Search Results (3,748)

Mica is a naturally layered material recognized for its superior insulation and exceptional barrier properties; however, it is prone to agglomeration, and its compatibility with resin remains to be resolved. In this work, phosphate butynediol ethoxylate (PBEO), synthesized by the reaction of a commercial corrosion inhibitor, butynediol ethoxylate, with phosphorus pentoxide, was employed to modify mica nanosheets (MNs), as evidenced by FTIR, Raman, and XPS. The obtained MN@PBEO demonstrated improved water dispersibility and enhanced compatibility with acrylic latex. EIS measurements revealed that the impedance (|Z|_0.01Hz) for the waterborne acrylic coating with 0.5 wt% MN@PBEO was approximately an order of magnitude greater than that of the pure waterborne acrylic coating after 28 days of immersion in a 3.5 wt% NaCl solution. Additionally, compared to the pure waterborne acrylic coating, the 0.5 wt% MN@PBEO/acrylic nanocomposite coating on Q235 carbon steel exhibited a water diffusion coefficient that was roughly ten times lower, demonstrating substantially enhanced corrosion protection, attributable to its superior barrier properties. Full article

Background/Objectives: Spinal diffusion tensor imaging (sDTI) remains a challenging method for the selective evaluation of key anatomical structures, like pyramidal tracts (PTs) and dorsal columns (DCs), and for reliably quantifying diffusion metrics such as fractional anisotropy (FA), radial diffusivity (RD), mean diffusivity (MD), and axial diffusivity (AD). This prospective, single-center study aimed to assess the reproducibility, robustness, and reliability of an optimized axial sDTI protocol, specifically intended for long fiber tracts. Methods: We developed an optimized Stejskal–Tanner sequence for high-resolution, axial sDTI of the cervical spinal cord at 3.0 T. Using advanced standardized evaluation and post-processing methods, we estimated DTI values for PTs, DCs, and AHs at the level of the second cervical vertebra. Reliability was evaluated through repeated measurements in 16 healthy volunteers and by comparing results from two 3.0 T scanners (Magnetom Skyra and Magnetom Prisma, Siemens Healthineers, Erlangen, Germany). Reproducibility was assessed using paired t-tests, intraclass correlation coefficients (ICCs), Bland–Altman analysis, and coefficients of variation (CVs). Results: The optimized sDTI protocol demonstrated high consistency for FA between test–retest sessions and across scanners. For the Skyra, the DC region showed the highest reliability (average ICC = 0.858) followed by the PT region (average ICC = 0.789). On the Prisma, the PT region reached an average ICC of 0.854, with the DC region at 0.758. Pooled inter-scanner data indicated good-to-excellent agreement, particularly in the PT region (average ICC = 0.860). FA CVs remained low (<10%) across all regions and scanners. RD showed good-to-excellent ICC values for PTs and DCs (average ICC for Skyra 0.642 and 0.769 and 0.926 and 0.830 for Prisma, respectively) but showed a higher CV between 14.6 and 19.4% for these two scanners. Conclusions: Improved sDTI offers highly reproducible FA measurements for all metrics with scanner independence, supporting its potential as a robust tool for detecting and monitoring spinal cord pathologies. Full article

Apparent diffusion coefficient (ADC) values, derived from diffusion-weighted magnetic resonance imaging (DW-MRI), increase with age, reflecting microstructural changes in white matter integrity. However, factors beyond chronological aging may influence cerebral diffusion characteristics. We investigated whether anticoagulant use is associated with favorable white matter ADC profiles, suggesting preserved microvascular health. ADC values were analyzed in cerebral white matter across four age-defined adult cohorts (20–59 years). Minimum, mean, and maximum ADC values were extracted. Patients at the lowest and highest ends of the ADC spectrum within each group were identified. The prevalence of anticoagulant use was compared between groups, and a logistic regression model adjusted for age was used to assess the independent association between anticoagulant use and lower ADC values. Across all cohorts (n = 892), anticoagulated patients (n = 89) were significantly overrepresented among individuals with low ADC values consistent with younger diffusion profiles. Of the anticoagulated patients, 93.3% had ADC values below the lower cut-off limit. In contrast, only 30% of non-anticoagulated patients exhibited such profiles. Anticoagulant use was independently associated with low ADC values after adjusting for age (OR = 4.89, p < 0.0001). Anticoagulation is strongly associated with lower, more favorable ADC values in cerebral white matter, independent of age. These findings support the potential neuroprotective role of anticoagulants and suggest that diffusion MRI may serve as a surrogate marker for early microvascular brain health. Full article

