Triple solutions of micropolar nanofluid in the presence of radiation over an exponentially preamble shrinking surface: Convective boundary condition

In this study, we attempt to obtain all probable multiple solutions of the magnetohydrodynamic (MHD) steady flow of micropolar nanofluid on an exponentially shrinking surface by the consideration of concentration slip, thermal radiation, and convective boundary condition with help of the revised model of Buongiorno. The significance of the mass suction on the existence of multiple solutions is integrated. The suitable pseudo‐exponential similarity variables have been adopted to transfer the system of nonlinear partial differential equations into a system of nonlinear quasi‐ordinary ordinary differential equations. The resultant system has been solved by employing the Runge–Kutta fourth‐order method along with the shooting method. Three different ranges of solutions are noticed, namely triple solutions and single solution. When ranges of the suction parameter are $S\ge S_{c1}$ and $S\ge S_{c2}$, then there exist triple solutions otherwise there exists only single solution. The effect of the numerous emerging parameters on the velocity profile, angular velocity profile, temperature profile, concentration profile, coefficient of skin friction, and local Nusselt and Sherwood numbers are demonstrated graphically. Results reveal that the velocity of the rotating fluid particles near the rigid surface declines regularly by the rise of the micropolar parameter $K$ in the second and first solutions.     

Intelligent computing for the dynamics of fluidic system of electrically conducting Ag/Cu nanoparticles with mixed convection for hydrogen possessions

The present study aims to provide an innovative stochastic numerical solver's application by the use of neural networks with Levenberg-Marquardt backpropagation to examine the dynamics of hydrogen possessions and variable viscosity in the fluidic system of electrically conducting copper and silver nanoparticles with mixed convection. The system of PDEs obtained by mathematical modeling of the physical phenomena are reduced into non-linear ODEs by utilizing suitable transformations. The ODEs dataset is constructed through Adams numerical solver and target parameters for input and output parameter of neural networks. The testing, validation and training processes are exploited in neural network models with learning based on backpropagation of LM method to calculate the solution for different scenarios created on variation of physical parameters of the proposed flow of Reynolds and Vogel models. Validation and verification of neural network model to find the solution of fluid flow problem is endorsed on the assessment of achieved accuracy through mean squared error, error histograms and regression studies.     

Self‐assembled nanoblends of functional polystyrene and a reactive aramid: Morphological and thermomechanical profile

Various blends composed of pure polystyrene (PS) and modified PS [amino‐functionalized polystyrene (PS–NH₂)] were prepared with a new aromatic polyamide obtained through the polycondensation of 1,5‐diaminonaphthalene and 1,4‐phenylenediamine with isophthaloyl chloride. The variation in the morphological and thermophysical profiles in the two blend systems with aramid loading were investigated. The amine functionality introduced to PS enhanced its compatibility with the polyamide because of the formation of an aramid‐graft‐PS copolymer. The grafting, hydrogen bonding, and phenylene‐ring (π–π) stacking between the chains of the two components finally nurtured self‐assembled nanostructured blends. A strong compatibilizing effect was observed for the 50, 60, and 70 wt % aramid blends, where an exceptionally inimitable cocontinuous self‐assembled morphology was formed by PS–NH₂/aramid. Significant developments in the morphology along with thermal and mechanical stability were observed for the reactive PS/aramid system. The most favorable mechanical and thermal data supported by the finest nanostructure were observed with 70 wt % polyamide addition. Future prospects may involve the formation of nanotemplates and nanostructured membranes. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 39954.     

Electrical detection of cancer biomarker using aptamers with nanogap break-junctions

Epidermal growth factor receptor (EGFR) is a cell surface protein overexpressed in cancerous cells. It is known to be the most common oncogene. EGFR concentration also increases in the serum of cancer patients. The detection of small changes in the concentration of EGFR can be critical for early diagnosis, resulting in better treatment and improved survival rate of cancer patients. This article reports an RNA aptamer based approach to selectively capture EGFR protein and an electrical scheme for its detection. Pairs of gold electrodes with nanometer separation were made through confluence of focused ion beam scratching and electromigration. The aptamer was hybridized to a single stranded DNA molecule, which in turn was immobilized on the SiO(2) surface between the gold nanoelectrodes. The selectivity of the aptamer was demonstrated by using control chips with mutated non-selective aptamer and with no aptamer. Surface functionalization was characterized by optical detection and two orders of magnitude increase in direct current (DC) was measured when selective capture of EGFR occurred. This represents an electronic biosensor for the detection of proteins of interest for medical applications.     

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Neural Computing and Applications - The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as...     

Novel aromatic and aromatic–aliphatic poly(thiourea‐amide)s for the extraction of toxic heavy metal ions

A series of novel aromatic and aromatic–aliphatic diamines [isophthaloyl bis(3‐(3‐aminophenyl)thiourea), terephthaloyl bis(3‐(3‐aminophenyl)thiourea), adipoyl bis(3‐(3‐aminophenyl)thiourea), sebacoyl bis(3‐(3‐aminophenyl)thiourea)] were synthesized starting from the dinitro compounds. Spectroscopic and elemental analyses were carried out for the structure elucidation of the monomers. Three series of poly(thiourea‐amide)s (PTAMs) bearing C?S groups were prepared through the condensation of new diamines with the diacid chlorides such as isophthaloyl, terephthaloyl and adipoyl chloride. The ensuing PTAMs were characterized using FTIR, ¹H‐NMR and ¹³C‐NMR techniques. Physical properties of the polymers such as solution miscibility, crystallinity, solution viscosity, molecular weight, and thermal properties were measured. Consequently, good organosolubility of these polymers was experiential in amide solvents as DMAc, DMF, DMSO and NMP. Moreover, PTAMs exhibited η_inh in the range of 0.92–1.56 dL/g and GPC measurements revealed M_w around 607 × 10²‐851 × 10². DSC served to envisage the glass transition temperatures (T_g) of poly(thiourea‐amide)s located between 232 and 258°C and the initial decomposition temperatures (T₀) probed by thermogravimetry were in the range of 305–419°C. Structure‐property relationship of these polymers was also studied. Eventually, solid?liquid extraction tests of the selected poly(thiourea‐amide)s systems revealed excellent results because these polymers show nearly 100% elimination of lead and mercury cations from water media. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

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.     

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.     

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.