Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

Isolation and identification of low-density polyethylene (LDPE) biodegrading bacteria from waste landfill in Yazd

Microorganisms used as a biodegradation technique can provide economic benefits and can be compatible with the environment. The aim of this study was to isolate low-density polyethylene (LDPE) degrading bacteria. The waste samples were collected from Yazd landfills. Biochemical and molecular tests based on 16S rDNA sequencing were done to identify the superior isolates. Biodegradation rate was measured using weight loss measurement, spectroscopic analysis with Fourier-transform infrared (FTIR) spectroscopy and an investigation of surface morphology changes using scanning electron microscopy (SEM). Two biodegrading isolates were identified as Pseudomonas aeruginosa strain SKN1 and strain SKN2. The weight loss of LDPE strips was measured at 10.32%. SEM micrographs showed the surface degradation and colony formation on LDPE strips. The FTIR spectrum revealed the structural changes on LDPE strip surfaces. Bio-decomposition was observed to have taken place and the bacterial strains had a special ability to biodegrade LDPE wastes.     

Investigating the wicking behavior of micro/nanofibrous core-sheath PET–PAN yarn modified by dimethyl 5-sodium sulfoisophthalate

The present research aims at imparting an improved wicking ability to the recycled multifilament yarns by wrapping composite nanofibers to attain an efficient material for filtration purposes. Therefore, polyacrylonitrile nanofibers containing dimethyl 5-sodium sulfoisophthalate nanoparticles were wrapped around the recycled poly(ethylene terephthalate) yarn by means of a novel electrospinning technique. Several tests were performed to investigate the parameters affecting wicking rise and moisture regain of the samples. Taguchi method was used in two separate designs (with or without nanoparticles). Some factors such as polymer solution concentration, mass ratio of nanoparticles, take-up rate, and number of filaments were considered as the variable factors while yarn wicking rate and moisture regain were the response factors. It was found that the hydrophilic nature of nanoparticles together with the ultrafine structure of nanofibers provide yarns with enhanced wicking properties. Although solution concentration is the predominant factor in wicking rate of the yarns containing nanoparticles, the most effective factor in wicking rise and moisture regain for other cases is the number of filaments. The mechanism of nanoparticle effect on fluidic jet during electrospinning process is explained by theory of nanofluids stability which has never been validated experimentally in previous research studies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48185.     

Erratum to: Efficient keyword search over graph-structured data based on minimal covered r-cliques

Frontiers of Information Technology & Electronic Engineering - Unfortunately the second author’s name has been misspelt. It should be read: Abbas NIKNAFS.     

ZnO Nanoparticles as Ethanol Gas Sensors and the Effective Parameters on Their Performance

<正>ZnO nanoparticles are synthesized and applied as ethanol gas sensors.In some cases,the sensitivity and response time of these particles are shown to be higher than that has been reported in the literature.It has been investigated that the most possible reason for this higher gas sensing performance can be attributed to the quantity of the activity coefficient of its initial components.However,other effects such as pH and thermal decomposition are of importance as well.Specific ion interaction(SIT) model is applied to derive the mean activity coefficient values of the additives used in synthesis of ZnO nanoparticles.     

Ultra-low power inductorless differential LNA for WSN application

Analog Integrated Circuits and Signal Processing - In this paper, a wide band inductorless differential low noise amplifier (LNA) is designed and analyzed. To attain ultra-low power consumption,...     

Solid phase extraction of ultra traces mercury (II) using octadecyl silica membrane disks modified by 1,3-bis(2-ethoxyphenyl)triazene (EPT) ligand and determination by cold vapor atomic absorption spectrometry

A facile and highly efficient method was developed for the preconcentration of the ultra trace amounts of mercury (II) ions. Octadecyl silica membrane disk was modified by the recently synthesized triazene ligand, 1,3-bis(2-ethoxyphenyl)triazene (EPT), and cold vapor atomic absorption spectrometry was used to determine the resultant preconcentrated Hg(II) ions. Solution studies with EPT and a series of metal ions were done in advance, and the results showed a strong affinity of EPT to the mercury ions. To perform solid phase extraction, various parameters such as pH of the sample, flow rates and the amount of the ligand were optimized. A linear calibration curve was obtained in the range of 0.02-1.90 μg L⁻¹ with r² = 0.9990 (n = 8), and the limit of detection (LOD) based on three times the standard deviation of the blank was 10.6 ng L⁻¹. The relative standard deviation (RSD) for the determination of 0.1 μg L⁻¹ Hg(II) found to be 2.9% while a RSD value of 1.1% was obtained for the determination of 1.0 μg L⁻¹ Hg(II) (n = 8). The preconcentration and improvement factors were 380 and 74, respectively. The newly developed method was successfully applied to the determination of mercury ions in real water samples.     

Efficient keyword search over graph-structured data based on minimal covered r-cliques

Frontiers of Information Technology & Electronic Engineering - Keyword search is an alternative for structured languages in querying graph-structured data. A result to a keyword query is a...