Lattice Boltzmann Modelling of Fluid Flow through Porous Media. A Comparison between Pore-Structure and Representative Elementary Volume Methods

In this study, we present a novel comparison between pore-structure (PS) and representative elementary volume (REV) methods for modelling fluid flow through porous media using a second-order lattice Boltzmann method (LBM). We employ the LBM to demonstrate the importance of the configuration of square obstacles in the PS method and compare the PS and the REV methods. This research provides new insights into fluid flow through porous media as a novel study. The behaviour of fluid flow through porous media has important applications in various engineering structures. The aim of this study is to compare two methods for simulating porous media: the PS method, which resolves the details of the solid matrix, and the REV method, which treats the porous medium as a continuum. Our research methodology involves using different arrangements of square obstacles in a channel including in-line, staggered and random for the PS method and a porosity factor and permeability value for the REV method. We found that the porosity and obstacle arrangement have significant effects on the pressure drop, permeability and flow patterns in the porous region. While the REV method cannot simulate the details of fluid flow through pore structures compared to the PS method, it is able to provide a better understanding of the flow field details around obstacles (Tortuosity). This study has important applications in improving our understanding of transport phenomena in porous media. Our results can be useful for designing and optimizing various engineering systems involving porous media

Medical Students’ View about the Effects of Practical Courses on Learning the General Theoretical Concepts of Basic Medical Sciences

Introduction: The basic medical sciences section requires 2.5 years in the medical education curriculum. Practical courses complement theoretical knowledge in this period to improve their appreciation. Despite spending lots of disbursement and time, this period’s efficacy is not clearly known. Methods: One hundred thirty-three General Practitioner (GP) students have been included in this descriptive cross-sectional study and were asked by questionnaire about the positive impact of practical courses on learning theoretical knowledge. Data were analyzed by descriptive statistics. Result: The agreement in “Practical Head and Neck Anatomy” was 40.91% ± 29.45, in “Practical Trunk Anatomy” was 63.62% ± 2.32 and in “Practical Anatomy of Extremities” was 56.16% ± 2.57. In “Practical Histology”, agreement was 69.50%±2.19; “Practical Biophysics” was 45.97%±2.25, “Practical Physiology” 61.75%±2.17; “Practical Biochemistry” 36.28%±2.42; “Practical Pathology” 59.80%±2.53; “Practical Immunology” 56.25%±26.40; “Practical Microbiology and Virology” 60.39%±2.27 and “Practical Mycology and Parasitology” 68.2%± 2.16.Conclusion: GP students in Tabriz University of Medical Sciences are not optimistic about the applicability of practical courses of basic medical sciences lessons

Precise Single-stage Detector

There are still two problems in SDD causing some inaccurate results: (1) In the process of feature extraction, with the layer-by-layer acquisition of semantic information, local information is gradually lost, resulting into less representative feature maps; (2) During the Non-Maximum Suppression (NMS) algorithm due to inconsistency in classification and regression tasks, the classification confidence and predicted detection position cannot accurately indicate the position of the prediction boxes. Methods: In order to address these aforementioned issues, we propose a new architecture, a modified version of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector (PSSD). Firstly, we improve the features by adding extra layers to SSD. Secondly, we construct a simple and effective feature enhancement module to expand the receptive field step by step for each layer and enhance its local and semantic information. Finally, we design a more efficient loss function to predict the IOU between the prediction boxes and ground truth boxes, and the threshold IOU guides classification training and attenuates the scores, which are used by the NMS algorithm. Main Results: Benefiting from the above optimization, the proposed model PSSD achieves exciting performance in real-time. Specifically, with the hardware of Titan Xp and the input size of 320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28 mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object detection models. Besides, the proposed model performs significantly well with larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove that the proposed model has a better trade-off between speed and accuracy.Comment: We will submit it soon to the IEEE transaction. Due to characters limitation, we can not upload the full abstract. Please read the pdf file for more detai

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed

Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images

The COVID-19 pandemic is a global, national, and local public health which causing a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose the patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To overcome the mentioned problems, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into normal and infected tissue. For improving the classification accuracy, we used two different strategies including Fuzzy c-mean clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find a more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved Precision 96%, Recall 97%, F-score, average surface distance (ASD) of 2.8\pm0.3\ mm and volume overlap error (VOE) of 5.6\pm1.2%

