Pore scale consideration in unstable gravity driven finger flow

To explain the dynamic behavior of the matric potential at the wetting front of gravity driven fingers, we take into account the pressure across the interface that is not continuous and depends on the radius of the meniscus, which is a function of pore size and the dynamic contact angle θ[subscript d] • θ[subscript d] depends on a number of factors including velocity of the water and can be found by the Hoffman-Jiang equation that was modified for gravity effects. By assuming that water at the wetting front imbibes one pore at a time, realistic velocities are obtained that can explain the capillary pressures observed in unstable flow experiments in wettable and water repellent sands.Keywords: overshoot, fingered flow, dynamic pressure, column flow, preferential flow, dynamic contact angl

Prevalence of Hypertension in Renal Diseases in Iran: Systematic Review and Meta-Analysis.

BACKGROUND: Hypertension is a risk factor for renal disease. Therefore, this study was aimed at estimating the prevalence of hypertension in renal patients in Iran through meta-analysis. METHODS: The search was carried out using authentic Persian and English keywords in national and international databases including IranMedex, SID, Magiran, IranDoc, Medlib, ScienceDirect, Pubmed, Scopus, Cochrane, Embase, Web of Science, Medline, and Google Scholar search engine without any time limitation until 2017. Heterogeneity of studies was assessed using the I 2 index. Data were analyzed using STATA ver 11. RESULTS: In 35 reviewed studies with a sample of 39,621 subjects, the prevalence of hypertension in renal patients was 35% (95% CI: 29%-41%) (25% in women and 18% in men). The prevalence of systolic hypertension in renal patients was 5%, diastolic hypertension 26%, and diabetes 23%. The prevalence of hypertension in hemodialysis patients was 34%, 27% in peritoneal dialysis, 43% in kidney transplantation, and 26% in chronic renal failure. In addition, meta-regression showed that the prevalence of hypertension in renal patients did not significantly decrease during the years 1988-2017. CONCLUSIONS: More than a third of kidney patients in Iran suffer from high blood pressure. The diastolic blood pressure of these patients is about five times higher than their systolic blood pressure. Moreover, the age group under 30 is a high-risk group. The prevalence of hypertension in women with kidney disease is higher than in men. In addition, patients who have kidney transplants are more likely to have high blood pressure than other kidney patients. KEYWORDS: Hypertension; Iran; kidney disease; meta-analysis; renal diseas

Quality of Life of patients with chronic kidney disease in Iran: Systematic Review and Meta-analysis

Introduction: Renal diseases are among the major health problems around the world that cause major changes in patients' lifestyle and affect their quality of lives. The aim of this study was to evaluate the quality of life of patients with chronic kidney disease (CKD) in Iran through a meta-analysis. Materials and Methods: This study was conducted using authentic Persian and English keywords in the national and international databases including IranMedex, SID, Magiran, IranDoc, Medlib, Science Direct, Pubmed, Scopus, Cochrane, Embase, Web of Science, and Medline. The data were analyzed using meta-analysis (random effects model). Heterogeneity of studies was assessed using I2 index. In this study, SF-36: 36-Item Short Form health-related quality of life (HRQOL), kidney disease quality of life-SF (KDQOL-SF), KDQOL and KDQOL-SFTM questionnaires were used. Data were analyzed using STATA Version 11 software. Results: A total of 17200 individuals participated in 45 reviewed studies, and the mean score of CKD patients' quality of life was estimated by SF-36 (60.31), HRQOL (60.51), and KDQOL-SF (50.37) questionnaires. In addition, meta-regression showed that the mean score of CKD patients' quality of life did not significantly decrease during the past years. Conclusion: The mean score of quality of life of patients with CKD was lower in different dimensions in comparison with that of normal people. Therefore, interventional measures should be taken to improve the quality of life of these patients in all dimensions

