Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh

Abstract The Poisson equation is commonly encountered in engineering, for instance, in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field. In the present work, we propose a novel fully convolutional neural network (CNN) architecture to infer the solution of the Poisson equation on a 2D Cartesian grid with different resolutions given the right-hand side term, arbitrary boundary conditions, and grid parameters. It provides unprecedented versatility for a CNN approach dealing with partial differential equations. The boundary conditions are handled using a novel approach by decomposing the original Poisson problem into a homogeneous Poisson problem plus four inhomogeneous Laplace subproblems. The model is trained using a novel loss function approximating the continuous ${L}^p$ norm between the prediction and the target. Even when predicting on grids denser than previously encountered, our model demonstrates encouraging capacity to reproduce the correct solution profile. The proposed model, which outperforms well-known neural network models, can be included in a CFD solver to help with solving the Poisson equation. Analytical test cases indicate that our CNN architecture is capable of predicting the correct solution of a Poisson problem with mean percentage errors below 10%, an improvement by comparison to the first step of conventional iterative methods. Predictions from our model, used as the initial guess to iterative algorithms like Multigrid, can reduce the root mean square error after a single iteration by more than 90% compared to a zero initial guess.</jats:p

Towards a reliable seismic microzonation in Tehran, Iran

A hybrid method for the calculation of realistic synthetic seismograms in laterally heterogeneous, anelastic media has been used to model the ground motion in Tehran city. The synthetic records compare reasonably well with the observed ground motion due to the 2004 Firozabad Kojor earthquake located 60 km north of Tehran. The ratio between the response spectrum for the signals calculated along a laterally varying structure, Sa(2D), and that for the signals at the bedrock regional reference structure, Sa(1D), shows a high amplification of seismic waves in Tehran. The procedure can be readily implemented for the entire Tehran city, which experienced large historical earthquakes in the past, simply by extending the analysis, made so far, in space and to different scenario earthquakes, consistent with the local and regional tectonics

High rates of nontuberculous mycobacteria isolation from patients with presumptive tuberculosis in Iran

Nontuberculous mycobacteria (NTM) can cause disease which can be indistinguishable from tuberculosis (TB), posing a diagnostic and therapeutic challenge, particularly in low- and middle-income settings. We aimed to investigate the mycobacterial agents associated with presumptive clinical pulmonary TB in Iran. A total of 410 mycobacterial isolates, obtained between March 2014 and January 2016, from 7600 clinical samples taken from consecutive cases of presumptive diagnosis of TB were identified. Phenotypic and molecular tests were used to identify the isolated organisms to the species level. Single-locus and multilocus sequence analysis based on 16S rRNA, rpoB, hsp65 and ITS locus were used to confirm the results. Of 410 consecutive strains isolated from suspected TB subjects, 62 isolates (15.1%) were identified as NTM. Patients with positive NTM cultures met American Thoracic Society diagnostic criteria for NTM disease. Mycobacterium simiae was the most frequently encountered (38.7%), followed by Mycobacterium fortuitum (19.3%), M. kansasii (17.7%) and M. avium complex (8.0%). Isolation of NTM, including M. simiae, from suspected TB cases is a serious public health problem and merits further attention by health authorities, physicians and microbiologists Keywords: Iran, mycobacterium, Mycobacterium simiae, nontuberculous, tuberculosi

The 7H11 Agar Medium Supplemented with Calf Bovine Serum for Susceptibility Testing of Mycobacterium tuberculosis Isolates Against Pyrazinamide

Despite its importance, pyrazinamide (PZA) is a blind spot in drug susceptibility testing in tuberculosis laboratories. The aim of this study was to set up a reliable agar-based proportion method for detection of PZA-resistant phenotypes using Middlebrook 7H11 agar supplemented with calf bovine serum (CBS) compared with albumin/dextrose/catalase (ADC) enrichment and pncA/rpsA sequencing results. The 7H11 agar medium supplemented with 10 ADC or 10 CBS (pH 6.2) and 100 mu g/mL PZA was used to detect PZA resistance among 64 Mycobacterium tuberculosis isolates. Sanger sequencing and whole-genome sequencing were performed to track mutations in the pncA, rpsA, and their upstream regions. A total of 43 rifampicin/multidrug-resistant, 20 drug-susceptible, and 1 isoniazid mono-resistant M. tuberculosis isolates were investigated. The 7H11+ADC and 7H11+CBS could detect 22 and 23 PZA-resistant strains, respectively. With the same specificity, the sensitivity and accuracy of 7H11+CBS was found to be a little greater than 7H11+ADC in PZA resistance detection compared with sequencing results. Twenty-four mutant strains were found to have different mutations in pncA-upstream, pncA and rpsA genes, in which Gly97Asp was the most dominant mutation. The results obtained from 7H11+CBS were comparable to the results of 7H11+ADC. Therefore, the 7H11 agar proportion method would be a less-expensive test using CBS and produces reliable results