Using Sensory Approach to Teach Medicinal Plants: a Before and After Study

Introduction: The most commonly-used method of teaching medicinal plants courses among the faculty of Traditional Medicine is a lecture-based slideshow, but we hypothesize that herb knowledge could be reinforced by using a sensory approach in which students have the opportunity to interact with these plants using their five senses. The aim of this study was to obtain students’ knowledge about the morphological characteristics of current medicinal plants. The students learned about the plants using all senses before, immediately after, and 40 days after intervention. We also assessed the satisfaction rate of students as a result of this educational intervention. Methods: As a pre-test, 27 students who had attended medicinal herb classes answered a questionnaire with open-ended questions about the morphological characteristics of herbs. Immediately after the educational intervention, for their post-test, students filled a questionnaire comprised of the same questions on the pre-test. The mean scores of students in pre-test (A) and post-test (B) were calculated. Forty days after the aforementioned session, students answered a different questionnaire covering the previously discussed morphological characteristics of herbs. The mean scores of participants in this exam were C. A and B, A and C as well as B and C were compared and analyzed by SPSS v.17 (p≤0.001). This workshop was evaluated by a questionnaire. Results: There was a significant difference between A and B, B and C as well as A and C (P-value= 0.001). The rate of student satisfaction on five items of the questionnaire was higher than 90%.Conclusion: Exclusive textbook-based learning of medicinal plants might not be sufficient to understand them, and it seems useful for the faculties to integrate physical sensory experiences into herbal educational methods

Modeling, simulation and applications of ionic polymer metal composites

In the present study, a multiphysics model is presented to predict the behavior of Ionic Polymer Metal Composite (IPMC). The analysis is carried out using commercial software COMSOL. The response of electro actuated IPMC, including the displacement and electric potentials profiles, of the IPMC is numerically calculated. Furthermore, IPMC-fluid interaction is studied with coupling electro-chemo-mechanical model of IPMC with Navier-Stokes equation. The mathematical model used in the numerical analysis consists of three different types of micropumps. Then we focus on the application of IPMC micropump in Biomedical for drug delivery and extracting excess fluid. One of the applications of ionic polymer metal composites is an ophthalmic micropump implant in order to remove excess aqueous humor that may be causing Glaucoma. Therefore, in this study, a low energy IPMC micropump is simulated, in which the fluid is driven by deformation of two IPMC diaphragms. Results show that micropump has the ability to transfer mentioned liquid outside of eye chamber to reduce the pressure and risk of the disease. Another interesting medical application of IPMC micropump is in insulin dispenser device. Simulation of micropump with IPMC diaphragm confirms that IPMC micropump can generate sufficient flow rate of insulin required for treatment of Diabetes. Here, we propose a two-dimensional numerical study on the flow induced by an IPMC cilia vibrating underwater. Concequently, we designed an IPMC cilia integrated micropump that contains various IPMC cilia employed in the upper and bottom side of the fluid channel. Deformation of IPMCs cilia pushes fluid in the channel. Numerical simulation results show that micropump generate fluid flow rate at low electro-potential.No presente estudo, um modelo multifísico é organizado para prever o comportamento do composito de polímeros ionicos e metal (IPMC) com resposta elétrica. A abordagem apresentada aqui, envolvendo quimio-eletro-mecânica, as equações de difusão para concentrações iônicas incorporam os termos de migração e difusão; A equação de Poisson é empregada para calcular diretamente a distribuição do potencial elétrico, e a deformação do IPMC é implementada facilmente pelas equações de pequena deformação. A análise é realizada usando o software comercial COMSOL. A resposta do IPMC accionado por eletrodo, incluindo o deslocamento e os perfis de potenciais elétricos são calculados numericamente. Uma das aplicações dos compostos metálicos de polímero iônico é um implante de microbomba oftálmica para remover o excesso de fluido que pode estar causando glaucoma. Portanto, neste estudo, uma microbomba IPMC de baixa energia é projetada e simulada, na qual o fluido é conduzido pela deformação de dois diafragmas IPMC. Os resultados mostram que a microbomba tem a capacidade de transferir o líquido mencionado fora da câmara do olho para reduzir a pressão e o risco de doença. Outra aplicação médica interessante da microbomba IPMC está em dispositivo dispensador de insulina. A simulação da microbomba com o diafragma IPMC confirma que a microbomba IPMC pode gerar um taxa de fluxo suficiente de insulina necessária para o tratamento do Diabetes. Finalmente, propomos um estudo numérico bidimensional sobre o fluxo induzido por um cílio IPMC deformando debaixo de água. Com o objetivo esses resultados, criamos uma microbomba integrada IPMC cilia que contém vários cílios IPMC empregados nos lados superior e inferior do canal fluido. Os resultados da simulação numérica mostram que a microbomba gera vazão de fluido com baixo potencial elétrico