MSBDA-Net: Multi-scale Siamese Building Damage Assessment Network

During or after natural disasters, information about location, cause, and severity, is crucial for early responders to act accordingly. Building damage is one of the major disaster types that occurred repeatedly. Being able to estimate the extent and location of damaged buildings are important so that emergency personnel and rescue teams can expedite efforts to the right building in affected location. Satellite imagery is a powerful visual resource that can be used to assess the extent of damages within a wide geographical area. However, current post-disaster practice requires manual annotation of damaged buildings, which is labor intensive and time consuming. Resultantly, traditional damage detection methods have been outperformed in terms of accuracy by Deep Learning (DL) architectures such as the Convolutional Neural Networks (CNN). Therefore, we developed a novel framework named Multi-scale Siamese Building Damage Assessment Network (MSBDA-Net). The proposed framework includes a two-step approach. The first stage is building localization, which a mask of all buildings before disaster will be generated. The second stage is a multi-scale Siamese damage assessment model, where the network takes the image pairs contained pre- and post-disaster as input and classify building on different damage levels. The evaluation results of proposed method indicate the applicability of the proposed method in both building segmentation (Fl-score=86.3%) and damage assessment (Fl-score=78.44 %)

On the use of XAI for CNN model interpretation: a remote sensing case study

In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) methods for the interpretation of two Convolutional Neural Network (CNN) classifiers in the field of remote sensing (RS). Specifically, the SegNet and Unet architectures for RS building information extraction and segmentation are evaluated using a comprehensive array of primary- and layer-attributions XAI methods. The attribution methods are quantitatively evaluated using the sensitivity metric. Based on the visualization of the different XAI methods, Deconvolution and GradCAM results in many of the study areas show reliability. Moreover, these methods are able to accurately interpret both Unet's and SegNet's decisions and managed to analyze and reveal the internal mechanisms in both models (confirmed by the low sensitivity scores). Overall, no single method stood out as the best one

Unboxing the Black Box of Attention Mechanisms in Remote Sensing Big Data Using XAI

This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness of CNN-based architectures with and without AMs. Secondly, we attempt to interpret the results produced by the CNNs using explainable artificial intelligence (XAI) methods. We compare CNNs with and without (vanilla) AMs for buildings detection. Five metrics are calculated, namely F1-score, precision, recall, intersection over union (IoU) and overall accuracy (OA). For the XAI portion of this work, the methods of Layer Gradient X activation and Layer DeepLIFT are used to explore the internal AMs and their overall effects on the network. Qualitative evaluation is based on color-coded value attribution to assess how the AMs facilitate the CNNs in performing buildings classification. We look at the effects of employing five AM algorithms, namely (i) squeeze and excitation (SE), (ii) convolution attention block module (CBAM), (iii) triplet attention, (iv) shuffle attention (SA), and (v) efficient channel attention (ECA). Experimental results indicate that AMs generally and markedly improve the quantitative metrics, with the attribution visualization results of XAI methods agreeing with the quantitative metrics

Pore scale consideration in unstable gravity driven finger flow

[2] To explain the dynamic behavior of the matric potential at the wetting front of gravity driven fingers, we take into account the pressure across the interface that is not continuous and depends on the radius of the meniscus, which is a function of pore size and the dynamic contact angle θd. θd depends on a number of factors including velocity of the water and can be found by the Hoffman-Jiang equation that was modified for gravity effects. By assuming that water at the wetting front imbibes one pore at a time, realistic velocities are obtained that can explain the capillary pressures observed in unstable flow experiments in wettable and water repellent sands

T helper type (Th1/Th2) responses to SARS-CoV-2 and influenza A (H1N1) virus: From cytokines produced to immune responses

Cytokines produced by T helper cells (Th cells) have essential roles in the body's defense against viruses. Type 1 T helper (Th1) cells are essential for the host defense toward intracellular pathogens while T helper type 2 (Th2) cells are considered to be critical for the helminthic parasites' elimination swine-origin influenza A (H1N1) virus, a disease led to an epidemic in 2009 and rapidly spread globally via human-to-human transmission. Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic in 2020 and is a serious threat to the public health. Pulmonary immunopathology is the leading cause of death during influenza and SARS-CoV-2 epidemics and pandemics. Influenza and SARS-CoV-2 cause high levels of cytokines in the lung. Both inadequate levels and high levels of specific cytokines can have side effects. In this literature review article, we want to compare the Th1 and Th2 cells responses in SARS-CoV-2 and H1N1