Corrosion inhibition effect of pyrazole derivatives on mild steel in hydrochloric acid solution

Synthesized compounds, namely methyl 5-(4-Chlorobenzoyloxy)-1-phenyl-1H-pyrazole-3-carboxylate (MCPPC) and 5-(4-methoxyphenyl)-3-(4-methylphenyl)4,5-dihydro-1H-pyrazol-1-yl-(pyridin-4-yl)methanone (MMDPPM) were investigated as corrosion inhibitor for mild steel (MS) in 15% HCl solution using weight loss measurement, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS) methods. Inhibition efficiency was found concentration-dependent and increased by increasing the concentration of MCPPC and MMDPPM. Both of inhibitors were efficient even at a very low concentration of 25 ppm. The inhibitors MCPPC and MMDPPM showed inhibition efficiency of 67.1 and 76.8%, respectively, at 25 ppm, whereas it was 92.0 and 95.9%, respectively, at 250 ppm concentration at 303 K. The potentiodynamic polarization curves showed that MCPPC and MMDPPM act as mixed-type inhibitor in 15% HCl solution. The Nyquist plots showed that charge transfer resistance increased and double-layer capacitance decreased on increasing the concentration of studied inhibitors due to adsorption of inhibitor molecules on MS surface. The adsorption of each inhibitor on MS surface obeys Langmuir adsorption isotherm. On the basis of thermodynamic adsorption parameters, mixed-type adsorption (physisorption and chemisorption) for the studied inhibitors on MS surface was suggested. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and atomic force microscopy (AFM) analyses confirmed the existence of a protective film of the inhibitor on MS surface. The density functional theory was employed for theoretical calculations, and the obtained results were found to be consistent with the experimental findings.     

A path-disjoint approach for blocking probability analysis in hybrid dynamic wavelength routed WDM grooming networks

The present article addresses a novel approach to enhance the overall performance of a WDM optical network. Centralized and distributed approaches for dynamic lightpath establishment are well studied in the literature. Both the approaches have some drawbacks. In this article a hybrid approach is proposed to accept the advantages and discard the disadvantages of both the approaches. With the proposed hybrid approach, a WDM optical network is divided into clusters of nodes. Within a cluster centralized mechanism is applicable whereas connection requests between the node pairs from different clusters follow the distributed mechanism. Also, in this article an analytical model is proposed to compute the expected blocking probability of the proposed hybrid network. First, blocking probabilities for the centralized and distributed approaches are computed and then it is extended for the hybrid approach. The distinguished feature of the proposed analytical model is that it efficiently utilizes the path information of the calls to determine the overall blocking probability of a WDM optical network. It extracts the necessary parameters of a network by simulation and utilize the information to analytically calculate the blocking probability of the network. The NSFNET T1 backbone network is used for the simulation study. To justify the analytical model, the simulation results are compared with that of the analytical model. The simulation results establish the effectiveness of the proposed hybrid approach over the distributed approach in merits of both the call setup time and blocking probability.     

Inhibitory activity of yarrow essential oil on Listeria planktonic cells and biofilms

The inhibitory effects of the essential oil obtained from yarrow (Achillea millefolium) were tested against planktonic cells and biofilms of Listeria monocytogenes and Listeria innocua isolates obtained from food processing environments. The plant essential oil was found to have strong bactericidal activity against planktonic cells which was found to be equivalent to that of tea tree oil (Melaleuca alternifolia). Inhibition of biofilm formation and growth after incubation with different concentrations of yarrow essential oil was assessed by the crystal violet and 3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide reduction assay. Yarrow essential oil significantly inhibited the initial cell attachment of the Listeria cells on polystyrene, stainless steel and high density polyethylene surfaces but was less inhibitory towards 6 h preformed biofilms formed on polystyrene surface. Metabolic activity of the biofilms decreased considerably after incubation with the oil. Yarrow essential oil, or its constituents, may be useful additives for the development of new disinfectant and sanitizer formulations for application in the food industry.     

Dynamic schedule execution in an agent based holonic manufacturing system

The present paper deals with the negotiation based task allocation to the resources for preparing dynamic scheduling in an agent based holonic control framework. The scheduling priority is developed by Multi Objective Optimization on the basis of Ratio Analysis (MOORA) technique under Fuzzy Multi Criteria Decision Making (FMCDM) environment considering several attributes. The well-known Contract Net Protocol (CNP) is followed for the purpose of task allocation by negotiation and cooperation, where message based communication is accomplished by eXtensible markup language (XML) using J2EE. Different Document Type Definitions (DTDs) are developed for intended applications. Necessary modifications in the scheduling arising out of changes in the volume-mix are made by a distributed cooperative problem-solving algorithm to meet the demand without violating the deadline. The algorithm is implemented using HTML code in front-end with Java Server Page (JSP) through Apache Tomcat 6.02 server. It is advocated that the cooperation based teamwork coupled with higher flexibility and agility is the key to success to remain unperturbed and provide reasonably good solution in the face of disturbances and stands superior to its hierarchical counterpart.     