In this study, the effects of non-thermal pretreatment such as corona discharge plasma (CDP-21 kV), dielectric barrier discharge plasma (DBDP-32 kV), and ultrasonic waves of different powers (US-180 W, 210 W, 240 W) on hot-air drying of ferruginous yam were compared. The regulatory effects of ultrasonic and cold plasma pretreatment on the drying characteristics and quality of yam were systematically evaluated by determining the drying kinetic parameters, physicochemical indexes, volatile components, and energy consumption. The results showed that ultrasonic pretreatment significantly improved the drying performance of yam compared with different cold plasma treatments, with the highest drying rate and effective moisture diffusion coefficient in the US-180 W group. In terms of quality, this treatment group exhibited better color retention, higher total phenol content (366 mg/100 g) and antioxidant activity, and optimal rehydration performance. Low-field nuclear magnetic resonance (NMR) analyses showed a more homogeneous water distribution, and gas chromatography–mass spectrometry (GC-MS) identified 55 volatile components. This study confirms that the US-180 W ultrasonic pretreatment technology can effectively improve the drying efficiency and product quality of yam and at the same time reduce the energy consumption. The results of this study provide a practical solution for the optimization of a process that can be replicated in the food drying industry. Full article

To address the challenges of low KCl recovery and high collector consumption during flotation at low temperature, a novel approach with utilizing a compound collector consisting of octadecylamine hydrochloride (ODA) and alcohols (butanol, octanol, and dodecanol) to enhance low-temperature KCl flotation recovery was proposed in this study. The flotation performance and underlying mechanisms of this novel amine–alcohol compound collector were investigated through combination of micro-flotation tests, contact angle measurements, and molecular dynamics simulations. The results revealed that KCl flotation recovery decreased with declining temperature using single ODA as the collector, and the maximum KCl flotation recovery was approximately 40% with an ODA concentration of 1 × 10⁻⁵ mol/L at the temperature of 0 °C. Moreover, amine–alcohol compound collector shows different KCl flotation recovery; among them, dodecanol (DOD) presents the best performance at 25 °C with an ODA concentration of 3 × 10⁻⁶ mol/L. The KCl flotation recovery initially increased and then gradually decreased with increasing the DOD concentration, and 90% KCl recovery was achieved with a DOD concentration of 1.5 × 10⁻⁵ mol/L (DOD:ODA = 5:1 in mole) under 25 °C. Furthermore, this compound collector exhibited high selectivity for KCl/NaCl flotation. Mechanism studies indicated that the trend in contact angle changes on the KCl crystal surface closely mirrored the trend in flotation recovery. Molecular dynamics simulations demonstrated that at 0 °C, the presence of DOD resulted in a higher diffusion coefficient for ODA molecules compared to the system without DOD. Additionally, the water molecules in System 3 exhibited a lower diffusion coefficient and a greater number of hydrogen bonds. This novel compound collector offers a potential solution for improving KCl recovery and reducing ODA consumption during low-temperature flotation. It holds significant theoretical and practical implications for advancing low-temperature KCl flotation technology. Full article

