Relative viscosity models and their application to capillary flow data of highly filled hard‐metal carbide powder compounds

The rheological behavior of highly filled polymer systems used in powder injection molding (PIM) technology strongly influences the final properties of the products. In this study, the capillary flow data of multi‐component polymer binders—based on polyethylene, paraffin, ethylene‐based copolymers, and polyethylene glycol—compounded with three various hard‐metal carbide powders were employed. The rheology of such highly filled (up to 50 vol%) multiphase systems is necessarily a complex phenomenon characterized by strain dependent, non‐Newtonian properties complicated by flow instabilities and yield. Over 15 mathematical models proposed for highly filled systems were tested, some of them calculating the maximum filler loading. Due to the complex structure of the filler (irregular shape, particle size distribution) and a multi‐component character of the binder, the applicability of these models varied with the powder‐binder systems studied. However, the particular values of maximum loadings are in good accordance with the predictions based on powder characteristics. Simple modification of Frankel‐Acrivos model to the systems containing unimodal hard‐metal carbide powders with particles of an irregular shape and broad particle size distribution gave precise agreement between experimental data and model prediction. POLYM. COMPOS., 26:29–36, 2005. © 2004 Society of Plastics Engineers.     

A quantum mutation-based backtracking search algorithm

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.

     

Performance assessment of a solar still using blackened surface and thermocol insulation

Fluoride contaminated drinking water is a severe problem in many parts of the world because of fluoride-related health hazards, which are considered to be a major environmental problem today. The present work is aimed at utilizing solar energy for removal of fluoride from drinking water by using a “solar still”. Also tests have been conducted with the “solar still” to find out hourly output rate and “still efficiencies” with various test matrixes. It is observed that the distillate from “solar still” showed a fluoride reduction of 92–96%. Further, the efficiency of “solar still” got increased by 11% when capacity of water in the solar basin was raised from 10 to 20 L. Upon suitable modification of the solar basin with appropriate base liner and insulation, this efficiency of the “solar still” is found to be further increased by 6% with a 20 L basin capacity.     

A novel quasi-oppositional chaotic antlion optimizer for global optimization

This paper proposes a novel quasi-oppositional chaotic antlion optimizer (ALO) (QOCALO) for solving global optimization problems. ALO is a population based algorithm motivated by the unique hunting behavior of antlions in nature and exhibits strong influence in solving global and engineering optimization problems. In the proposed QOCALO algorithm of the present work, the initial population is generated using the quasi-opposition based learning (QOBL) and the concept of QOBL based generation jumping is utilized inside the main searching strategy of the proposed algorithm. Utilization of QOBL ensures better convergence speed of the proposed algorithm and it also provides better exploration of the search space. Alongside the QOBL, a chaotic local search (CLS) is also incorporated in the proposed QOCALO algorithm. The CLS guides local search around the global best solution that provides better exploitation of the search space. Thus, a better trade-off between exploration and exploitation holds for the proposed algorithm which makes it robust. It is observed that the proposed algorithm offers better results than the original ALO in terms of solution quality and convergence speed. The proposed QOCALO algorithm is implemented and tested, successfully, on nineteen mathematical benchmark test functions of varying complexities and the experimental results are compared to those offered by the basic ALO and some other recently developed nature inspired algorithms. The efficacy of the proposed algorithm is further utilized to solve three real world engineering optimization problems viz. (a) the placement and sizing problem of distributed generators in radial distribution networks, (b) the congestion management problem in power transmission system and (c) the optimal design of pressure vessel.     

Study of the antiferromagnetic phase in Sm(Mn1−xCrx)2Ge2

Magnetic and thermal expansion measurements have been carried out on the polycrystalline Sm(Mn_1−xCr_x)₂Ge₂ samples to see how the antiferromagnetie (AFMII) region in SmMn₂Ge₂ is affected by Cr substitution. It is found that the antiferromagnetic region disappears for samples with less than 2 at.% of Cr. Sharp changes in the thermal expansivity (Δl/l) at FMI–AFMII and AFMII–FMII transitions are observed, indicating first order transitions. The decrease in relative thermal expansivity at the two transitions with the increase of Cr concentration is related to the decrease in the stability and the temperature-range of the AFMII phase observed in magnetization measurements. A spin reorientation transition (T_SR) has been observed for x=0, at 148 K. It is found that the T_SR increases with the increase of Cr concentration. A magnetic phase diagram as a function of Cr concentration in Sm(Mn_1−xCr_x)₂Ge₂ has been constructed.     

