Biological indicators in relation to coastal pollution along Karnataka coast, India

Marine pollutants in relation to planktonic and benthic organisms were examined at two locations along Karnataka coast, one at Kulai (74 degrees 47.74'E and 12 degrees 55.16'N) receiving huge amount of industrial effluents from fertilizer, petroleum and chemical plants along with the sewage discharges. The other site Padubidri (74 degrees 45'E and 13 degrees 10'N) is located 20 km away, which is a typically agricultural and fishing village having no stress of industrial discharges. Although the concentrations of dissolved oxygen (DO), nutrients and trace metals in water and sediment showed marginal differences at these two locations, the concentration of petroleum hydrocarbon (PHC) remained exceptionally high with a maximum of 1523 microg/l at Kulai which is 10 times higher than that at Padubidri (144 microg/l). Biomass and population of phytoplankton and zooplankton showed that the seasonal differences were more conspicuous rather than the regional changes. Macro and meiobenthic population remained high at both the locations during the two seasons. Phytoplankton species indicated that centric diatoms such as Rhizosolenia, Leptocylindricus, Chaetoceros, Thalassiosira and Coscinodiscus contributed to > 90% of population in May and > 70% in January at Kulai. While mixed population of centric, pennate, cyanophycean and dinoflagellates prevailed at Padubidri in January. Lower species diversity and richness accompanied with dominance of fast growing centric diatoms over pennates observed at Kulai act as an index for detection of organic pollution and nutrient enrichment. Similarly, proliferation of benthic bivalves > 54% at Kulai relative to Padubidri suggests that these organisms could sustain organic and industrial pollutants. The results suggest that although Kulai receives large quantities of industrial and sewage effluents responsible for alteration of the ecosystem structure, the excellent wind-driven mixing and tidal flushing keep the waters well aerated thus reducing the severe pollution stress by dispersing the organic and other pollutants. Direct relationship of PHC with Cd and Pb as contaminants, NO3 and PO4 as oxidants of excess PHC and species diversity as promoters of phytoplankton (centric diatoms) and benthic bivalves shown by multiple regression analysis further suggest that these biological parameters could serve as indicators for detecting moderately high environmental stress at Kulai, compared to Padubidri.     

Impact behaviour of GFRP and Kevlar/epoxy sandwich composite plate: Experimental and FE analyses

In this paper, dynamic response of GFRP composite plate under ballistic impact has been studied by experimental tests and also by an efficient FE model. Material characterization of GFRP composite is performed as required for progressive damage analysis of laminate due to impact. Shock effect of impact is considered in the material modeling of composite plate. The influence of the target thickness on the ballistic performance of the composite plate is also studied by considering 3.12, 6.24 and 9.36 mm thick targets. Moreover, sandwich composite plate consisting of GFRP and Kevlar/epoxy in three different combinations of laminate layers is considered to obtain suitable combination for structural application that offer better penetration resistance. Damage pattern, contact force histories and stresses in composite plate are studied in addition to residual velocity and acceleration of projectile. Numerical results from present FE model in terms of residual velocities and damage patterns in composite plate show good agreement with the experimental results.

     

Alkyl amine functional dextran macromonomer‐based thin film composite loose nanofiltration membranes for separation of charged and neutral solutes

Thin film composite (TFC) nanofiltration membranes with defined porous structure of the separation layer are desirable for the concentration of neutral solute and separation of salts from a mixture. Herein, we report the formation of TFC membranes composed of polyamide (PA) separation layer by the interfacial polymerization between new dextran‐butyl amine (Dex‐NH₂) macromonomer and trimesoyl chloride on polysulfone support membrane. The membranes prepared with 1%–1.5% (wt/vol) of Dex‐NH₂ exhibited water permeance of 110–116 L m^?2 h^?1 MPa^?1 with 62%–71% rejection of Na₂SO₄ and 12%–14% rejection of MgCl₂. The membranes also showed about 91% rejection of poly(ethylene glycol) of molecular weight 2000 g/mol and about 11% rejection of NaCl. A decrease in permeance and ions selectivity was observed with increasing concentration of Dex‐NH₂. The dextran chains attached to the PA network restrict the diffusion of Dex‐NH₂ toward the interfacial zone and thereby assist the formation of porous and thin PA layer compared to that when free amine (alkyl diamine) was used. These membranes are applicable for the separation of small molecular weight neutral solutes from mixture containing monovalent salts. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45301.     

