Search Results (9,719)

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is prevalent in adolescents with obesity and is linked to insulin resistance and cardiovascular disease (CVD). Pancreas steatosis might be associated with MASLD and early CVD. Imaging-based analyses of these associations have not been studied extensively in children. Objectives: To assess the reproducibility of liver and pancreatic steatosis and volume measurement on MRI in adolescents with obesity and MASLD and their association with homeostatic model assessment of insulin resistance (HOMA-IR) and subclinical vascular changes on ultrasound. Methods: This is an observational study on adolescents with MASLD and obesity. Hepatic and pancreatic steatosis, volume, and abdominal fat were assessed using magnetic resonance spectroscopy and proton density fat fraction. Reproducibility of these measurements was performed. Vascular markers included non-invasive vascular elastography (NIVE), carotid artery intima-media thickness (IMT), and pericardial fat thickness. Fasting blood tests measured the HOMA-IR. Bivariate correlation and simple linear regression were performed using SPSS. Results: We obtained 23 participants aged 12 to 17 years (78.3% male). Measurements were reproducible [ICC 0.807–0.998]. Liver steatosis was positively correlated with HOMA-IR (p = 0.015). Pancreas steatosis was positively correlated with HOMA-IR (p = 0.02), IMT/diameter (p = 0.002), and pericardial fat (p = 0.03). Liver steatosis was not significantly correlated with pancreas steatosis nor vascular markers. There were negative associations between NIVE metrics and visceral abdominal fat (p = 0.009) and intraperitoneal fat (p = 0.047). Conclusions: Liver and pancreas steatosis measurements on MRI are reproducible. In this exploratory study, adolescents with obesity and MASLD, pancreas steatosis, and pancreas volume show association with subclinical CVD markers. Visceral and intraperitoneal abdominal fat show association with increased vascular stiffness, suggesting a potential role of imaging-based cardiovascular risk assessment in this population if validated. These preliminary findings require validation in larger, diverse prospective cohorts. Full article

Stable adhesion on non-magnetic, steep, and irregular underwater surfaces (e.g., concrete dams with cracks or biofilms) remains a challenge for inspection robots. This study develops a novel adsorption mechanism based on the synergistic operation of a Venturi-ejector and a composite suction cup. The mechanism utilizes the Venturi effect to generate stable negative pressure via hydrodynamic entrainment and innovatively adopts a composite suction cup—comprising a rigid base and a dual-layer EPDM sponge (closed-cell + open-cell)—to achieve adaptive sealing, thereby reliably applying the efficient negative-pressure generation capability to rough underwater surfaces. Theoretical modeling established the quantitative relationship between adsorption force (F) and key parameters (nozzle/throat diameters, suction cup radius). CFD simulations revealed optimal adsorption at a nozzle diameter of 4.4 mm and throat diameter of 5.8 mm, achieving a peak simulated F of 520 N. Experiments demonstrated a maximum F of 417.9 N at 88.9 W power. The composite seal significantly reduced leakage on high-roughness surfaces (Ra ≥ 6 mm) compared to single-layer designs. Integrated into an inspection robot, the system provided stable adhesion (>600 N per single adsorption device) on vertical walls and reliable operation under real-world conditions at Balnetan Dam, enabling mechanical-arm-assisted maintenance. Full article

In response to the increasing demand for environmentally friendly and cost-effective adsorbents in wastewater treatment, this study reports the green synthesis, characterization, and application of a magnetic epichlorohydrin Rosa canina (m-ECH-RC) nanocomposite for removing Safranin O (SO), a commonly used cationic dye in textile effluents. The synthesized material was characterized using Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and zeta potential analyses to reveal its surface morphology, pore structure, functional groups, crystallinity, and colloidal stability. Adsorption performance was systematically tested under various conditions, including pH, adsorbent dose, contact time, ionic strength, and initial dye concentration. Kinetic analyses revealed that the adsorption process of Safranin O dye mainly obeys pseudo-second-order kinetics, but intraparticle and film diffusion also contribute to the process. As a result of the Isotherm analysis, it was found that the adsorption process conformed to the Langmuir model. Testing on real textile wastewater samples demonstrated a removal efficiency of 75.09% under optimized conditions. Reusability experiments further revealed that the material maintained high adsorption–desorption performance for up to five cycles, emphasizing its potential for practical use. These findings suggest that m-ECH-RC is a viable and sustainable adsorbent for treating dye-laden industrial effluents. Full article