Hedgehog Antagonist Pyrimidine–Indole Hybrid Molecule Inhibits Ciliogenesis through Microtubule Destabilisation

One of the crucial regulators of embryonic patterning and tissue development is the Hedgehog‐glioma (Hh‐Gli) signalling pathway; its uncontrolled activation has been implicated in different types of cancer in adult tissues. Primary cilium is one of the important factors required for the activation of Hh signalling, as it brings the critical components together for key protein–protein interactions required for Hh pathway regulation. Most of the synthetic and natural small molecule modulators of the pathway primarily antagonise Smoothened (Smo) or other effectors like Hh ligand or Gli. Here, we report a previously described Hh antagonist, with a pyrimidine–indole hybrid (PIH) core structure, as an inhibitor of ciliogenesis. The compound is unique in its mode of action, as it shows perturbation of microtubule dynamics in both cell‐based assays and in vivo systems (zebrafish embryos). Further studies revealed that the probable targets are α‐tubulin and its acetylated form, found in the cytoplasm and primary cilia. PIH also showed axonal defasiculation in developing zebrafish embryos. We thus propose that PIH antagonises Hh signalling by repressing cilia biogenesis and disassembling α‐tubulin from its stabilised form.     

Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition

In this paper, the concept of finding an appropriate classifier ensemble for named entity recognition is posed as a multiobjective optimization (MOO) problem. Our underlying assumption is that instead of searching for the best-fitting feature set for a particular classifier, ensembling of several classifiers those are trained using different feature representations could be a more fruitful approach, but it is crucial to determine the appropriate subset of classifiers that are most suitable for the ensemble. We use three heterogenous classifiers namely maximum entropy, conditional random field, and support vector machine in order to build a number of models depending upon the various representations of the available features. The proposed MOO-based ensemble technique is evaluated for three resource-constrained languages, namely Bengali, Hindi, and Telugu. Evaluation results yield the recall, precision, and F-measure values of 92.21, 92.72, and 92.46%, respectively, for Bengali; 97.07, 89.63, and 93.20%, respectively, for Hindi; and 80.79, 93.18, and 86.54%, respectively, for Telugu. We also evaluate our proposed technique with the CoNLL-2003 shared task English data sets that yield the recall, precision, and F-measure values of 89.72, 89.84, and 89.78%, respectively. Experimental results show that the classifier ensemble identified by our proposed MOO-based approach outperforms all the individual classifiers, two different conventional baseline ensembles, and the classifier ensemble identified by a single objective?Cbased approach. In a part of the paper, we formulate the problem of feature selection in any classifier under the MOO framework and show that our proposed classifier ensemble attains superior performance to it.     

Preparation and characterization of poly(vinyl alcohol)‐poly(vinyl pyrrolidone) blend: A biomaterial with latent medical applications

Aqueous solutions of poly(vinyl alcohol) and poly(vinyl pyrrolidone) are blended and films are produced by casting method with the further intention of being used as bio‐materials with latent medical application. Glutaraldehyde, 4,4′‐diazido‐2,2′‐stilbenedisulfonic acid disodium salt tetra‐hydrate are used as crosslinker agents, whereas lactic acid is the plasticizer in the blend. The obtained films are characterized by differential scanning calorimetry (DSC), mechanical properties, swelling and solubility behavior. DSC measurements show that the blends exhibit a single glass transition temperature indicating that they are miscible, even in the presence of the plasticizer and crosslinker agents. By the combination of all mentioned additives, a relevant enhancement of the swelling is observed, accompanied by a stabilization of the solubility during the tested time. Finally, mechanical properties show an appropriate performance in the studied parameters. As a consequence, the obtained films could be suitable for use as medium or long‐term implants. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013