Fuel combustion is a crucial process in energy utilization. As a key bulk transport mechanism, Stefan flow significantly affects heat and mass transfer during char combustion. However, its physical nature and engineering implications have long been underestimated, and no systematic review has been conducted. This paper presents a comprehensive review of Stefan flow in char combustion, with a focus on its impact on mass transfer and combustion behavior under both air-fuel and oxy-fuel conditions. It also highlights the critical role of Stefan flow in enhancing energy conversion efficiency and optimizing carbon capture processes. The analysis reveals that Stefan flow has been widely neglected in traditional combustion models, resulting in significant errors in calculated mass transfer coefficients (up to 21% in air-fuel combustion and as high as 74% in oxy-fuel combustion). This long-overlooked deviation severely compromises the accuracy of combustion efficiency predictions and model reliability. In oxy-fuel combustion, the gasification reaction (C + CO₂ = 2CO) induces a much stronger outward Stefan flow, reducing CO₂ transport by up to 74%, weakening local CO₂ enrichment, and substantially increasing the energy cost of carbon capture. In contrast, the oxidation reaction (2C + O₂ = 2CO) results in only an 18% reduction in O₂ transport. Stefan flow hinders the inward mass transfer of O₂ and CO₂ toward the char surface and increases heat loss during combustion, resulting in reduced reaction rates and lower particle temperatures. These effects contribute to incomplete fuel conversion and diminished thermal efficiency. Simulation studies that neglect Stefan flow produce significant errors when predicting combustion characteristics, particularly under oxy-fuel conditions. The impact of Stefan flow on energy balance is more substantial in the kinetic/diffusion-controlled regime than in the diffusion-controlled regime. This review is the first to clearly identify Stefan flow as the fundamental physical mechanism responsible for the differences in combustion behavior between air-fuel and oxy-fuel environments. It addresses a key gap in current research and offers a novel theoretical framework for improving low-carbon combustion models, providing important theoretical support for efficient combustion and clean energy conversion. Full article

Purpose: This study evaluates the combined prognostic value of the apparent diffusion coefficient (ADC) from multiparametric MRI (mpMRI) and prostate-specific antigen (PSA) levels at 6 months post-radiotherapy (RT) in assessing treatment response in prostate cancer patients treated with RT and androgen deprivation therapy (ADT). Materials and Methods: All prostate cancer patients classified as unfavorable intermediate-risk, high-risk, or very high-risk, according to NCCN criteria, who received ADT and RT between 2008 and 2019 and underwent mpMRI and PSA testing 6 months after RT were included. Patients were stratified into three profiles based on threshold PSA (≤ vs. >0.1 ng/mL) levels and ADC (≤ vs. >1.24 × 10⁻³ mm²/s) values: Profile A: low PSA and high ADC; Profile B: either high PSA/high ADC or low ADC/low PSA; Profile C: high PSA and low ADC. Ten-year progression-free survival (PFS) and metastasis-free survival (MFS) were analyzed using Kaplan–Meier curves and multivariate Cox regression. Results: Ninety-eight consecutive patients were retrospectively analyzed, of which 73 (74.5%) were high-risk. After a mean follow-up of 95.3 months, 19 (19.39%) patients progressed. Ten-year PFS, MFS, and overall survival were 75.6%, 87%, and 89.5% respectively. Progression events were 9.1% (Profile A), 29.4% (Profile B), and 44.4% (Profile C). Eight-year PFS was 89.2% (profile A), 70.9% (profile B) HR: 3.021 (CI 95%: 1.031–8.849; p = 0.044) and 44.4% (profile C) HR: 6.145 (CI 95%: 1.645–22.955; p = 0.007). Multivariate analysis confirmed a higher risk of progression in patients from profile B with HR: 3.958 (CI 95%: 1.18–13.191; p = 0.025) and profile C with HR: 41.945 (CI 95%: 5.000–351.761; p < 0.001) compared to patients from profile A. Metastasis events were 5.5% (Profile A), 8.8% (Profile B), and 33.3% (Profile C). Eight-year MFS was 100% (profile A), 89.6% (profile B) HR: 1.373 (CI 95%: 0.277–6.811; p = 0.689), and 74.1% (profile C) HR: 5.566 (CI 95%: 1.119–27.692; p = 0.047). Conclusions: The integration of PSA response and ADC measures at 6 months post-RT provides an effective combined prognostic factor to identify patients at higher risk of relapse, supporting closer monitoring and potential treatment intensification. Full article