Effect of aging on speech features and phoneme recognition: a study on Bengali voicing vowels

The article studies age related variations of speech characteristics of two age groups, in the Bengali language. The study considers 60 speakers in the each age groups, 60–80 years and 20–40 years, respectively. We have considered different voice source features like fundamental frequency, formant frequencies, jitter, shimmer and harmonic to noise ratio. Cepstral domain feature, Mel Frequency Cepstral coefficients (MFCC) of different voiced Bengali vowels are also analyzed for younger and older adult groups. MFCC feature and Hidden Markov model parameter of different voiced vowels are used to study phoneme dissimilarities measure between two age groups. Age related changes in elderly speech affect the automatic speech recognition performance as was observed in our study, raising the need for specific acoustic models for elderly persons.     

Synthesis and Superior Optical‐Limiting Properties of Fluorene‐Thiophene‐Benzothiadazole Polymer‐Functionalized Graphene Sheets

A polymer based on fluorene, thiophene, and benzothiadazole as the donor–spacer–acceptor triad is covalently coupled to reduced graphene oxide (rGO) sheets via diazonium coupling with phenyl bromide, followed by Suzuki coupling. These polymer–graphene hybrids show good solubility in organic solvents, such as chloroform, tetrahydrofuran (THF), toluene, dichlorobenzene, and N,N‐dimethylformamide (DMF), and exhibit an excellent optical‐limiting effect with a 532‐nm laser beam. The optical‐limiting threshold energy values (0.93 J cm^?2 for G–polymer 1 and 1.12 J cm^?2 for G–polymer 2) of these G–polymer hybrids are better than that of carbon nanotubes (3.6 J cm^?2).     

A novel automated magnetic resonance image segmentation approach based on elliptical gamma mixture model for breast lumps detection

This article introduces a novel semisupervised automated segmentation approach for breast magnetic resonance (MR) image on multicore CPU-GPU systems. The basic idea of the proposed method is clustering-based semisupervised classifier devised by elliptical gamma mixture model (EGMM). Parameters of EGMM are identified by the iterative log-expectation maximization (EM) algorithm. The suggested classifier labels the groups of voxels in an input image first and then classifies the image slices using the EGMM. Two different implementations of the proposed algorithm have been developed based on two different types of high-performance computing architectures such as graphics processing units (GPUs) and multicore processors. To realize the real-time segmentation performance of our algorithm with two distinctive architecture, we have tested a set of breast MR images collected from MedPix. Comparison between two architectures in terms of segmentation performance and computational cost is assessed by the analysis of simulation and experimental results.     

Recent developments in spoken term detection: a survey

Spoken term detection (STD) provides an efficient means for content based indexing of speech. However, achieving high detection performance, faster speed, detecting ot-of-vocabulary (OOV) words and performing STD on low resource languages are some of the major research challenges. The paper provides a comprehensive survey of the important approaches in the area of STD and their addressing of the challenges mentioned above. The review provides a classification of these approaches, highlights their advantages and limitations and discusses their context of usage. It also performs an analysis of the various approaches in terms of detection accuracy, storage requirements and execution time. The paper summarizes various tools and speech corpora used in the different approaches. Finally it concludes with future research directions in this area.     

Approximation Algorithms for the Unsplittable Flow Problem

We present approximation algorithms for the unsplittable flow problem (UFP) in undirected graphs. As is standard in this line of research, we assume that the maximum demand is at most the minimum capacity. We focus on the non-uniform capacity case in which the edge capacities can vary arbitrarily over the graph. Our results are: We obtain an approximation ratio for UFP, where n is the number of vertices, is the maximum degree, and is the expansion of the graph. Furthermore, if we specialize to the case where all edges have the same capacity, our algorithm gives an approximation. For certain strong constant-degree expanders considered by we obtain an approximation for the uniform capacity case. For UFP on the line and the ring, we give the first constant-factor approximation algorithms. All of the above results improve if the maximum demand is bounded away from the minimum capacity. The above results either improve upon or are incomparable with previously known results for these problems. The main technique used for these results is randomized rounding followed by greedy alteration, and is inspired by the use of this idea in recent work.     

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.