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• Book : 13(01)
• Pub. Date : 2025
• Page : pp.163-172
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The cement-based materials widely used in infrastructure construction, such as bridges and ports, are subjected to seawater erosion and medium erosion during their service life, and their durability has always been a concern. The diffusion coefficient of chloride ions is an important indicator in the research of cement-based materials’ durability, and the pore structure is one of the most fundamental reasons affecting the diffusion behavior of chloride ions. In this paper, Mercury intrusion porosimetry (MIP), Nuclear magnetic resonance (NMR), and Nitrogen adsorption method (NAD) were used to analyze the pore structures of mortars with different volume fractions of sands. The relationship between mortar pore structure and chloride ion diffusion coefficient was established to predict its chloride ion diffusion coefficient. It may provide a new idea for studying the durability of cement-based materials. Results indicated that similar to cement paste, the pore structure of mortar satisfied the fractal characteristics of solid phase within a certain range of pores. The most probable gel pore diameter of mortars with different sand volume fractions was about 4 nm, while the most probable capillary pore diameter was approximately 46 nm, and the critical pore diameter was ranging from 50 to 60 nm. MIP results indicated that with the increase in sand volume fraction (ϕagg), the total porosity (fmip) of the mortar decreased, satisfying the relationship of fmip = 0.1859 − 0.0789ϕagg. However, the porosity of the matrix (fbase) increased with the increase in sand volume fraction, which was due to the introduction of more interfaces by the addition of aggregates. The effective chloride ion diffusion coefficient (Dcp,base) of the matrix can be obtained by fitting. Based on this, the interface transition zone (ITZ) and the cement matrix were comprehensively considered as a whole fractal phase. The predicted value of the chloride ion diffusion coefficient obtained by the Mori–Tanaka homogenization method was in good agreement with the results obtained from rapid chloride migration (RCM) experiments, and the maximum error between the simulated and experimental values did not exceed 11%. This finding can provide new ideas for accurately predicting the chloride ion diffusion coefficient of mortar and even concrete.

• Book : 15(3)
• Pub. Date : 2025
• Page : pp.383-383
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Abstract Background A wide range of congenital and acquired abnormalities can impact the superior vena cava (SVC), with some remaining asymptomatic. The most common anomaly is the persistent left SVC draining into the coronary sinus or right atrium, while the right SVC draining into the left atrium is rarely found. These two conditions may coexist, which is an extremely rare phenomenon. Case presentation A 38-year-old man with a history of resected astrocytoma presented with seizures and cardiac arrest. Laboratory investigations revealed leukocytosis, hyperglycemia, severe acidosis, and elevated cardiac enzymes. Transthoracic echocardiogram was performed and was unremarkable. However, a bubble study was not performed at the time. Brain imaging confirmed a diagnosis of brain micro-abscesses/embolic infarctions, while cardiac computed tomography revealed a right-sided superior vena cava draining into the left atrium with a right-to-left shunt and a persistent left superior vena cava draining into the coronary sinus. Following the discovery of right-to-left shunt on cardiac CT, a transesophageal echocardiogram and a cardiac MRI were booked. However, the patient left against medical advice to go back to his home country and seek medical care there. Conclusion In conclusion, the coexistence of dual drainage of the superior vena cava, with the right SVC draining into the left atrium and the persistent left SVC draining into the right atrium, is an extremely rare congenital anomaly. The right-to-left shunt may result in significant cerebrovascular complications requiring surgical correction, while the persistent left SVC draining into the coronary sinus may require ablation if resulted in arrhythmias.

• Book : 56(1)
• Pub. Date : 2025
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Abstract Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction. Nevertheless, a successful clinical application requires a thorough evaluation of uncertainty to ensure informed clinical judgment. We propose NPB-LDPET, a DL-based non-parametric Bayesian framework for LD PET reconstruction and uncertainty assessment. Our framework utilizes an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to sample from the underlying posterior distribution. We employed the Ultra-low-dose PET Challenge dataset to assess our framework’s performance relative to the Monte Carlo dropout benchmark. We evaluated global reconstruction accuracy utilizing SSIM, PSNR, and NRMSE, local lesion conspicuity using mean absolute error (MAE) and local contrast, and the clinical relevance of uncertainty maps employing correlation between the uncertainty measures and the dose reduction factor (DRF). Our NPB-LDPET reconstruction method exhibits a significantly superior global reconstruction accuracy for various DRFs (paired t-test, $$p<0.0001$$ p < 0.0001 , N=10, 631). Moreover, we demonstrate a 21% reduction in MAE (573.54 vs. 723.70, paired t-test, $$p<0.0001$$ p < 0.0001 , N=28) and an 8.3% improvement in local lesion contrast (2.077 vs. 1.916, paired t-test, $$p<0.0001$$ p < 0.0001 , N=28). Furthermore, our framework exhibits a stronger correlation between the predicted uncertainty 95th percentile score and the DRF ( $$r^2=0.9174$$ r 2 = 0.9174 vs. $$r^2=0.6144$$ r 2 = 0.6144 , N=10, 631). The proposed framework has the potential to improve clinical decision-making for LD PET imaging by providing a more accurate and informative reconstruction while reducing radiation exposure. Graphical abstract

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