Investigation of some oleochemicals as green inhibitors on mild steel corrosion in sulfuric acid

The inhibitive effect of four oleo chemicals (namely; 2-Pentadecyl-1,3-imidazoline (PDI), 2-Undecyl-1,3-imidazoline (UDI), 2-Heptadecyl-1,3-imidazoline (HDI), 2-Nonyl-1,3-imidazoline (NI)), regarded as green inhibitors, were studied for the corrosion protection of mild steel in 0.5 M H₂SO₄. The methods employed were weight loss, potentiodynamic polarization and electrochemical impedance techniques. Scanning electron microscopy (SEM) was carried out on the inhibited and uninhibited metal samples to characterize the surface. The purity of synthesized inhibitors was checked by FT-IR and NMR studies. The inhibition efficiency increased with increase in inhibitor concentration, immersion time and decreased with increase in solution temperature. No significant change in IE values was observed with increase in acid concentration. The best performance was obtained for UDI possessing 96.2% inhibition efficiency at 500 ppm concentration. The adsorption of the compounds on the mild steel surface in the presence of sulfuric acid obeyed Langmuir’s adsorption isotherm. The values obtained for free energy of adsorption and heats of adsorption suggest physical adsorption. The addition of inhibitor decreased the entropy of activation suggesting that the inhibitors are more orderly arranged along the mild steel surface. The potentiodynamic polarization data indicate mixed control. The electrochemical impedance study further confirms the formation of a protective layer on the mild steel surface through the inhibitor adsorption.     

Magnetodielectric composite with ferrite inclusions as substrates for microstrip patch antennas at microwave frequencies

A study has been conducted to estimate the complex permittivity and permeability along with magnetic characterization of different volume fractions of magnetodielectric composites with cobalt ferrite nano inclusions. Using an in touch superstrate technique dielectric properties are estimated. Cavity perturbation technique is used to study the complex permeability of the samples. 4πM_s value and coercivity is measured using vibrating sample magnetometry. Structural and surface morphologies on the composite samples are conducted to determine the size and homogeneous distribution of nano inclusions. The average grain size of cobalt ferrite nanoparticles is found to be ～10 nm. The real part of permittivity and permeability of the samples varies from ～1–2.905 to ～1.01–1.05 with increase in inclusion content from 1% VF to 5% VF, respectively. The tan δ of permittivity and imaginary part of permeability is found to be of the order of ～10^?3 and ～10^?1 respectively. Verification of these composites as potential substrates for microstrip patch antenna is carried out by fabricating simple rectangular patch at 9.5 GHz using transmission line model. Rectangular patch is designed on 5% VF composite system. The return loss for the composite system was found to be ～?19.451 dB which is comparable with that designed on standard glass epoxy substrate (?_r = 4.5).     

Antibacterial,anticancer and antioxidant potential of silver nanoparticles engineered using Trigonella foenum‐graecum seed extract

In this study, the authors report a simple and eco‐friendly method for the synthesis of silver nanoparticles (AgNPs) using Trigonella foenum‐graecum (TFG) seed extract. They explored several parameters dictating the biosynthesis of TFG‐AgNPs such as reaction time, temperature, concentration of AgNO₃, and TFG extract amount. Physicochemical characterisation of TFG‐AgNPs was done on dynamic light scattering (DLS), field emission electron microscopy, energy dispersive X‐ray spectroscopy, X‐ray diffraction and Fourier transform infrared spectroscopy. The size determination studies using DLS revealed of TFG‐AgNPs size between 95 and 110 nm. The antibacterial activity was studied against Escherichia coli, Proteus vulgaris, Pseudomonas aeruginosa and Staphylococcus aureus. The biosynthesised TFG‐AgNPs showed remarkable anticancer efficacy against skin cancer cell line, A431 and also exhibited significant antioxidant efficacy.Inspec keywords: antibacterial activity, cancer, biomedical materials, silver, nanofabrication, nanomedicine, nanoparticles, microorganisms, skin, cellular biophysics, biochemistry, light scattering, X‐ray chemical analysis, X‐ray diffraction, Fourier transform infrared spectra, particle sizeOther keywords: antibacterial potential, anticancer potential, antioxidant potential, silver nanoparticles, Trigonella foenum‐graecum seed extract, eco‐friendly method, biosynthesis, reaction time, AgNO₃ concentration, TFG extract amount, physicochemical characterisation, dynamic light scattering, field emission electron microscopy, energy dispersive X‐ray spectroscopy, X‐ray diffraction, Fourier transform infrared spectroscopy, size determination, TFG‐AgNPs size, Escherichia coli, Proteus vulgaris, Pseudomonas aeruginosa, Staphylococcus aureus, skin cancer cell line A431, Ag     

IM-GSO: A Community Directed Group Search Optimization Approach for Influence Maximization

