Search Results (127)

Brain tumors, encompassing subtypes with distinct progression and risk profiles, are a serious public health concern. Magnetic resonance imaging (MRI) is the primary imaging modality for non-invasive assessment, providing the contrast and detail necessary for diagnosis, subtype classification, and individualized care planning. In this paper, we evaluate the capability of modern deep learning models to classify gliomas as high-grade (HGG) or low-grade (LGG) using reduced training data from MRI scans. Utilizing the BraTS 2019 best-slice dataset (2185 images in two classes, HGG and LGG) divided in two folders, training and testing, with different images obtained from different patients, we created subsets including 10%, 25%, 50%, 75%, and 100% of the dataset. Six deep learning architectures, DeiT3_base_patch16_224, Inception_v4, Xception41, ConvNextV2_tiny, swin_tiny_patch4_window7_224, and EfficientNet_B0, were evaluated utilizing three-fold cross-validation (k = 3) and increasingly large training datasets. Explainability was assessed using Grad-CAM. With 25% of the training data, DeiT3_base_patch16_224 achieved an accuracy of 99.401% and an F1-Score of 99.403%. Under the same conditions, Inception_v4 achieved an accuracy of 99.212% and a F1-Score of 99.222%. Considering how the models performed across both data subsets and their compute demands, Inception_v4 struck the best balance for MRI-based glioma classification. Both convolutional networks and vision transformers achieved superior discrimination between HGGs and LGGs, even under data-limited conditions. Architectural disparities became increasingly apparent as training data diminished, highlighting unique inductive biases and efficiency characteristics. Even with a relatively limited amount of training data, current deep learning (DL) methods can achieve reliable performance in classifying gliomas from MRI scans. Among the architectures evaluated, Inception_v4 offered the most consistent balance between accuracy, F1-Score, and computational cost, making it a strong candidate for integration into MRI-based clinical workflows. Full article

We present an integrated methodology for three-dimensional inversion of large-scale airborne electromagnetic (AEM) and magnetic survey data that simultaneously recovers electrical conductivity, chargeability, and both induced and remanent magnetizations. A central feature of the AEM component is the explicit incorporation of induced polarization (IP) effects. Neglecting IP responses can lead to biased conductivity models, particularly in mineralized systems where disseminated sulfides contribute strongly to chargeability. Using the Generalized Effective-Medium Theory of Induced Polarization (GEMTIP), the inversion produces physically consistent 3D distributions of conductivity and chargeability. To enhance magnetic interpretation, we also implement a vector magnetic inversion that resolves both induced and remanent magnetization from Total Magnetic Intensity (TMI) data, enabling geologically realistic magnetization models in terranes with significant remanence. This integrated workflow was applied to airborne AEM and TMI datasets collected over the Asankrangwa Gold Belt in central Ghana. The inversion results delineate a key exploration target defined by coincident magnetic low and elevated chargeability, interpreted as sulfide-rich gold mineralization and subsequently confirmed by drilling. These results demonstrate that jointly accounting for IP and remanent magnetization in 3D inversion substantially improves subsurface characterization and provides a powerful tool for mineral exploration in structurally and lithologically complex environments. Full article

This review provides a comprehensive analysis of the petrophysical controls influencing greensand reservoirs, with an emphasis on the role of glauconite and associated clay minerals in determining porosity, permeability, and water saturation. Greensands contain iron-rich clay minerals that exert paramagnetic and conductive effects, challenging conventional well-log interpretations and often leading to biased estimates of reservoir parameters. Several challenges for petrophysical property measurements are faced in the laboratory due to clay-induced pore-throat obstruction and microporosity, which underscores the importance of tailored interpretation workflows and data integration. In this paper we highlight the necessity of integrated approaches such as combining core analysis, spectral gamma-ray, and nuclear magnetic resonance (NMR) logging with conventional well logs to calibrate petrophysical models using shale–sand water saturation models, such as Waxman–Smits and Simandoux, to better characterise economical pay zones. Finally, future research directions are indicated, which include refining the calibration of saturation and permeability models, advancing rock-typing methodologies, and understanding mineralogical influences on reservoir quality to optimise hydrocarbon recovery from greensand reservoirs. Full article