Model development guidelines for UML-RT: conventions,patterns and antipatterns

Software development guidelines are a set of rules which can help improve the quality of software. These rules are defined on the basis of experience gained by the software development community over time. This paper discusses a set of design guidelines for model-based development of complex real-time embedded software systems. To be precise, we propose nine design conventions, three design patterns and thirteen antipatterns for developing UML-RT models. These guidelines have been identified based on our analysis of around 100 UML-RT models from industry and academia. Most of the guidelines are explained with the help of examples, and standard templates from the current state of the art are used for documenting the design rules.     

NUMERICAL ANALYSIS OF ASEPTIC PROCESSING OF A NON-NEWTONIAN LIQUID FOOD IN A TUBULAR HEAT EXCHANGER

The aseptic processing of a non-Newtonian fluid in a tubular heat exchanger with no holding section was simulated numerically. Two tube diameters (1·270 × 10^-2 m and 1·905 × 10^-2 m), two process temperatures (410 K and 422 K) and three flow rates (1·0, 1·5 and 2·0L/min) were included in the analysis. A model liquid food with temperature dependent and shear thinning viscosity was used. For each case, the length of heating section needed to provide a required lethality (F₀) of approximately 6·0 min on the center line for low-acid foods was calculated. The lethalities for Clostridium botulinum and other microorganisms of interest were calculated using the modified General Method. Increasing the temperature from 410 K to 422 K decreased the required length of the heating section by about 20 to 30% for the same average velocity. Thiaminc retention was also calculated for each case and was found to be higher for a smaller diameter tube and lower temperature. The liquid traveling near the wall was predicted to be highly overprocessed and retained less than 10% thiamine     

Response of sandwich composites with nanophased cores under flexural loading

It is well known that in a sandwich structure, the core plays an important role in enhancing the flexural rigidity and by controlling the failure mechanisms. If the core is made from foam, the strength of the core material and the debond strength at the core–skin interface almost entirely dictate the performance of structural sandwich composites especially under flexure. In this investigation attempts have been made to improve the performance of the sandwich by strengthening the core but partially sacrificing the debond fracture toughness of the sandwich construction. Strengthening of the core has been accomplished by infusing nanoparticles into the parent polymer of the core material when it was in the liquid stage. The core material is polyurethane foam made from polymeric isocyanate (Part A) and reacting with polyol (Part B). Spherical nanoparticles such as TiO₂ of about 29 nm in diameters were dispersed in Part A of liquid polyurethane through an ultrasonic cavitation process. The amount of nanoparticles infused into liquid foam varied from 1 to 3% by weight. Once Part A was doped with nanoparticles, it was mixed with Part B, and was cast in a rectangular mold to produce the nanophased polyurethane foam. The nanophased foam was then used with regular S-2 Glass fiber preforms and SC-15 epoxy to manufacture sandwich composites in a VARTM set up. Test coupons were then extracted from foam as well as from sandwich panels to conduct flexural and various other chemical tests. A parallel set of control panels were also made with neat polyurethane core materials. Thermogravimetric and SEM analyses have indicated that the decomposition temperature of the nanophased foams increases by about 27 °C and the cell size almost doubles with nanoparticle infusion. A significant improvement in flexural strength and stiffness has also been observed with 3% loading of TiO₂ nanoparticles. Debond fracture toughness parameters (G_c) were also determined for both categories of sandwich constructions, and it was seen that nanoparticle infusion reduces the value of G_c by almost a factor of three. Despite this reduction, strength of nanophased sandwich increased by about 53% over the neat system. Details of manufacturing and analyses of test results are included in the paper.     

Wireless Node Localization under Hostile Radio Environment using Smart Antenna

This paper presents a resilient localization scheme for wireless sensor networks (WSNs). It suits well in estimation of node position under a corrupted radio environment. Position computation is based on information of angle-of-arrivals (AoA) and references obtained from a few mobile anchors. In the network, anchors are equipped with smart antennas and global positioning system receivers. They broadcast signals in a synchronous and periodic fashion. The neighboring nodes having the signals with received signal strength values above a prescribed threshold level, respond with their respective IDs. Anchors evaluate AoA information from these signals using estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm. Next, they forward beacon messages, containing their references and estimated angles, to the corresponding nodes and move along random trajectories. After receiving three sets of such data, at least, nodes can initiate selective segregation of the inconsistent position estimations. Simulation results attaining higher degree of localization accuracy validate its competency over the existing schemes.

     

Akin-based Orthogonal Space (AOS): a subspace learning method for face recognition

A projection learning space is an approach to mapping a high-dimensional vector space to a lower dimensional vector space. In this paper, we proposed an algorithm, namely, AOS: Akin based Orthogonal Space. The algorithm is driven with two major targets - (i) to choose most representative image(s) from a group of face images of an individual, (ii) finally to produce a learning space which follows a Gaussian distribution to reduce the influence of grosses like non-Gaussianly distributed data noises, variations in facial expression and illumination. To improve the recognition performance, we proposed another approach i.e. fusion between AOS features and a custom VGG features. We justify the effectiveness of the proposed approaches over five benchmark face datasets using two classifiers. Experimental results show that the proposed learning algorithm has obtained maximum of 92.22% recognition rate, as well deep learning based fusion approch greatly improves the recognition accuracy. The comparative performances demonstrate that the proposed method could significantly outperform other relevant subspace learning methods.