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

Magnetohydrodynamic (MHD) flow and heat transfer in porous media are central to many engineering applications, including heat exchangers, MHD generators, and polymer processing. This study examines the boundary layer flow and thermal behavior of an electrically conducting viscous fluid over a porous stretching tube. The model accounts for nonlinear thermal radiation, internal heat generation/absorption, and Darcy–Forchheimer drag to capture porous medium resistance. Similarity transformations reduce the governing equations to a system of coupled nonlinear ordinary differential equations, which are solved numerically using the BVP4C technique with Response Surface Methodology (RSM) and sensitivity analysis. The effects of dimensionless parameters magnetic field strength (M), Reynolds number (Re), Darcy–Forchheimer parameter (Df), Brinkman number (Br), Prandtl number (Pr), nonlinear radiation parameter (Rd), wall-to-ambient temperature ratio (rw), and heat source/sink parameter (Q) are investigated. Results show that increasing M, Df, and Q suppresses velocity and enhances temperature due to Lorentz and porous drag effects. Higher Re raises pressure but reduces near-wall velocity, while rw, Rd, and internal heating intensify thermal layers. The entropy generation analysis highlights the competing roles of viscous, magnetic, and thermal irreversibility, while the Bejan number trends distinctly indicate which mechanism dominates under different parameter conditions. The RSM findings highlight that rw and Rd consistently reduce the Nusselt number (Nu), lowering thermal efficiency. These results provide practical guidance for optimizing energy efficiency and thermal management in MHD and porous media-based systems.: Full article

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution. Full article

This paper proposes a novel direct-drive rotary actuator based on a modular five-phase outer-rotor flux-switching permanent magnet (FSPM) machine to overcome the limitations of conventional actuators with gear reducers, such as mechanical complexity and low reliability. The research focused on a synergistic design of a lightweight, high-torque-density motor and a precise control strategy. The methodology involved a structured topology evolution to create a modular stator architecture, followed by finite element analysis-based electromagnetic optimization. To achieve precision control, a multi-vector model predictive current control (MPCC) scheme was developed. This optimization process contributed to a significant performance improvement, increasing the average torque to 13.33 Nm, reducing torque ripple from 9.81% to 2.36% and obtaining a maximum position error under 1 mil. The key result was experimentally validated using an 8 kg inertial load, confirming the actuator’s feasibility for industrial deployment. Full article

Accurate inversion of the T₂ spectrum of shale oil reservoir fluids is crucial for reservoir evaluation. However, traditional nuclear magnetic resonance inversion methods face challenges in extracting features from multi-exponential decay signals. This study proposed an inversion method that combines autoencoder (AE) and Fourier transform, aiming to enhance the accuracy and stability of T₂ spectrum estimation for shale oil reservoirs. The autoencoder is employed to automatically extract deep features from the echo train, while the Fourier transform is used to enhance frequency domain features of multi-exponential decay information. Furthermore, this paper designs a customized weighted loss function based on a self-attention mechanism to focus the model’s learning capability on peak regions, thereby mitigating the negative impact of zero-value regions on model training. Experimental results demonstrate significant improvements in inversion accuracy, noise resistance, and computational efficiency compared to traditional inversion methods. This research provides an efficient and reliable new approach for precise evaluation of the T₂ spectrum in shale oil reservoirs. Full article