Unconventional oil reservoirs are tight and often host micro-nano pores, and huff and puff is usually adopted for such reservoirs, mainly utilizing the mechanism of spontaneous imbibition. The penetration depth into the matrix during imbibition is one of the key influencing factors of oil recovery. In circumstances where a water phase is present in the reservoir, the injected oil displacement agent may not directly contact the oil phase, but instead needs to diffuse and migrate to the oil–water interface to adjust the capillary force, thereby affecting the imbibition depth. Therefore, the diffusion law of the oil displacement agent can indirectly affect the oil recovery by imbibition. In this study, microfluidic experiments were conducted to investigate the diffusion of nano oil displacement agents at different pore sizes (100 μm). The results show that the concentration distribution of nano oil displacement agents near the injection end was uniform during the diffusion process, and the concentration showed a decreasing trend with increasing depth. As the pore size decreased, the diffusion coefficient also decreased, and the diffusion effect deteriorated. There was a lower limit of pore size that allowed diffusion at approximately 15.66 μm. The diffusion law of the nano oil displacement agent in porous media obtained in this study is of great significance for improving the recovery rate of unconventional oil and gas resources. Full article

This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate features from different modalities of data, thereby improving the accuracy of brain tumor segmentation. We have designed an effective missing modality training method to address the issue of missing modalities in actual clinical scenarios. To evaluate the effectiveness of MSAM, a series of experiments were conducted comparing its performance with U-Net across various modality combinations. The results demonstrate that MSAM consistently outperforms U-Net in terms of both Dice Similarity Coefficient and 95% Hausdorff Distance, particularly when structural modality data are used alone. Through feature visualization and the use of missing modality training, we show that MSAM can effectively adapt to missing data, providing robust segmentation even when key modalities are absent. Additionally, segmentation accuracy is influenced by tumor region size, with smaller regions presenting more challenges. These findings underscore the potential of MSAM in clinical applications where incomplete data or varying tumor sizes are prevalent. Full article

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

This paper addresses the issue of reduced lifespan of coastal concrete piles due to chloride ion corrosion. A combination of concrete mix optimization and pile geometry improvement measures is proposed. Based on the diffusion coefficient optimization of Fick’s second law, the service life prediction of concrete piles in corrosive environments is completed. The results show that, compared to single slag incorporation and the “slag-fly ash” dual-component mix, the “slag-fly ash-corrosion inhibitor” triple-component concrete achieves a 28-day compressive strength of 67.4 MPa, and the chloride ion diffusion coefficient is reduced to 1.14 × 10⁻¹² m²/s, significantly improving overall performance. Finite element simulations reveal that, compared to ordinary square piles, cut-corner square piles can effectively alleviate stress concentration at the pile tip and reduce settlement. The maximum stress is 3.94 MPa, and the settlement is 22.64 mm, representing reductions of about 16.3% and 15.5%, respectively, compared to ordinary square piles. Concrete service life prediction confirms that the concrete with corrosion inhibitors has a predicted service life of 31.5 years, extending 7.4 years and 13.3 years longer than the single slag and the “slag-fly ash” dual-component groups, respectively. The “material-structure” optimization theory proposed in this study provides a theoretical basis and technical path for the long-life design of coastal engineering pile foundations. Full article

