Search Results (213)

In magnetometer, accelerometer, and rate gyroscope (MARG) attitude and heading reference systems, accelerometers and magnetometers are susceptible to external acceleration and soft/hard magnetic anomalies, which reduce the attitude estimation accuracy. To address this problem, a sequential adaptive Kalman filter algorithm based on the geophysical field is proposed for anti-interference MARG attitude estimation. By establishing the linear system model based on the gravitational field and geomagnetic field, the singularity and coupling in other system models are avoided. Additionally, the sequential Sage–Husa adaptive strategy is employed to estimate the measurement noise parameters in real time by a specific force and magnetic vector, which suppresses the impact of external acceleration and the soft/hard magnetic anomalies. To verify the effectiveness and advancement of the proposed algorithm, a series of anti-interference experiments were designed. Experimental results show that, compared with the geophysical-field-based Kalman filter algorithm without an adaptive strategy, the proposed improved algorithm reduces the yaw maximum error by over 94% and inclination maximum error by over 21%, which improves the MARG attitude estimation robustness and makes this algorithm superior to the existing three adaptive strategies and two algorithms. Full article

Underground gas storage (UGS) is critical to national reserves and seasonal peak-shaving, and its safe operation is integral to energy security. In UGS surface process pipelines, heterogeneous bimetal composite pipes—carbon-steel substrates lined with stainless steel—are widely used but susceptible under coupled thermal–pressure–flow loading to geometry-induced instabilities (local buckling, adhesion, and collapse), which can restrict flow, concentrate stress, and precipitate rupture and unplanned shutdowns. Conventional ultrasonic testing and magnetic flux leakage have limited sensitivity to such instabilities, while standard eddy-current testing is impeded by the ferromagnetic substrate’s high permeability and electromagnetic shielding. This study introduces magnetically saturated pulsed eddy-current testing (MS-PECT). A quasi-static bias field drives the substrate toward magnetic saturation, reducing differential permeability and increasing effective penetration; combined with pulsed excitation and differential reception, the approach improves defect responsiveness and the signal-to-noise ratio. A prototype was developed and evaluated through mechanistic modeling, numerical simulation, laboratory pipe trials, and in-service demonstrations. Field deployment on composite pipelines at the Hutubi UGS (Xinjiang, China) enabled rapid identification and spatial localization of liner collapse under non-shutdown or minimally invasive conditions. MS-PECT provides a practical tool for composite-pipeline integrity management, reducing the risk of unplanned outages, enhancing peak-shaving reliability, and supporting resilient UGS operations. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Multi-centennial C budgets in forested watersheds require information on forest growth, detritus turnover, and disturbances, as well as the transfer to and fate of terrestrial C in aquatics systems. Here, a sediment gravity core was collected from a drinking water reservoir in Canada, and analyzed for temporal changes in charcoal, magnetic susceptibility, carbon, and nitrogen. These indicators were used to assess disturbance history and terrestrial C sequestration in sediments. During the reservoir development period from 1910 to 2012, charcoal flux and magnetic susceptibility increased ca. 10 years after nearby fire and forest-clearing events associated with reservoir expansion. Total C and δ¹³C gradually declined during the development period, likely due to increased inputs of mineral soil from human activity. Concurrently, total terrestrial C sequestered in sediments, estimated using three or eight marker compounds, ranged between 3557 and 5145 Mg C/100 yrs, accounting for 11%–17% of DOC exports to the reservoir (30,640 Mg C/100 yrs), as estimated from a previously developed terrestrial carbon budget model. In comparison, mixed-severity fires burned around the reservoir during the pre-development period (pre-1910), as evidenced by stand ages and/or increases in charcoal flux. In general, decreased terrestrial C flux was associated with higher-severity fires that burned between 1870 and 1890 and perhaps around 1790. Further, comparisons show that soil erosion was up to 3× greater in the development versus the pre-development period. Overall, this investigation revealed the impact of land use change and fire on watershed carbon budgets and advanced a previously developed pyGC-MS methodology that demonstrated the amount of terrestrial and aquatic C being buried in sediment. It also identified the fraction of terrestrial C that was exported from the forest to the reservoir and sequestered in the sediment, uncommon data that could inform current and future landscape C budget modelling studies in this region. Full article