Multiple-Q magnetic states encompass a broad class of noncollinear and noncoplanar spin textures generated by the superposition of spin density waves. In this study, we theoretically explore the emergence of vortex crystals formed by multiple-Q spin density waves on a two-dimensional triangular lattice with $D_{3\mathrm{h}}$ point group symmetry. Using simulated annealing applied to an effective spin model, we demonstrate that the synergy among the easy-plane single-ion anisotropy, the biquadratic interaction, and the out-of-plane Dzyaloshinsky–Moriya interaction defined in momentum space can give rise to a variety of double-Q and triple-Q vortex crystals. We further examine the role of easy-plane single-ion anisotropy in triple-Q vortex crystals and show that weakening the anisotropy drives topological transitions into skyrmion crystals with skyrmion numbers $\pm 1$ and $\pm 2$. The influence of an external magnetic field is also analyzed, revealing a field-induced phase transition from vortex crystals to single-Q conical spirals. These findings highlight the crucial role of out-of-plane Dzyaloshinskii–Moriya interactions in stabilizing unconventional vortex crystals, which cannot be realized in systems with purely polar or chiral symmetries. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Rare-earth transition metal-based double perovskite ceramics E₂TMO₆ (where E = rare-earth metals, T = transition metals, and M = metal) have received impressive attention lately for cryogenic applications as a result of their intrinsic physical features such as multiferroicity, dielectric features, and adjustable magnetic transition temperature. However, determination and enhancement of magnetic transition temperature of E₂TMO₆ ceramic are subject to experimental procedures and processes with a significant degree of difficulties and cumbersomeness. This work proposes an extreme learning machine (ELM)-based intelligent method of determining magnetic transition temperature of E₂TMO₆ ceramics with activation function sigmoid (SM) and sine (SE) at varying magnetic field. The outcomes of the SE-ELM and SM-ELM models were compared with genetically optimized support vector regression (GEN-SVR) predictive models using RMSE, CC, and MAE metrics. Using the testing samples of E₂TMO₆ ceramics, SE-ELM predictive model outperforms GEN-SVR with a superiority of 6.3% (using RMSE metric) and 15.7% (using MAE metric). The SE-ELM predictive model further outperforms the SM-ELM model, with an improvement of 5.3%, using CC computed with training ceramic samples. The simplicity of the employed descriptors, coupled with the outstanding performance of the developed predictive models, would potentially strengthen E₂TMO₆ ceramics exploration for low-temperature cryogenic applications and circumvent energy challenges in different sectors. Full article

As the production of electric vehicles grows, the rare earth elements Pr and Nd become increasingly significant, as they are key in magnetic materials production. In order to achieve the green and efficient recovery of Pr and Nd from the rare earth leachate, this paper selected kaolinite and halloysite as adsorbents to conduct rare earth solution adsorption experiments for exploring the effects of the initial leachate concentration, the solution pH, and the adsorption temperature on the adsorption process. The adsorption characteristics of Pr and Nd by clay minerals were analyzed by quantum chemical calculation. The results showed that the adsorption effects of clay minerals on Pr and Nd decreased with the rise of leachate concentration. When leachate pH increased, the adsorption efficiency of kaolinite and halloysite for Pr firstly increased and then decreased, and the optimal adsorption efficiency was 13.33% and 24.778% at pH 6, respectively. The adsorption effects of kaolinite and halloysite on Nd enhanced gradually with the increase in pH, which increased to 15.925% and 30.482% at pH 7, respectively. With temperature increased, the adsorption of Pr and Nd by kaolinite and halloysite was positively correlated. The isothermal adsorption model was fitted to the experimental data, and it was found that the adsorption of Pr and Nd by kaolinite and halloysite was consistent with the Langmuir model, with R² above 0.96, indicating that the adsorption process was a single molecular layer adsorption. The results provide theoretical support for the effective recycling of Pr and Nd, which is of great significance for the utilization of rare earth resources in permanent magnets. Full article

Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging capabilities. However, recent advances in spectral computed tomography (CT), particularly dual-energy CT (DECT), have introduced new diagnostic opportunities, offering improved soft tissue characterization. Objective: To evaluate the diagnostic performance of DECT in detecting and grading lumbar disk herniations using dedicated color-coded fat maps. Materials and Methods: A total of 205 intervertebral levels from 41 consecutive patients with lumbar symptoms were prospectively analyzed. All patients underwent both DECT and MRI within 3 days. Three radiologists with varying years of experience independently assessed DECT images using color-coded reconstructions. A five-point grading score was attributed to each lumbar level: 1 = normal disk, 2 = bulging/protrusion, 3 = focal herniation, 4 = extruded herniation, and 5 = migrated fragment. The statistical analysis included Pearson’s correlation for score consistency, Cohen’s Kappa for interobserver agreement, generalized estimating equations for a cluster-robust analysis, and an ROC curve analysis. The DECT diagnostic accuracy was assessed in a dichotomized model (grades 1–2 = no herniation; 3–5 = herniation), using MRI as reference. Results: A strong correlation was observed between DECT and MRI scores across all readers (mean Pearson’s r = 0.826, p < 0.001). The average exact agreement between DECT and MRI was 79.4%, with the highest concordance at L1–L2 (86.7%) and L5–S1 (80.4%). The interobserver agreement was substantial (mean Cohen’s κ = 0.765), with a near-perfect agreement between the two most experienced readers (κ = 0.822). The intraclass correlation coefficient was 0.906 (95% CI: 0.893–0.918). The ROC analysis showed excellent performance (AUC range: 0.953–0.986). In the dichotomous model, DECT demonstrated a markedly higher sensitivity than conventional CT (95.1% vs. 57.2%), with a comparable specificity (DECT: 99.0%; CT: 96.5%) and improved overall accuracy (98.4% vs. 90.0%). Subgroup analyses by age and disk location revealed no statistically significant differences. Conclusions: The use of DECT dedicated color-coded fat map reconstructions showed high diagnostic performance in the assessment of lumbar disk herniations compared to MRI. These findings support the development of dedicated post-processing tools, facilitating the broader clinical adoption of spectral CT, especially in cases where MRI is contraindicated or less accessible. Full article

