with Holmium boride compounds prepared via alternative methods. Full article

Forward modeling the magnetic effects of an inferred source is the basis of magnetic anomaly inversion for estimating subsurface magnetization parameters. This study uses numerical least-squares Gauss–Legendre quadrature (GLQ) integration to evaluate the magnetic potential, anomaly, and gradient components of a cylindrical prism element. Relative to previous studies, it quantifies for the first time the magnetic gradient components, enabling their applications in the interpretation of cylindrical bodies. A comparison of this method to other methods of evaluating the vertical component of the magnetic field associated with a full cylinder shows that it has comparable to improved performance in computational accuracy and speed. Based on the developed theory, a conceptual design is presented for an instrument to measure the magnetic gradient effects of subsurface material in the vicinity of a borehole. The significance of this instrument relative to conventional borehole magnetometers is in its ability to determine the azimuthal directions of magnetic sources within the borehole environment. Full article

Objectives: Computer-aided schizophrenia (SZ) detection methods mainly depend on electroencephalogram and brain magnetic resonance images, which both capture physical signals from patients’ brains. These inspection techniques take too much time and affect patients’ compliance and cooperation, while difficult for clinicians to comprehend the principle of detection decisions. This study proposes a novel method using face diagnosis images based on traditional Chinese medicine principles, providing a non-invasive, efficient, and interpretable alternative for SZ detection. Methods: An innovative face diagnosis image analysis method for SZ detection, which learns feature representations based on Vision Transformer (ViT) directly from face diagnosis images. It provides a face features distribution visualization and quantitative importance of each facial region and is proposed to supplement interpretation and to increase efficiency in SZ detection while keeping a high detection accuracy. Results: A benchmarking platform comprising 921 face diagnostic images, 6 benchmark methods, and 4 evaluation metrics was established. The experimental results demonstrate that our method significantly improves SZ detection performance with a 3–10% increase in accuracy scores. Additionally, it is found that facial regions rank in descending order according to importance in SZ detection as eyes, mouth, forehead, cheeks, and nose, which is exactly consistent with the clinical traditional Chinese medicine experience. Conclusions: Our method fully leverages semantic feature representations of first-introduced face diagnosis images in SZ, offering strong interpretability and visualization capabilities. It not only opens a new path for SZ detection but also brings new tools and concepts to the research and application in the field of mental illness. Full article

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T₁ and T₂ peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields. Full article

The longitudinal and transverse nuclear magnetic resonance relaxivity dispersion (NMRD) of ¹H in water induced by the paramagnetic relaxation enhancement (PRE) of dissolved lanthanide ions (Ln³⁺) can become very strong. Longitudinal and transverse ¹H NMRD for Gd³⁺, Dy³⁺, Er³⁺ and Ho³⁺ were measured from 20 MHz/0.47 T to 1382 MHz/32.5 T, which extended previous studies by a factor of more than two in the frequency range. For the NMRD above 800 MHz, we used a resistive magnet, which exhibits reduced field homogeneity and stability in comparison to superconducting and permanent NMR magnets. These drawbacks were addressed by dedicated NMRD methods. In a comparison of NMRD measurements between 800 MHz and 950 MHz performed in both superconducting and resistive magnets, it was found that the longitudinal relaxivities were almost identical. However, the magnetic field fluctuations of the resistive magnet strongly perturbed the transverse relaxation. The longitudinal NMRDs are consistent with previous work up to 600 MHz. The transverse NMRD nearly scales with the longitudinal one with a factor close to one. The data can be interpreted within a PRE model that comprises the dipolar hyperfine interactions between the ¹H and the paramagnetic ions, as well as a Curie spin contribution that is dominant at high magnetic fields for Dy³⁺, Er³⁺ and Ho³⁺. Our findings provide a solid methodological basis and valuable quantitative insights for future high-frequency NMRD studies, enhancing the measurement accuracy and applicability of PRE models for paramagnetic ions in aqueous solutions. Full article