The 2021 eruption of the Tajogaite volcano (La Palma, Canary Islands) provided a unique opportunity to investigate the early post-eruptive magnetic structure of a newly formed volcanic edifice. Understanding these structures is essential for improving hazard assessment and risk mitigation strategies. In this study, we present the first high-resolution, drone-based aeromagnetic dataset over the Tajogaite volcano, aimed at clarifying its still-uncertain geodynamic framework at shallow depths. We describe the data acquisition and processing workflows for surveying volcanic terrains, providing insights into the challenges encountered and the methodologies applied. The magnetic dataset was analyzed and used to construct a 3D magnetic susceptibility model of the volcanic edifice and its surroundings. Our results revealed very low magnetic susceptibility values at very shallow depths (~50 m below the surface) over the main volcanic edifice, suggesting the presence of a likely vertical, dyke-like structure feeding the eruption. These findings indicate that these materials remain above their Curie temperature around two years after the eruption. Moreover, the magnetic anomalies display patterns that correlate with the previously inferred two-fault systems, which likely played a critical role in channelling magma toward the eruptive vents. An elongated zone of slightly low magnetic susceptibility was identified following the NE-SW Mazo fault orientation, extending toward the eruptive fissure. This feature was associated with a single, fault-controlled magma pathway that remained at high temperatures at the time of the survey, in agreement with studies in other volcanic environments. This study highlights the value of aeromagnetic surveys, particularly those conducted with drones, as effective tools for advancing our understanding of young and dynamic volcanic systems, especially regarding their shallow structures. Full article

Long and continuous lacustrine sediments in Southwest China provide exceptional records of the Indian summer monsoon (ISM) evolution. Rock magnetic and environmental magnetic methods have significant roles in these lacustrine studies. However, lacustrine sedimentary environments are complex and magnetic mineral signatures can be altered by post-depositional processes. This study applies isothermal remanent magnetization (IRM) component unmixing methods to lacustrine sediments from the Heqing core, to identify and quantify magnetic mineral components. We analyzed 104 samples based on lithological variations and magnetic susceptibility (χ) to examine the composition of magnetic minerals and their relative contributions. Three distinct magnetic components were identified in IRM component unmixing results: a low-coercivity detrital component, a medium-coercivity authigenic component, and a hard magnetic component. Based on rock magnetic results, the medium-coercivity component was attributed to greigite. These components exhibit stratigraphic trends that reflect changes in paleoenvironmental conditions. The medium-coercivity component shows an upwards decrease, indicating a significant change in ISM science at about 1.8 Ma. The study highlights the importance of considering post-depositional processes when interpreting magnetic mineral signatures in lacustrine sediments. The CLG model, combined with conventional rock magnetic analyses, provides a rapid approach for characterizing magnetic assemblages in weakly magnetic sediments. Full article

We investigated the effects of chemical and physical changes on the interplay between the Néel and Brown superspin relaxation mechanisms in ferrofluids containing 18 nm-diameter Co_0.2Fe_2.8O₄ magnetic nanoparticles. We attempted to tune the ferrofluid’s magnetization dynamics via three methods: (i) changing the carrier fluid from Isopar M to kerosene (ii) doubling the Co-doping level from x = 0.2 to x = 0.4, and (iii) diluting the Co_0.2Fe_2.8O₄/Isopar M nanomagnetic fluid from δ = 1 mg/mL to δ = 0.1 mg/mL. We used temperature-resolved ac-susceptibility measurements at different frequencies, χ″ vs. T|_f, to gain insight into the thermally driven superspin dynamics of the nanoparticles within the ferrofluid. Our data demonstrates that both increasing x and using a different carrier fluid quantitatively alter the temperature dependence of the Néel and Brown relaxation frequency (f_N vs. T and f_B vs. T) by changing the nanoparticles’ magnetic moments and the fluid’s viscosity. Yet, the two mechanisms remain decoupled, as indicated by the presence of two magnetic events (peaks in the χ″ vs. T|_f datasets) one corresponding to the Néel and the other to Brown relaxation. On the other hand, diluting the ferrofluid leads to a qualitative change in the collective superspin dynamics behavior. Indeed, there is just one χ″-peak in the data from the δ = 0.1 mg/mL nanofluid, and its f vs. T dependence is well-described by a model that includes coupled contributions from both the Néel and Brown relaxation: $\mathrm{f}\left(\mathrm{T}\right)=\mathrm{p}·{\displaystyle \frac{T}{{\gamma }_0}}·\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathrm{E}}^{\prime }}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0^{\prime }\right)}}\right]+$ (1 − p) ${\mathrm{f}}_0\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathrm{E}}_{\mathrm{B}}}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0\right)}}\right]$. This is a remarkable behavior that demonstrates the ability to control a ferrofluids magnetization dynamics through simple chemical and physical changes. Full article