Ultra-high-energy cosmic rays (UHECRs) are the most energetic particles known—and yet their origin is still an open question. However, with the precision and accumulated statistics of the Pierre Auger Observatory and the Telescope Array, in combination with advancements in theory and modeling—e.g., of the Galactic magnetic field—it is now possible to set solid constraints on the sources of UHECRs. The spectrum and composition measurements above the ankle can be well described by a population of extragalactic, homogeneously distributed sources emitting mostly intermediate-mass nuclei. Additionally, using the observed anisotropy in the arrival directions, namely the large-scale dipole >8 EeV, as well as smaller-scale warm spots at higher energies, even more powerful constraints on the density and distribution of sources can be placed. Yet, open questions remain—like the striking similarity of the sources that is necessary to describe the rather pure mass composition above the ankle, or the origin of the highest energy events whose tracked back directions point toward voids. The current findings and possible interpretation of UHECR data will be presented in this review. Full article

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a functional imaging modality that can quantify tissue permeability and blood flow. Due to vasculature changes resulting from radiation therapy (RT), DCE-MRI quantitative parameters should be significantly different in regions receiving a high radiation dose compared to regions receiving a low radiation dose. This study sought to determine whether a significant difference exists in post-head-and-neck-cancer (HNC)-RT DCE-MRI quantitative parameters (K^trans and v_e) between regions of the mandible receiving a high radiation dose and regions of the mandible receiving a low radiation dose. Methods: DCE-MRI was acquired from HNC subjects post-RT. The DCE-MRI quantitative parameters K^trans and v_e were obtained through Tofts model fitting. Four mandible sections (left ramus, left body, right ramus, and right body) were delineated on subject mandible contours. Two Friedman tests comparing the mean K^trans and v_e in low-dose (≤60 Gy) areas of the four mandible regions were computed. If the Friedman test determined that a significant difference for a parameter between mandible regions exists, post hoc Wilcoxon signed-rank tests were completed comparing the four mandible regions. If the Friedman test determined that there was no significant difference between mandible regions, a Wilcoxon signed-rank test was used to determine whether a significant difference exists in the parameter between high-dose (>60 Gy) and low-dose (≤60 Gy) mandible regions. Results: 48 HNC subjects were included in the analysis. The Friedman tests showed no significant difference in v_e means between mandible regions (${\chi }_{\left(3\right)}^2$ = 1.63, p = 0.44) and a significant difference in K^trans means between mandible regions (${\chi }_{\left(3\right)}^2$ = 10.29, p = 0.005). Post hoc testing between K^trans mandible regions found that the left body and right body differed significantly from the left ramus and right ramus. The Wilcoxon signed-rank test comparing the mean v_e between high- and low-dose mandible regions found a significant difference (W = 214, p = 0.00013). Conclusions: no inherent difference in the DCE-MRI quantitative parameter v_e was observed within subject mandibles, but a significant difference was observed between v_e means in high- and low-radiation-dose mandible regions. These results provide evidence of the utility of DCE-MRI to monitor mandible vasculature changes resulting from head and neck cancer radiation therapy. Monitoring post-HNC-RT mandible vasculature changes is important to initiate earlier toxicity management and ultimately improve HNC survivors’ quality of life. Full article