Iron (Fe) chelators are used to treat iron-overloaded disorders, metal detoxification, radionuclides, and molecular imaging; however, they can cause side effects. In this study, we identified and characterized Coprogen B (CPGB), a hexadentate trihydroxamate siderophore secreted by the opportunistic dimorphic fungus Talaromyces marneffei and compared its properties with deferoxamine (DFO). Siderophore production was enriched from a ΔsreA strain and purified via Amberlite XAD2 and Sephadex LH20 chromatography, followed by reverse-phase HPLC. Active fractions were confirmed by Ultraviolet–Visible (UV–Vis) spectral fingerprints (≈230 nm) for hydroxamate, with a band at 430–450 nm upon Fe(III) complexation, as well as by chrome azurol A assay, Nuclear Magnetic Resonane (NMR) spectroscopy, High-Performance Liquid Chromatography–Mass Spectrometry (HPLC-MS), and Matrix-Assisted Laser Desorption/Ionization–Time-of-Flight Mass Spectrometry (MALDI-TOF-MS). CPGB exhibited strong molar absorptivity and rapid, concentration-dependent chelation of Fe(III), yielding a sustained binding profile that matched or exceeded that of DFO over time. In determining n-octanol/water partitioning for CPGB and DFO (230 nm) and their Fe(III) complexes, the partitioning (P) assay revealed that CPGB was moderately hydrophilic (P = 0.505 ± 0.063; cLogP = −0.299 ± 0.053), while DFO was strongly hydrophilic (P = 0.098 ± 0.005; cLogP = −1.010 ± 0.022). Fe(III) complexation reduced lipophilicity: CPGB–Fe partitioned ~30–35% into octanol, while DFO–Fe complex partitioned ~7–8%, remaining largely aqueous. Overall, this outcome potentially suggested improved clearance in vivo. These data nominate CPGB as a promising alternative to existing iron chelators. The siderophore exhibited greater lipophilicity, emphasizing better passive membrane permeability than DFO, while siderophore–Fe(III) binding indicated increased biases toward the aqueous phase. Future in vivo studies are warranted to confirm its pharmacokinetics, safety, and therapeutic efficacy. Full article

Epitaxial thick films of yttrium iron garnet (YIG) are ideal for use in microwave devices due to their low losses at high frequencies. This report is on the growth of strain-engineered YIG films by liquid-phase epitaxy (LPE) on yttrium aluminum garnet (YAG) substrates with −3% lattice mismatch with YIG. Since the use of a lattice-matched substrate is preferred for LPE growths, a seed layer of YIG, 370–400 nm in thickness, was deposited by pulsed laser deposition (PLD) on (100), (110), and (111) YAG substrates. The seed layers were stoichiometric with magnetic parameters in agreement with the parameters for bulk single-crystal YIG and with strain-induced perpendicular magnetic anisotropy field H_a = 0.19–0.43 kOe. YIG films, 4 to 8.4 μm in thickness, were grown by LPE at 870 °C on YAG substrates with the seed layers using the PbO+B₂O₃ flux and annealed in air at 1000 °C. The films were Y-rich and Fe-deficient and confirmed to be epitaxial single crystals by X-ray diffraction. The saturation magnetization 4πM_s at room temperature was rather high and ranged from 1.9 kG to 2.3 kG. Ferromagnetic resonance at 5–15 GHz showed the absence of significant magneto-crystalline anisotropy in the LPE films with the line-width ΔH in the range 85–160 Oe, and H_a = 0.27–0.80 kOe which is much higher than for the seed layers. The high magnetization and H_a-values for the LPE films could be partially attributed to the off-stoichiometry. Although the strain due to the film–substrate lattice mismatch contributes to H_a, the mismatch in the thermal expansion coefficients for YIG and YAG is also a likely cause of H_a due to the high growth and annealing temperatures. The LPE-grown YIG films with high strain-induced anisotropy fields have the potential for use in self-biased microwave devices. Full article

