Virus‐Templated Plasmonic Nanoclusters with Icosahedral Symmetry via Directed Self‐Assembly

The assembly of plasmonic nanoparticles with precise spatial and orientational order may lead to structures with new electromagnetic properties at optical frequencies. The directed self‐assembly method presented controls the interparticle‐spacing and symmetry of the resulting nanometer‐sized elements in solution. The self‐assembly of three‐dimensional (3D), icosahedral plasmonic nanosclusters (NCs) with resonances at visible wavelengths is demonstrated experimentally. The ideal NCs consist of twelve gold (Au) nanospheres (NSs) attached to thiol groups at predefined locations on the surface of a genetically engineered cowpea mosaic virus with icosahedral symmetry. In situ dynamic light scattering (DLS) measurements confirm the NSs assembly on the virus. Transmission electron micrographs (TEM) demonstrate the ability of the self‐assembly method to control the nanoscopic symmetry of the bound NSs, which reflects the icosahedral symmetry of the virus. Both, TEM and DLS show that the NCs comprise of a distribution of capsids mostly covered (i.e., 6–12 NS/capsid) with NSs. 3D finite‐element simulations of aqueous suspensions of NCs reproduce the experimental bulk absorbance measurements and major features of the spectra. Simulations results show that the fully assembled NCs give rise to a 10‐fold surface‐averaged enhancement of the local electromagnetic field.     

mrPSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

This paper presents a hybrid filter-wrapper feature subset selection algorithm based on particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter model is based on the mutual information and is a composite measure of feature relevance and redundancy with respect to the feature subset selected. The wrapper model is a modified discrete PSO algorithm. This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr²PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO. Hence, mr²PSO uniquely brings together the efficiency of filters and the greater accuracy of wrappers. The proposed algorithm is tested over several well-known benchmarking datasets. The performance of the proposed algorithm is also compared with a recent hybrid filter-wrapper algorithm based on a genetic algorithm and a wrapper algorithm based on PSO. The results show that the mr²PSO algorithm is competitive in terms of both classification accuracy and computational performance.     

Molecularly inherent voltage-controlled conductance switching

Molecular electronics has been proposed as a pathway for high-density nanoelectronic devices. This pathway involves the development of a molecular memory device based on reversible switching of a molecule between two conducting states in response to a trigger, such as an applied voltage. Here we demonstrate that voltage-triggered switching is indeed a molecular phenomenon by carrying out studies on the same molecule using three different experimental configurations-scanning tunnelling microscopy, crossed-wire junction, and magnetic-bead junction. We also demonstrate that voltage-triggered switching is distinctly different from stochastic switching, essentially a transient (time-dependent) phenomenon that is independent of the applied voltage.     

ROBUST ADAPTIVE CONTROL FOR STRICT‐FEEDBACK NONLINEAR SYSTEMS

In this paper, we propose a robust adaptive tracking control based on the backstepping strategy for strict‐feedback nonlinear systems with nonparametric uncertain nonlinearities. It is shown that one can design a stable adaptive control system provided that the uncertain nonlinearities can be decomposed by unknown bounded nonlinear functions and known nonlinear functions. The proposed method can deal with uncertain nonlinearities that appear at the control input term too. It is also shown that suitable choice of design parameters guarantees the convergence of tracking error to any desired bound.     

Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information

In just-in-time (JIT) manufacturing environments, on-time delivery is a key performance measure for dispatching and routing of freight vehicles. Growing travel time delays and variability, attributable to increasing congestion in transportation networks, are greatly impacting the efficiency of JIT logistics operations. Recurrent and non-recurrent congestion are the two primary reasons for delivery delay and variability. Over 50% of all travel time delays are attributable to non-recurrent congestion sources such as incidents. Despite its importance, state-of-the-art dynamic routing algorithms assume away the effect of these incidents on travel time. In this study, we propose a stochastic dynamic programming formulation for dynamic routing of vehicles in non-stationary stochastic networks subject to both recurrent and non-recurrent congestion. We also propose alternative models to estimate incident induced delays that can be integrated with dynamic routing algorithms. Proposed dynamic routing models exploit real-time traffic information regarding speeds and incidents from Intelligent Transportation System (ITS) sources to improve delivery performance. Results are very promising when the algorithms are tested in a simulated network of South-East Michigan freeways using historical data from the MITS Center and Traffic.com.     

Sintering by recirculation of ferrogenous waste

Utilization of more plant wastes for production of quality sinters improves the productivity of the furnace at the same time plant wastes materials are recycled. The ferrogenous waste like dust catcher dust and gas cleaning plant sludge can be utilized in sintering for production of hot metal. Recycling of these iron bearing materials and flux materials have great economic importance in sintering as it is not only utilizing all iron generated byproducts but also the associated fuel rate reduction benefit for sintering process.     

Toughened epoxy adhesive modified with acrylate based liquid rubber

The adhesive and mechanical properties of epoxy resins modified with carboxyl terminated poly(2‐ethylhexyl acrylate) (CTPEHA) liquid rubber have been investigated as a function of the concentration of liquid rubber. CTPEHA was synthesized by the bulk polymerization technique. CTPEHA oligomer was prereacted with the epoxy and the modified epoxy networks were made by curing with an ambient temperature curing agent. The modified epoxy networks were evaluated with respect to their adhesive and mechanical properties. The optimum properties were obtained at about 10–15 phr (phr stands for parts per hundred parts of epoxy resin) concentration of modifier. Fracture surface analysis by optical microscopy (OM) indicates the presence of two phase microstructures. © 2000 Society of Chemical Industry     

In vitro Digestibility of Starch from Native and Germinated Pigeon Pea (Cajanus cajan L.)

The in vitro digestibility of native and germinated pigeon pea starches both in raw and cooked forms were investigated. The rate of digestibility (glucoamylase) was different for the two raw starches. There was a preferential utilization of low molecular weight ‘amyloses’ in the germinated samples, during in vitro enzymatic digestion of the raw granule. On acid hydrolyses, germinated samples produced lower molecular weight fragments than did raw starch, though the rate of reducing sugar released was not different. It may therefore be concluded that the size of the „amylopectin”︁ molecule is smaller in germinated starch. The easy availability of low molecular weight „amyloses”︁ may also influence the greater susceptibility of germinated starches to amylolysis even after cooking.     

Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering

A prerequisite to widespread deployment of condition-based maintenance (CBM) systems in industry is autonomous yet effective diagnostics and prognostics algorithms. The concept of ‘autonomy’ in the context of diagnostics and prognostics is usually based on unsupervised clustering techniques. This paper employs an unsupervised competitive learning algorithm to perform hidden Markov model (HMM) based clustering of multivariate temporal observation sequences derived from sensor signal(s). This method improves autonomy of the diagnostic engine over the more traditional classifier based diagnostics models. Classifier models, such as the model presented by Baruah and Chinnam [Baruah, P. and Chinnam, R.B., 2005. HMM for diagnostics and prognostics in machining processes. International Journal of Production Research, 43 (6), 1275–1293] employ ‘labelled’ feature vectors for supervised model building and subsequent health-state classification during condition monitoring. Improving the autonomy of the diagnostics model also improves the autonomy of the prognostics module that often builds upon information extracted through the diagnostics module. We have validated the proposed methods on a physical test-bed that involved monitoring drill-bits on a CNC machine outfitted with thrust-force and torque sensors. Experimental results demonstrate the ability of this method to estimate on-line the remaining-useful-life of a drill-bit with significant accuracy.