Synthesis and Characterization of Aromatic–Aliphatic Polyamide Nanocomposite Films Incorporating a Thermally Stable Organoclay

Nanocomposites were synthesized from reactive thermally stable montmorillonite and aromatic–aliphatic polyamide obtained from 4-aminophenyl sulfone and sebacoyl chloride. Carbonyl chloride terminal chain ends were generated using 1% extra sebacoyl chloride that could interact chemically with the organoclay. The distribution of clay in the nanocomposites was investigated by XRD, SEM, and TEM. Mechanical and thermal properties of these materials were monitored using tensile testing, TGA, and DSC. The results revealed delaminated and intercalated nanostructures leading to improved tensile strength and modulus up to 6 wt% addition of organoclay. The elongation at break and toughness of the nanocomposites decreased with increasing clay contents. The nanocomposites were thermally stable in the range 400–450 °C. The glass transition temperature increased relative to the neat polyamide due to the interfacial interactions between the two phases. Water uptake of the hybrids decreased upon the addition of organoclay depicting reduced permeability.     

Role of mineral nutrition in minimizing cadmium accumulation by plants

Cadmium (Cd) is a highly toxic heavy metal for both plants and animals. The presence of Cd in agricultural soils is of great concern regarding its entry into the food chain. Cadmium enters into the soil–plant environment mainly through anthropogenic activities. Compounds of Cd are more soluble than other heavy metals, so it is more available and readily taken up by plants and accumulates in different edible plant parts through which it enters the food chain. A number of approaches are being used to minimize the entry of Cd into the food chain. Proper plant nutrition is one of the good strategies to alleviate the damaging effects of Cd on plants and to avoid its entry into the food chain. Plant nutrients play a very important role in developing plant tolerance to Cd toxicity and thus, low Cd accumulation in different plant parts. In this report, the role of some macronutrients (nitrogen, phosphorus, sulfur and calcium), micronutrients (zinc, iron and manganese), and silicon (a beneficial nutrient) has been discussed in detail as to how these nutrients play their role in decreasing Cd uptake and accumulation in crop plants. Copyright © 2010 Society of Chemical Industry     

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.     

Computation of Structural,Mechanical, Thermal,and Magnetic Characteristics of Newly Designed Quaternary Heusler Alloys CoNbCrZ (Z = Al,Ga, Si,Ge)

Journal of Superconductivity and Novel Magnetism - First-principles calculations accomplished to investigate structural, mechanical, thermal and magnetic properties of newly designed CoNbCrZ...     

Second Law Analysis and Optimization of Elliptical Pin Fin Heat Sinks Using Firefly Algorithm

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems. Based on mass, energy, and entropy balance, three models are developed for thermal resistance, hydraulic resistance, and entropy generation rate in the heat sink. The major axis is used as the characteristic length, and the maximum velocity is used as the reference velocity. The entropy generation rate comprises the combined effect of thermal resistance and pressure drop. The total EGR is minimized by utilizing the firefly algorithm. The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors. It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm. It is demonstrated that the performance of FFA is much better than PPA.     

Generalized Model of Blood Flow in a Vertical Tube with Suspension of Gold Nanomaterials: Applications in the Cancer Therapy

Gold metallic nanoparticles are generally used within a lab as a tracer, to uncover on the presence of specific proteins or DNA in a sample, as well as for the recognition of various antibiotics. They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches. As the cancer cells are very smaller so for the infiltration, the properly sized nanoparticles have been injected in the blood. For this reason, gold nanoparticles are very effective. Keeping in mind the above applications, in the present work a generalized model of blood flow containing gold nanoparticles is considered in this work. The blood motion is considered in a cylindrical tube under the oscillating pressure gradient and magnetic field. The problem formulation is done using two types of fractional approaches namely CF (Caputo Fabrizio) and AB (Atangana-Baleanue) derivatives, whereas blood is considered as a counter-example of Casson fluid. Exact solutions of the problem are obtained using joint Laplace and Hankel transforms, and a comparative analysis is made between CF and AB. Results are computed in tables and shown in various plots for embedded parameters and discussed. It is found that adding 0.04-unit gold nanoparticles to blood, increase its heat transfer rate by 4 percent compared to regular blood. It is also noted that the heat transfer can be enhanced in the blood with memory.1     

Sliding mode control with PID sliding surface and experimental application to an electromechanical plant

In this study, a sliding mode control system with a proportional+integral+derivative (PID) sliding surface is adopted to control the speed of an electromechanical plant. A robust sliding mode controller is derived so that the actual trajectory tracks the desired trajectory despite uncertainty, nonlinear dynamics, and external disturbances. The proposed sliding mode controller is chosen to ensure the stability of overall dynamics during the reaching phase and sliding phase. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. The chattering problem is overcome using a hyperbolic function for the sliding surface. Experimental results that are compared with the results of conventional PID verify that the proposed sliding mode controller can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.     

Structure–function analysis of D9N and N291S mutations in human lipoprotein lipase using molecular modelling

Lipoprotein lipase (LPL) plays a central role in lipid metabolism. The D9N and N291S mutations in the LPL gene are associated with elevated triglyceride and decreased HDL-cholesterol levels. Published in vitro expression studies suggest that these two mutations are associated with reduced LPL enzymatic activity. We sought to gain further insight on the impact of these two mutations on the LPL structure and function by molecular modelling techniques. Homology modelling was used to develop a three-dimensional (3D) structure of LPL from human pancreatic lipase. Two separate LPL models for the D9N and N291S substitutions were constructed and compared with the wild type LPL for differences in hydrophobicity, atomic burial, hydrogen bond pattern, and atomic mobility. In comparison to the wild type model, the 9N model was associated with significantly increased atomic mobility of its neighboring residues, but the catalytic site was not affected. The region near residue 9 in the upper part of the N-domain was considered a candidate site for protein–protein interaction. In the N291S model, alterations in H-bonds and constrained atomic mobility were among conformational changes in the region where the substitution had occurred. These are hypothesized to cause an increase in the rate of dissociation in LPL dimerization, subsequently affecting the LPL enzymatic activity. We also modelled the C-domain of apoCII, the obligatory cofactor of LPL, from 2D NMR data and docked the model with LPL to explore their interaction site. These docking experiments suggest that the C-domain of apoCII interacts with the interface of N- and C-domains of LPL and part of the lid structure that covers the catalytic site. In summary, we provide molecular modelling data on two well-known mutations in the LPL gene to help explain the published in vitro expression findings and propose a possible LPL-apoCII interaction site. Our data indicate that molecular modelling of LPL mutations could provide a valuable tool to understand the effects of a mutation on the structure–function of this important enzyme.     

Learning Translation Templates from Bilingual Translation Examples

A mechanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the different parts must correspond to the respective parts in the translated sentences. The correspondences between similarities and between differences are learned in the form of translation templates. A translation template is a generalized translation exemplar pair where some components are generalized by replacing them with variables in both sentences and establishing bindings between these variables. The learned translation templates are obtained by replacing differences or similarities by variables. This approach has been implemented and tested on a set of sample training datasets and produced promising results for further investigation.     

Supporting ranking queries on uncertain and incomplete data

Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes introduces new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records’ scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. In this paper, we present a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty. Under this model, we formulate several ranking query types with different semantics. We describe and analyze a set of efficient query evaluation algorithms. We show that our techniques can be used to solve the problem of rank aggregation in partial orders under two widely adopted distance metrics. In addition, we design sampling techniques based on Markov chains to compute approximate query answers. Our experimental evaluation uses both real and synthetic data. The experimental study demonstrates the efficiency and effectiveness of our techniques under various configurations.