Background/Objectives: Non-specific low back pain (LBP), a highly prevalent musculoskeletal condition, may be associated with overuse of radiological imaging, despite clinical guidelines restricting its use to cases with suspected serious pathology. This study investigated demographic, clinical, and physiotherapy-related factors influencing radiological imaging use in patients with non-specific LBP. Methods: A retrospective cross-sectional study included 179 non-specific LBP patients from an outpatient physiotherapy clinic in Saudi Arabia. Patient data were anonymized and retrieved from electronic health records, including demographic, clinical, physiotherapy and imaging information. Independent variables included patient demographics, non-specific LBP characteristics, physiotherapy engagement, and pain-related outcomes. Descriptive, inferential, and multiple linear regression analyses were conducted to identify predictors of radiological imaging. Results: Among the total study sample (n = 179), 159 (88.8%) patients underwent radiological imaging, primarily X-ray (32.4%) and Magnetic Resonance Imaging (8.4%); 48.0% received multiple imaging modalities. Significant predictors of imaging use included gender (p < 0.001), higher body mass index (BMI) (p = 0.012), greater physiotherapist experience (p = 0.019), and presence of comorbidities (p = 0.023). Non-specific LBP medication use was negatively associated with imaging (p = 0.032). Physiotherapy engagement and pain-related outcomes showed no significant impact on imaging use. Conclusions: Gender, BMI, physiotherapist experience, and comorbidities could influence radiological imaging use in non-specific LBP patients. These findings highlight potential biases in imaging referral patterns and reinforce the need for adherence to evidence-based guidelines to prevent unnecessary imaging, reduce healthcare costs, and enhance patient care. Full article

The Solar Magnetic Field Telescope (SMFT) at Huairou Solar Observing Station (HSOS) has conducted continuous observations of solar vector magnetic fields for nearly four decades, and while the primary optical system remains unchanged, critical components—including filters, polarizers, and detectors—have undergone multiple upgrades and replacements. Maintaining data consistency is essential for reliable long-term studies of magnetic field evolution and solar activity, as well as current helicity. In this study, we systematically analyze background bias and noise levels in SMFT observations from 1988 to 2019. Our dataset comprises 12,281 vector magnetograms of 1484 active regions. To quantify background bias, we computed mean values of Stokes $Q/I$, $U/I$ and $V/I$ over each entire magnetogram. The background bias of Stokes $V/I$ is small for the whole dataset. The background biases of Stokes $Q/I$ and $U/I$ fluctuate around zero during 1988–2000. From 2001 to 2011, however, the fluctuations in the background bias of both $Q/I$ and $U/I$ become significantly larger, exhibiting mixed positive and negative values. Between 2012 and 2019, the background biases shift to predominantly positive values for both Stokes $Q/I$ and $U/I$ parameters. To address this issue, we propose a potential method for removing the background bias and further discuss its impact on the estimation of current helicity. For each magnetogram, we quantify measurement noise by calculating the standard deviation ($\sigma$) of the longitudinal ($B_{\mathrm{l}}$) and transverse ($B_{\mathrm{t}}$) magnetic field components within a quiet-Sun region. The noise levels for $B_l$ and $B_{\mathrm{t}}$ components were approximately 15 Gauss (G) and 87 G, respectively, during 1988–2011. Since 2012, these values decreased significantly to ∼6 G for $B_{\mathrm{l}}$ and ∼55 G for $B_{\mathrm{t}}$, likely due to the installation of a new filter. Full article