Abstract

Influence maximization is a fundamental problem in the study of complex relationship networks, such as viral marketing in business application areas. It is directed towards extracting a minimal (or k-sized) subset of most influential nodes with largest cascading effect across the network as per seeding budget. The problem is categorized as NP hard and hence greedy/heuristic techniques are extensively studied in the literature for generating reasonably acceptable solutions. This article proposes a novel nature based heuristic optimization algorithm IM-GSO to dynamically evolve near to optimal K-sized influential seed nodes for varied structural real world networks. IM-GSO smartly incorporates hidden structural patterns like communities, node degrees, betweenness and similarities for efficient candidate population generation. This smartly initialized population is then evolved using a discrete adaption of Group Search Optimization (GSO) algorithm. Correctness of IM-GSO is verified by optimizing two prominent spread estimation functions SIMPATH and MAGA, on varied sized (small/medium/large) networks. Detailed experimental evaluations by execution of 10,000 Monte Carlo simulations under Information Cascade (IC) model indicates a significantly high influence spread for IM-GSO seeds in contrast to standard heuristics techniques.     

Graph based Ranked Answers for Keyword Graph Structure

Keyword query processing over graph structured data is beneficial across various real world applications. The basic unit, of search and retrieval, in keyword search over graph, is a structure (interconnection of nodes) that connects all the query keywords. This new answering paradigm, in contrast to single web page results given by search engines, brings forth new challenges for ranking. In this paper, we propose a simple but effective Fuzzy set theory based Ranking measure, called FRank. Fuzzy sets acknowledge the contribution of each individual query keyword, discretely, to enumerate node relevance. A novel aggregation operator is defined, to combine the content relevance based fuzzy sets and, compute query dependent edge weights. The final rank, of an answer, is computed by non-monotonic addition of edge weights, as per their relevance to keyword query. FRank evaluates each answer based on the distribution of query keywords and structural connectivity between those keywords. An extensive empirical analysis shows superior performance by our proposed ranking measure as compared to the ranking measures adopted by current approaches in the literature.     

Chemical function-based pharmacophore development for novel, selective kappa opioid receptor agonists

In an effort to reduce or eliminate the centrally associated side effects produced by opioid analgesics there has been an interest in the preparation of peripherally acting opioid receptor agonists. These compounds would have very limited or no access to the central nervous system. As a first step towards developing peripheral kappa opioid receptor (KOP) agonists, we have developed a quantitatively predictive chemical function-based pharmacophore model of selective kappa opioid receptor agonists by using the HypoGen algorithm implemented in the Catalyst software. The input for HypoGen was a training set of 26 KOP agonists exhibiting K(i) values ranging between 0.015nM and 2300nM. The best output hypothesis consists of four features: one hydrophobic (HYD), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one positive ionizable (PI) function. The predictive power of the model could be demonstrated by internal and external validation of the generated hypothesis. The resulting Catalyst pharmacophore can be used concurrently for rapid virtual screening of chemical databases to identify novel, selective KOP agonists that may be easily restricted to target tissues by synthetic modification. It is anticipated that such an approach will lead to the generation of novel selective KOP agonists that are clinically useful for the treatment of pain through peripheral mechanisms.     

Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach

The interest in m-payments through mobile phones to replace the use of cash, credit cards or cheques is rapidly increasing in our society. The present study aims to examine the situation of near field communication (NFC) m-payment services along with the determinants of users’ continuance intention. To this intent, a sample of 1840 respondents with experience in using NFC payments participated in an online survey. During the first phase of this research, an structural equation modelling (SEM) technique was used to identify the acceptance predictors of mobile payments as well as to analyse the eventual moderating effect of the gender and age of the users of this tool. The second phase focused on the neural network model’s proficiency in assessing the relative impact of the most relevant predictors stemming from the aforementioned SEM analysis. The results obtained revealed subjective norms, risk, perceived usefulness, customer brand engagement and trust as the most significant antecedents of continuance intention towards NFC payments. The study also discusses the managerial implications derived from this research while assessing and suggesting potential user behaviour-based business opportunities for service providers.

     

Retrieving keyworded subgraphs with graph ranking score

Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology.     

A blockwise relaxation labeling scheme and its application to edge detection in cardiac MR image sequences

We present a segmentation scheme for magnetic resonance (MR) image sequences based on vector quantization of a block-partitioned image followed by a relaxation labeling procedure. By first searching a coarse segmentation, the algorithm yields very fast and effective performance on images that are inherently noisy, and can effectively use the correlation in a sequence of images for robust performance and efficient implementation. The algorithm defines feature vectors by the local histogram on a block-partioned image and approximates the local histograms by normal distributions. The relative entropy is chosen as the meaningful distance measure between the feature vectors and the templates. After initial computation of the normal distribution parameters, a blockwise classification maximization algorithm classifies blocks in the block-partitioned image by minimizing their relative entropy distance for a coarse-resolution segmentation; and finally, finer resolution is obtained by contextual Bayesian relaxation labeling in which label update is performed pixelwise by incorporating neighborhood information. Sequence processing is then performed to segment all images in the sequence. The scheme is applied to left ventricular boundary detection in short-axis MR image sequences, and results are presented to show that the algorithm successfully extracts the endocardial contours and that sequence processing significantly improves edge detection performance and can avoid local minima problems. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 340–350, 1998