Small interfering RNAs (siRNAs) are particularly attractive among the frontier drugs due to their high specificity of action, activity on disease-inducing genes, and small molecular weight, thus being one of the most studied agents for gene therapy. However, siRNAs are prone to fast enzymatic degradation in the bloodstream, as well as other limitations that challenge their clinical translation. Nanoparticle (NP) delivery of siRNA has been proposed as a potential solution, overcoming their intrinsic limitations. In this regard, the siRNA delivery by magnetic nanoparticles is of particular interest because, being susceptible to external magnetic fields, it may be guided remotely, maximizing transfection efficiency and minimizing side effects. In addition, magnetic NPs would also allow a theranostic combination of drug delivery, magnetic resonance imaging, and hyperthermia. In this work we have studied the uptake of a model therapeutic siRNA by iron-doped hydroxyapatite nanoparticles (FeHA NPs), which are known to have excellent biocompatibility and magnetic susceptibility. We discovered that FeHA NPs stabilized by citrate (Cit-FeHA NPs) uptake siRNA by adsorption quickly and with high efficiency (ca. 90%) without altering nanoparticles physicochemical properties or colloidal stability. SiRNA-loaded Cit-FeHA NPs are able to slowly release their payload, with a sustained release of 45 days without siRNA degradation. Our work is therefore the preliminary validation of the suitability of FeHA NPs for magnetically guided delivery of therapeutic siRNAs. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate and compare the performance of three advanced multimodal LLMs (ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) in classifying brain MRI sequences. Methods: A total of 130 brain MRI images from adult patients without pathological findings were used, representing 13 standard MRI series. Models were tested using zero-shot prompts for identifying modality, anatomical region, imaging plane, contrast-enhancement status, and MRI sequence. Accuracy was calculated, and differences among models were analyzed using Cochran’s Q test and McNemar test with Bonferroni correction. Results: ChatGPT-4o and Gemini 2.5 Pro achieved 100% accuracy in identifying the imaging plane and 98.46% in identifying contrast-enhancement status. MRI sequence classification accuracy was 97.7% for ChatGPT-4o, 93.1% for Gemini 2.5 Pro, and 73.1% for Claude 4 Opus (p < 0.001). The most frequent misclassifications involved fluid-attenuated inversion recovery (FLAIR) sequences, often misclassified as T1-weighted or diffusion-weighted sequences. Claude 4 Opus showed lower accuracy in susceptibility-weighted imaging (SWI) and apparent diffusion coefficient (ADC) sequences. Gemini 2.5 Pro exhibited occasional hallucinations, including irrelevant clinical details such as “hypoglycemia” and “Susac syndrome.” Conclusions: Multimodal LLMs demonstrate high accuracy in basic MRI recognition tasks but vary significantly in specific sequence classification tasks. Hallucinations emphasize caution in clinical use, underlining the need for validation, transparency, and expert oversight. Full article

The present study provides a detailed compilation and analysis of the bulk density and magnetic susceptibility of the rocks from the northwest Himalayas, Pakistan. The area is tectonically extremely complex and comprises sedimentary, metamorphic, and igneous rocks. These rocks range in age from Early Proterozoic to Recent. During the fieldwork, 476 rock samples were collected for density measurements and 410 for magnetic susceptibility measurements from the major rock units exposed in the study area. The measured physical parameters reveal a significant difference in the density and susceptibility of the rocks present in the investigated area. The sedimentary rock units belonging to the Indian Plate show the lowest mean values for bulk density, followed by metasedimentary rocks, Early Proterozoic rocks, igneous and metaigneous rock units of the Indian Plate, Indus Suture Melange Zone, and Kohistan Island Arc rocks, respectively. The magnetic susceptibility of sedimentary rock units of the Indian Plate has the lowest mean values, followed by metasedimentary rocks of the Indian Plate, igneous and metaigneous rock units of the Indian Plate, Early Proterozoic rocks of the Indian Plate, Kohistan Island Arc rocks, and Indus Suture Melange Zone. In brief, the sedimentary rocks of the Indian Plate have the lowest bulk density and magnetic susceptibility values, whereas the Kohistan Island Arc rocks have the highest values. Overall, the bulk density and magnetic susceptibility of rock units in the study area follow those predicted for different types of rocks. These measurements can be used to develop possible potential field models of the northwest Himalayas to better understand the tectonics of the ongoing continental-to-continental collision, as well as for many other geological analyses. Full article

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Reliable, data-driven identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In such geological settings, magnetic susceptibility, radioactive heat production, and seismic wave characteristics play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this methodology to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the statistical and AI-based models evaluated, Density-Based Spatial Clustering of Applications with Noise and Autoencoder-Based Deep Clustering identified the most promising and spatially confined subregions with high geothermal production potential. The potential geothermal sites identified by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region. Full article