Low-temperature oxidation of titanomagnetite in oceanic basalts distorts the primary thermoremanent magnetization (TRM) signal essential for reconstructing Earth’s magnetic field history, though the specific impact of magma intrusion-induced oxidation on paleointensity preservation remains poorly constrained. This investigation simulates such oxidation processes using a novel experimental design involving isothermal annealing (260 $°$C; 50 µT field; durations 12.5–1300 h) of Red Sea rift basalts (P72/4), employing the Thellier-Coe method to quantify how chemical remanent magnetization (CRM) overprinting affects TRM fidelity under controlled field orientations aligned either parallel or perpendicular to the initial TRM. Results demonstrate two-sloped Arai-Nagata diagrams with reliable TRM preservation below 360 $°$C but significant alteration artifacts above this threshold. Crucially, field orientation during oxidation critically influences accuracy: parallel configurations maintain fidelity (±3% deviation at $Z=0.48$), while perpendicular fields introduce systematic biases (38% overestimation at $Z=0.15$; 20% underestimation at $Z>0.48$), which is attributable to magnetostatic interactions in core-shell grain structures. These findings establish that paleointensity reliability in basalt prone to low-temperature oxidation depends fundamentally on the alignment between oxidation-era magnetic fields and primary TRM direction, necessitating stringent sample selection and directional constraints in marine paleomagnetic research to mitigate CRM-TRM interference. Full article

Fiber optic technologies have strong potential to augment and improve existing areas of sensor performance across many applications. Magnetic sensing, in particular, has attracted significant interest in structural health monitoring and ferromagnetic object detection. However, current technologies such as fluxgate magnetometers and inspection gauges rely on measuring magnetic fields as single-point sensors. By using fiber optic distributed strain sensors in tandem with magnetically biased magnetostrictive material, static and dynamic magnetic fields can be detected across long lengths of sensing fiber. This paper investigates the relationship between Fiber Bragg Grating (FBG)-based strain sensors and the magnetostrictive alloy Metglas^® 2605SC for the distributed detection of static fields for use in a compact cable design. Sentek Instrument’s picoDAS system is used to interrogate the FBG based sensors coupled with Metglas^® that is biased with an alternating sinusoidal magnetic field. The sensing system is then exposed to varied external static magnetic field strengths, and the resultant strain responses are analyzed. A minimum magnetic field strength on the order of 300 nT was able to be resolved and a variety of sensing configurations and conditions were also tested. The sensing system is compact and can be easily cabled as both FBGs and Metglas^® are commercialized and readily acquired. In combination with the robust and distributed nature of fiber sensors, this demonstrates strong promise for new means of magnetic characterization. Full article

The transport properties of biased type II superconductors are strongly influenced by external magnetic fields, which play a crucial role in optimizing the stability and performance of low-noise superconducting electronic devices. A major challenge is the stochastic behavior of Abrikosov vortices, which emerge in the mixed state and lead to energy dissipation through their nucleation, motion, and annihilation. Uncontrolled vortex dynamics can introduce electronic noise in low-power systems and trigger thermal breakdown in high-power applications. This study examines the effect of a perpendicular external magnetic field on vortex pinning in biased YBa₂Cu₃O_7-δ devices containing laser-written, rectangular-shaped, partially deoxygenated regions (δ ≈ 0.2). The results show that increasing the magnetic field amplitude induces an asymmetry in the concentration of vortices and antivortices, shifting the annihilation line toward a region of lower flux density and altering the flux pinning characteristics. Oxygen-deficient segments aligned parallel to the current flow act as barriers to vortex motion, enhancing the net pinning force by preventing vortex–antivortex pairs from reaching their annihilation zone. The current–voltage characteristics reveal periodic voltage steps corresponding to the onset and suppression of thermally activated flux flow and flux creep. These features indicate magnetic field–tunable transport behavior within a narrow range of temperatures from 0.94·T_c to 0.98·T_c, where T_c is the critical temperature of the superconductor. These findings offer new insights into the design of vortex-motion-controlled superconducting electronics that utilize engineered pinning structures. Full article

