Simultaneous multi-objective optimization of stainless steel clad layer on pressure vessels using genetic algorithm

Metal cladding is a process of depositing a filler material to enhance the surface properties of base material using a suitable welding process. In this work the clad specimens are produced by surfacing a layer of filler material using weld cladding process to minimize the heat loss across the walls of the pressure vessels. It is done by depositing a low thermal conductivity austenitic stainless steel grade of 316L on structural steel plates used for boiler construction using flux cored arc welding process. The experimental study is carried out as per design of experiments availed for five factors five levels central composite design using response surface methodology. The mathematical models are developed for the prediction of clad layer height, clad layer width and depth of penetration. These models are employed in formulating fitness functions for multi-objective optimization of clad layer dimensions using genetic algorithm (GA). The set of optimal solutions suggested by response surface optimizer and genetic algorithm are compared and discussed. Conformity tests are conducted to validate the prediction capability of developed models and optimum settings. Optimum clad layer dimensions have been arrived and optimized stainless steel clad specimen has been produced. The heat transfer analysis is planned to be conducted in the next phase. The findings can be used in energy efficient design of pressure vessels.     

Sustainable Drilling of Nano SiC Reinforced Al Matrix Composites Using MQL and Cryogenic Cooling for Achieving the Better Surface Integrity

Silicon - Metal Matrix Nano Composites (MMNCs) are progressive alternatives of formal metal materials, are presently accomplishing a growing inclination of research and engineering approaches for...     

Insights into the Interaction of LVV-Hemorphin-7 with Angiotensin II Type 1 Receptor

Hemorphins are known for their role in the control of blood pressure. Recently, we revealed the positive modulation of the angiotensin II (AngII) type 1 receptor (AT1R) by LVV-hemorphin-7 (LVV-H7) in human embryonic kidney (HEK293) cells. Here, we examined the molecular binding behavior of LVV-H7 on AT1R and its effect on AngII binding using a nanoluciferase-based bioluminescence resonance energy transfer (NanoBRET) assay in HEK293FT cells, as well as molecular docking and molecular dynamics (MD) studies. Saturation and real-time kinetics supported the positive effect of LVV-H7 on the binding of AngII. While the competitive antagonist olmesartan competed with AngII binding, LVV-H7 slightly, but significantly, decreased AngII’s k_D by 2.6 fold with no effect on its B_max. Molecular docking and MD simulations indicated that the binding of LVV-H7 in the intracellular region of AT1R allosterically potentiates AngII binding. LVV-H7 targets residues on intracellular loops 2 and 3 of AT1R, which are known binding sites of allosteric modulators in other GPCRs. Our data demonstrate the allosteric effect of LVV-H7 on AngII binding, which is consistent with the positive modulation of AT1R activity and signaling previously reported. This further supports the pharmacological targeting of AT1R by hemorphins, with implications in vascular and renal physiology.     

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.     

Scientific publishing in West Africa: comparing Benin with Ghana and Senegal

We compared scientific indicators related to Benin, Senegal and Ghana. We collected data from Web of Science and used bibliometric indicators like annual production, language and type of publication, citable and cited documents, citations, h-index, field share, specialization index, and international collaboration rate. Results show that Benin performs well regarding the percentage of citable and cited documents, the share of production and the specialization index in the fields of Natural sciences and Agricultural sciences; it occupies the median position with respect to the production and the specialization index in the fields of Engineering and technology on the one hand and Medical and health sciences on the other hand, behind Ghana and ahead Senegal. It lays however behind Ghana and Senegal with respect to the total output, citations per citable or cited documents, h-index, the share of production and specialization index in the fields of Social science and Humanities; it has the highest international collaboration rate. The study revealed that the three countries cooperated less, and only if a third western country intervened. It pointed out the role of Western countries in driving collaboration among developing countries.     

Effect of the surface roughness of conducting polypyrrole thin-film electrodes on the electrocatalytic reduction of nitrobenzene

Conducting polypyrrole (PPy) thin-film electrodes were prepared by the electropolymerization of pyrrole on gold-coated glass plates. Films of various roughnesses were obtained by the variation of the scan rates during electropolymerization. These thin films were modified by doping with 6mM of the dopant NiCl₂. The surface morphology of the films was studied by scanning electron microscopy and atomic force microscopy (AFM), which suggested films prepared with a high scan rate were rougher in nature than the films produced with a low scan rate. The electrocatalytic reduction of nitrobenzene was carried out with these electrodes with the cyclic voltammetry technique in acetonitrile containing 0.1M HClO₄ as a supporting electrolyte. The various results obtained show that the conducting PPy thin-film electrodes were catalytically active toward the electroreduction process. The modified PPy film electrodes doped with NiCl₂ were more active toward nitrobenzene electroreduction than the PPy film alone. The results indicate that the roughness of the films played a very important role in determining their catalytic activity. The PPy films that were more rough in nature were catalytically more active than the smooth films; this may have been due to the availability of more reactive sites in the case of rough films. The apparent diffusion coefficients of the PPy film electrodes were also calculated. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Analysis of blood and tissue in gallbladder cancer

Particle induced X-ray emission, particle induced γ-ray emission studies has been carried out to analyse normal and carcinoma tissues and blood samples of gallbladder of both sexes and seventeen trace elements namely Na, Mg, Al, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br and Pb were estimated in the tissue and blood samples. In the present study, concentration of Zn in the carcinoma gallbladder tissue is less than that of the normal gallbladder tissue. Tobacco habit could be one of the important factors to decrease the elemental concentrations in blood and tissue samples.     

Hybrid visual servoing: A combination of nonlinear control and linear vision

In this paper, the visual servoing problem is addressed by coupling nonlinear control theory with a convenient representation of the visual information used by the robot. The visual representation, which is based on a linear camera model, is extremely compact to comply with active vision requirements. The devised control law is proven to ensure global asymptotic stability in the Lyapunov sense, assuming exact model and state measurements. It is also shown that, in the presence of bounded uncertainties, the closed-loop behavior is characterized by a global attractor. The well known pose ambiguity arising from the use of linear camera models is solved at the control level by choosing a hybrid visual state vector including both image space (2D) information and 3D object parameters. A method is expounded for on-line visual state estimation that avoids camera calibration. Simulation and real-time experiments validate the theoretical framework in terms of both system convergence and control robustness.