The Pécel loess–paleosol profile is a 25.72-metre-high well-preserved sequence in the northern part of Hungary. It was sampled every 4 cm for the purpose of sedimentological analysis and every 12 cm for the purpose of mollusc investigation, which are relatively high resolutions in loess investigation. Twenty samples were radiocarbon-dated from the L1 layer (top 8 m of the sequence). Subsequently, an age–depth model was constructed, from which an accumulation rate was calculated. Based on these radiocarbon and previous magnetic susceptibility data, the Pécel’s L1 layer is correlated with the Chinese Loess Plateau’s L1 layer and the MIS 2–4 stages. The malacological examinations show that the temperature was basically warm during the development, and there was open vegetation except on the S2, S1 and L1S1 paleosol layers, where significant forest expansion was shown. With the magnetic susceptibility and the malacological data, it is possible to track the changes in the conditions through the Chinese Loess Plateau’s timeline. Full article

We use Co doping to alter the magnetic relaxation dynamics in superparamagnetic nanofluids made of 18 nm average diameter Fe₃O₄ nanoparticles immersed in Isopar M. Ac-susceptibility data recorded at different frequencies and temperatures, χ″vs. T|_f, reveals a major (~100 K) increase in the superspin blocking temperature of the Co_0.2Fe_2.8O₄-based fluid (CFO) compared to its Fe₃O₄ counterpart (FO). We ascribe this behavior to the strengthening of the interparticle magnetic dipole interactions upon Co doping, as demonstrated by the relative χ″-peak temperature variation per frequency decade $\mathsf{\Phi }={\displaystyle \frac{∆\mathrm{T}}{\mathrm{T}·∆\mathrm{l}\mathrm{o}\mathrm{g}\left(\mathrm{f}\right)}}$, which decreases from Φ~0.15 in FO to Φ~0.025 in CFO. In addition, χ″vs. T|_f datasets from the CFO fluid reveal two magnetic events at temperatures T_p1 = 240 K and T_p2 = 275 K, both above the fluid’s freezing point (T_F = 197 K). We demonstrate that the physical rotation of the nanoparticles within the fluid, the Brown mechanism, is entirely responsible for the collective superspin relaxation observed at T_p1, whereas the Néel mechanism, the superspin flip across an energy barrier within the particle, is dominant at T_p2. We confirm this finding through fits of models that describe the temperature dependence of the relaxation time via the two mechanisms: ${\mathsf{\tau }}_{\mathrm{B}}(\mathrm{T})={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}\mathrm{T}}}\exp \left[{\displaystyle \frac{{\mathrm{E}}^{\prime }}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{{\mathrm{T}}_0}^{\prime }\right)}}\right]$ and ${\mathsf{\tau }}_{\mathrm{N}}\left(\mathrm{T}\right)={\mathsf{\tau }}_0\exp \left[{\displaystyle \frac{{\mathrm{E}}_{\mathrm{B}}}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0\right)}}\right]$. The best fits yield ${\mathsf{\gamma }}_0={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}}}$= 1.5 × 10⁻⁸ s·K, E′/k_B = 7 03 K, and T₀′ = 201 K for the Brown relaxation, and E_B/k_B = 2818 K and T₀ = 143 K for the Néel relaxation. Full article

In this research, an analysis of the thermomagnetic properties of a nanoscale spin-2 and spin-3/2 system is conducted. This system is modeled with as a quasi-spherical Ising-type nanoparticle with a diameter of 2 nm, in which atoms with spin-2 and spin-3/2 configured in body-centered cubic (BCC) lattices interact within their relevant nanostructures. To determine the thermomagnetic behaviors of the nanoparticle, numerical simulations using Monte Carlo techniques and thermal bath class algorithms are performed. The results exhibit the effects of exchange couplings ($J_1,J_2^{\prime }$), magnetocrystalline anisotropies ($D_{3/2}^{\prime },D_2^{\prime }$), and external magnetic fields ($h^{\prime }$) on the finite-temperature phase diagrams of magnetization ($M_T$), magnetic susceptibility (${\chi }_T$), and thermal energy ($k_B{T}^{\prime }$). The influences of the exchange, anisotropic, and external field parameters are clearly reflected in the compensation, hysteretic, and pseudocritical phenomena presented by the quasi-spherical nanoparticle. When the parameter reflecting ferromagnetic second-neighbor exchanges in the nanosphere ($J_2^{\prime }$) increases, for a given value of the external magnetic field, the compensation ($T_{comp}$) and pseudocritical ($T_{pc}$) temperatures increase. Similarly, in the ranges $0<J_2^{\prime }⩽4.5$ and $-15⩽h^{\prime }⩽15$ at a specific temperature, an increase in $J_2^{\prime }$ results in the appearance of exchange anisotropies (exchange bias) and and increased hysteresis loop areas in the nanomodel. 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