Dye-tolerant marine Acinetobacter baumannii-mediated biodegradation of reactive red

The objective of this study was to isolate a potent dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.Reactive red 198 is an extensively used textile dye and is a major environmental pollutant in water bodies. In this study, a bacterial strain was isolated from sea sediments and identified as Acinetobacter baumannii with 16S rRNA sequencing. The isolated bacteria were immobilized in calcium alginate and decolorization studies were carried out to determine the optimum pH, temperature, dye concentration, inoculum volume,and static/agitated condition using the one factor at a time(OFAT) approach. The Box-Behnken design, a type of response surface methodology,was adopted to improve the degradation efficiency. At 37℃ using an inoculum volume of six beads, 96.20% decolorization was observed in 500 mg/L of reactive red 198 after 72 h. Dye degradation was confirmed with UV-visible spectroscopy and Fourier-transform infrared(FTIR)spectroscopy studies of the dye and degraded metabolites. Microbial toxicity studies using Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa and phytotoxicity studies using Vigna radiata proved that the toxicity of the dye was significantly reduced after degradation. We can conclude that the isolated A. baumannii strain is an efficient dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.     

Region based coronary artery segmentation using modified Frangi's vesselness measure

Recent research suggests that the cardiovascular diseases (CVDs), seem to be the foremost cause of mortality among the world populace. Three dimensional (3D) imaging modality such as computed tomography angiography(CTA) is a standard noninvasive imaging modality which has great potentials for the visualization of heart and coronary arteries. This article presents a fully automated method for coronary artery extraction using modified Frangi's vesselness measure and region based segmentation. In this article, grayness and gradient based measures are used while computing Frangi's vesselness measure to improve the extraction of coronary arteries. The obtained vesselness measures are utilized for automatically computing the location of ostia. The locations of ostia are then used as starting seed points in region growing segmentation to extract coronary arteries. Three major coronary arteries, namely the left anterior descending artery (LAD), left circumflex artery (LCX) and right coronary artery (RCA) are segmented using the proposed method and the centerlines are extracted for the main coronary branches. The performance of the proposed method is evaluated using 12 3D CCTA data set. The experimental results reveal that during the calculation of modified Frangi's vesselness measure the proposed method gives improved results. The qualitative results obtained during the segmentation stage are also convincing. The average segmentation accuracy and overlap measure of the proposed method are 97.4% and 77.86%, respectively. Hence, the proposed automated approach can detect and extract coronary arteries in CCTA images with high performance.     

Influence of Axial Force on Tensile Strength and Microstructural Characteristics of Friction Stir Buttwelded Aluminum Alloy/Steel Joints

Strength of Materials - In this work, the effect of axial force on the tensile strength, microhardness, joint interface microstructure and fracture surface morphology of friction stir welded (FSW)...     

Facile synthesis of heterostructure NiO–SnO2 nanocomposite for selective electrochemical determination of l-cysteine

Journal of Materials Science: Materials in Electronics - In this present research, heterostructure NiO–SnO2 nanocomposite modified electrode was developed to determine l-cysteine molecule....     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Miniaturization of monopole antenna using high refractive index metamaterial loading

In this article, miniaturization of the wire monopole antenna with the help of high refractive index (HRI) metamaterial is presented. For the first time, HRI medium is realized by using the array of single ring split ring resonator. By surrounding the wire monopole with the array of SR‐SRRs, the effective wavelength is squeezed in the vicinity of the near field dominance. By the loading of the monopole with such an HRI medium, the size of the antenna is reduced from 29.5 to 16 mm, without any deformation in the current distribution and radiation pattern corresponding to the fundamental resonance. The simulated and measured results are agreed to the same.     

Coaxial‐fed high‐gain triple‐band zeroth‐order circularly polarized antenna using pseudo‐open termination and asymmetric unit cells

A coaxial‐fed tri‐band zeroth‐order resonance (ZOR) circularly polarized antenna with higher gains for all the excited ZORs is designed and analyzed in this paper. Epsilon negative transmission line (ENG TL) and pseudo‐open termination (P‐OT) unit cells with different series capacitances (C_S and C_S1 ) resonate shunt ZOR (f_sh ) and two series ZORs (f_se and f_se1 ), respectively. Asymmetric unit cell concept is applied to ENG‐TL and P‐OT unit cells to create vertical and horizontal components, and the 90° phase shift is provided by the ZOR, resulting in circular polarization (CP). Left‐hand CP (LHCP) is achieved by creating two 90° right bends to the extended stubs in ENG TL and P‐OT unit cells. Higher gains for all the excited ZORs are achieved by shifting the shorting pins of ENG TL and P‐OT unit cells far away from the center position. After fabrication, the measured resonances occur at 4.64 GHz (f_sh ), 4.04 GHz (f_se ), and 3.86 GHz (f_se1 ) with fractional bandwidths of 1.62%, 1.73%, and 1.6%, respectively. The measured LHCP peak gains are 4.05 dBic (f_sh ), 3.85 dBic (f_se ), and 3.94 dBic (f_se1 ). The average axial ratio obtained is less than 3‐dB in the 10‐dB fractional bandwidth of the proposed antenna.     

Slip-line solution for orthogonal cutting with a chip breaker and flank wear

A slip-line field model for orthogonal cutting with chip breaker and flank wear has been developed. For a worn tool, this slip-line field includes a primary deformation zone with finite thickness; two secondary shear zones, one along the rake face and the other along the flank face; a predeformation zone; a curled chip; and a flank force system. It is shown that the cutting geometry is completely determined by specifying the rake angle, tool-chip interface friction and the chip breaker constraint. The chip radius of curvature, chip thickness, and the stresses and velocities within the plastic region are readily computed. Grid deformation patterns, calculated with the velocity field determined, demonstrate that the predicted effects of changes in frictional conditions at the tool-chip interface and of the rake angle on chip formation are in accord with experimental observations. The calculated normal stress distribution at the tool-chip interface is in general agreement with previously reported experimental measurements. The model proposed predicts a linear relationship between flank wear and cutting force components. The results also show that non-zero strains occur at and below the machined surface when machining with a worn tool. Severity and depth of deformation below the machined surface increases with increasing flank wear. Forces acting on the chip breaker surface are found to be small and suggest that chip control for automated machining may be feasible with other means.     

Transmit Antenna Selection with Optimum Combining for Aggregate Interference in Cognitive Underlay Radio Network

Transmit antenna selection (TAS) is most popular technique in underlay cognitive radio (CR) networks as they increase the capacity of secondary users with less hardware requirements. In this paper, a new scenario of CR ad-hoc network topology is proposed in which apart from primary users, there are multiple number of secondary users which are assumed to be distributed as homogeneous spatial Poisson point process (PPP) and are trying to use the primary spectrum in underlay mode. These multiple secondary transmitters generate the aggregate interference and can degrade the performance of secondary receiver. Here this aggregate interference is estimated and its impact on performance of secondary receiver under unconstrained mode of operation is presented. Further, to enhance the performance of secondary receivers in this scenario, single TAS technique based on maximizing the received signal to interference noise ratio by using optimum combining (OC) method is proposed. Furthermore, in this work the design of end to end Simulink based environment for secondary trans–receiver system with advancements in channel design and estimation is proposed. The bit error rate (BER) analysis is presented and verified for image data for single TAS-OC technique for unconstrained mode in underlay CR network in Rician fading channel. The BER performance is also presented for different number of secondary interference sources which are located at fixed distance in one case and they are assumed to be distributed as PPP in another case.