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD. Full article

In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor. Full article

Objective: Alzheimer’s disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to late sixties. It progresses from mild to severe stages, so an accurate diagnostic tool is necessary for effective intervention and treatment planning. Methods: This work proposes a novel AD classification architecture that integrates depthwise separable convolutional layers with traditional convolutional layers to efficiently extract features from structural magnetic resonance imaging (sMRI) scans. This model benefits from excellent feature extraction and lightweight operation, which reduces the number of parameters without compromising accuracy. The model learns from scratch with optimized weight initialization, resulting in faster convergence and improved generalization. However, medical imaging datasets contain class imbalance as a major challenge, which often results in biased models with poor generalization to the underrepresented disease stages. A hybrid sampling approach combining SMOTE (synthetic minority oversampling technique) with the ENN (edited nearest neighbors) effectively handles the complications of class imbalance issue inherent in the datasets. An explainable activation space occlusion sensitivity map (ASOP) pixel attribution method is employed to highlight the critical regions of input images that influence the classification decisions across different stages of AD. Results and Conclusions: The proposed model outperformed several state-of-the-art transfer learning architectures, including VGG19, DenseNet201, EfficientNetV2S, MobileNet, ResNet152, InceptionV3, and Xception. It achieves noteworthy results in disease stage classification, with an accuracy of 98.87%, an F1 score of 98.86%, a precision of 98.80%, and recall of 98.69%. These results demonstrate the effectiveness of the proposed model for classifying stages of AD progression. Full article

Owing to the distinct advantages of stator–permanent magnet (PM) motors over other PM machines, their prominence in high-power-density applications is surging dramatically, capturing growing interest across diverse applications. This article proposes an innovative design procedure for two primary stator–PM motor types, flux switching and biased flux, yielding 30 novel motor designs. The procedure involves splitting teeth, incorporating a flux reversal effect, and embedding flux barriers into the conventional structure. The analytical reasons behind the novel motors’ architecture are mathematically expressed and verified using finite element analysis (FEA). Through an effective optimisation based on a multi-objective genetic algorithm, various feasible stator/rotor pole combinations are explored, with over 36,000 samples evaluated using FEA coupled with the algorithm. The electromagnetic characteristics of promising motors are analysed, revealing that adding the flux reversal effect and flux barriers, which reduce PM volume while decreasing leakage flux and enhancing air gap flux, improves torque production by up to 68%. Beyond torque enhancement, other electromagnetic parameters, including torque ripple, core loss, and the power factor, are also improved. The proposed motors enhance the PM torque density significantly by about 115% compared to conventional motors and reduce the motor costs. A generalised decision-making process and thermal analysis are applied to the top-performing motors. Additionally, the prototyping measures and considerations are thoroughly discussed. Finally, a comprehensive conclusion is reached. Full article

Artificial intelligence (AI) is transforming cardiovascular medicine by enabling the analysis of high-dimensional biomedical data with unprecedented precision. Initially employed to automate human tasks such as electrocardiogram (ECG) interpretation and imaging segmentation, AI’s true potential lies in uncovering hidden disease data patterns, predicting long-term cardiovascular risk, and personalizing treatments. Unlike human cognition, which excels in certain tasks but is limited by memory and processing constraints, AI integrates multimodal data sources—including ECG, echocardiography, cardiac magnetic resonance (CMR) imaging, genomics, and wearable sensor data—to generate novel clinical insights. AI models have demonstrated remarkable success in early dis-ease detection, such as predicting heart failure from standard ECGs before symptom on-set, distinguishing genetic cardiomyopathies, and forecasting arrhythmic events. However, several challenges persist, including AI’s lack of contextual understanding in most of these tasks, its “black-box” nature, and biases in training datasets that may contribute to disparities in healthcare delivery. Ethical considerations and regulatory frameworks are evolving, with governing bodies establishing guidelines for AI-driven medical applications. To fully harness the potential of AI, interdisciplinary collaboration among clinicians, data scientists, and engineers is essential, alongside open science initiatives to promote data accessibility and reproducibility. Future AI models must go beyond task automation, focusing instead on augmenting human expertise to enable proactive, precision-driven cardiovascular care. By embracing AI’s computational strengths while addressing its limitations, cardiology is poised to enter an era of transformative innovation beyond traditional diagnostic and therapeutic paradigms. Full article