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Electret monolith filters have the advantage of low pressure drop and high filtration efficiency. In such filters, the filtration of submicron aerosol particles occurs as air passes through millions of microchannels. This article investigates the flow and filtration mechanisms in a representative rectangular microchannel of an electret monolith filter. An improved incompressible lattice Boltzmann method with Bhatnagar–Gross–Krook (traditionally shortened as LBGK) and lattice velocity D3Q15 model is employed to simulate no-slip and slip flows in the rectangular microchannels of a monolith filter. We considered mono-disperse submicron particles and one-way coupling (particle motion was affected by the flow, but the presence of particles did not affect the flow). Based on flow computations, the effects of key dimensionless parameters (Reynolds number, Knudsen number, Stokes number and the dimensionless length of the channel) on the total capture efficiency of mono-disperse submicron particles were investigated. Our results indicate that the optimal monolith filter should be characterized by a Knudsen number between 0.022 and 0.044, and that the dimensionless length of the channel should be between 4 and 8. Copyright © 2016 American Association for Aerosol Research [ABSTRACT FROM PUBLISHER]

Thyroid nodules are common, and patients with potential malignant lesions are usually diagnosed using ultrasound imaging to determine further treatment options. This study aims to propose a computer-aided diagnosis method for benign and malignant classification of thyroid nodules in ultrasound images. We propose a novel multi-task framework that combines the advantages of dense connectivity, Squeeze-and-Excitation (SE) connectivity, and Atrous Spatial Pyramid Pooling (ASPP) layer to enhance feature extraction. The Dense connectivity is used to optimize feature reuse, the SE connectivity to optimize feature weights, the ASPP layer to fuse feature information, and a multi-task learning framework to adjust the attention of the network. We evaluate our model using a 10-fold cross-validation approach based on our established Thyroid dataset. We assess the performance of our method using six average metrics: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC, which are 93.49, 95.54, 91.52, 91.63, 95.47, and 96.84%, respectively. Our proposed method outperforms other classification networks in all metrics, achieving optimal performance. We propose a multi-task model, DSMA-Net, for distinguishing thyroid nodules in ultrasound images. This method can further enhance the diagnostic ability of doctors for suspected cancer patients and holds promise for clinical applications., Competing Interests: Conflict of interestThe authors declare no conflicts of interest., (© Korean Society of Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)