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative coding, and semantic clustering with sentence embeddings. Eight core roles were identified: forecasting and prediction, optimisation of energy systems, renewable energy integration, monitoring and anomaly detection, grid management and stability, energy market operations/trading, cybersecurity, and infrastructure and resource planning. According to the results, the most frequently identified roles, based on the average distribution across all three methods, are forecasting and prediction, optimisation of energy systems, and energy market operations/trading. Roles such as cybersecurity and infrastructure and resource planning appear less frequently and are primarily detected through manual interpretation and semantic clustering. Trigram analysis alone failed to capture these functions due to terminological ambiguity or diffuse expression. However, correlation coefficients indicate high concordance between manual and semantic methods (Spearman’s ρ = 0.91), confirming the robustness of the classification. A structured typology of AI roles supports the development of more coherent analytical frameworks in energy research. Future research incorporating full texts, policy taxonomies, and real-world use cases may help integrate AI more effectively into energy security planning and decision support environments. Full article

The increasing depth of coal mine construction has led to complex geological conditions involving high ground stress and elevated groundwater levels, presenting new challenges for water-sealing technologies in rock microfissure grouting. This study investigates ultrafine cement grouting in microfissures through systematic analysis of slurry properties and grouting simulations. Through systematic analysis of ultrafine cement grout performance across water–cement (W/C) ratios, this study establishes optimal injectable mix proportions. Through dedicated molds, sandstone-like microfissures with 0.2 mm apertures and controlled roughness (JRC = 0–2, 4–6, 10–12) were fabricated, and instrumented with fiber Bragg grating (FBG) sensors for real-time strain monitoring. Triaxial stress-permeation experiments under 6 and 7 MPa confining pressures quantify the coupled effects of fissure roughness, grouting pressure, and confining stress on volumetric flow rate and fissure deformation. Key findings include: (1) Slurry viscosity decreased monotonically with higher W/C ratios, while bleeding rate exhibited a proportional increase. At a W/C ratio = 1.6, the 2 h bleeding rate reached 7.8%, categorizing the slurry as unstable. (2) Experimental results demonstrate that increased surface roughness significantly enhances particle deposition–aggregation phenomena at grouting inlets, thereby reducing the success rate of grouting simulations. (3) The volumetric flow rate of ultrafine cement grout decreases with elevated roughness but increases proportionally with applied grouting pressure. (4) Under identical grouting pressure conditions, the relative variation in strain values among measurement points becomes more pronounced with increasing roughness of the specimen’s microfissures. This research resolves critical challenges in material selection, injectability, and seepage–deformation mechanisms for microfissure grouting, establishing that the W/C ratio governs grout performance while surface roughness dictates grouting efficacy. These findings provide theoretical guidance for water-blocking grouting engineering in microfissures. Full article

Based on the literature data on the structure of liquid Pu, the pair contribution to the EAM potential of liquid Pu is calculated and the embedding potential is determined using the dependence of density on temperature. The properties of liquid Pu are calculated at temperatures up to 5000 K and at ambient pressure, as well as upon cooling to 500 K. Agreement with experiment is obtained for density, bulk modulus, and viscosity. Self-diffusion coefficients are calculated. The Stokes–Einstein relation is well satisfied at all temperatures above the melting point but, below it, the deviations are observed, increasing upon cooling. The heat capacity of the real liquid is significantly higher than that of the models. Excess surface energy is considered. Full article

Characterizing complex heterogeneous structures in high-dimensional data remains a significant challenge. Traditional approaches often rely on summary statistics such as histograms, skewness, or kurtosis, which—despite their simplicity—are insufficient for capturing nuanced patterns of heterogeneity. Motivated by a brain tumor study, we consider data in the form of point clouds, where each observation consists of a variable number of points. Our goal is to detect differences in the heterogeneity structures across distinct groups of observations. To this end, we employ the Dirichlet Diffusion Tree (DDT) to characterize the latent heterogeneity structure of each observation. We further extend the DDT framework by introducing a regression component that links covariates to the hyperparameters of the latent trees. We develop a Markov chain Monte Carlo algorithm for posterior inference, which alternatively updates the latent tree structures and the regression coefficients. The effectiveness of our proposed method is evaluated by a simulation study and a real-world application in brain tumor imaging. Full article