Random sequential multilayer deposition of different‐sized k mers on a one dimensional infinite substrate

Kinetics of random sequential, irreversible multilayer deposition of macromolecules of two different sizes on a one dimensional infinite lattice is analyzed at the mean field level. A formal solution for the corresponding rate equation is obtained. The Jamming limits and the distribution of gaps of exact sizes are discussed. In the absence of screening, the jamming limits are shown to be the same for all the layers. A detailed analysis for the components differing by one monomer unit is presented. The small and large time behaviors and the dependence of the individual jamming limits of the k mers and (k−1) mers on k and the rate parameters are analyzed.

DRIFTS studies on CO and NO adsorption and NO plus CO reaction over Pd2+-substituted CeO2 and Ce0.75Sn0.25O2 catalysts

Sequential adsorption of CO and NO as well as equimolar NO + CO reaction with variation of temperature over Pd2+ ion-substituted CeO2 and Ce0.75Sn0.25O2 supports has been studied by DRIFTS technique. The results are compared with 2 at.% Pd/Al2O3 containing Pd-0. Both linear and bridging Pd-0-CO bands are observed over 2 at.% Pd/Al2O3. But, band positions are shifted to higher frequencies in Ce0.98Pd0.02O2-delta and Ce0.73Sn0.25Pd0.02O2-delta catalysts that could be associated with Pd delta+-CO species. In contrast, a Pd2+-CO band at 2160 cm(-1) is observed upon CO adsorption over Ce0.98Pd0.02O2-delta and Ce0.73Sn0.25Pd0.02O2-delta catalysts pre-adsorbed with NO and a Pd+-CO band at 2120 cm(-1) is slowly developed on Ce(0.73)Srl(0.25)Pd(0.02)O(2-delta) over time. An intense linear Pd-0-NO band at 1750 cm(-1) found upon NO exposure to CO pre-adsorbed 2 at.% Pd/Al2O3 indicates molecular adsorption of NO. On the other hand, a weak Pd2+-NO band at 1850 cm(-1) is noticed after NO exposure to Ce0.98Pd0.02O2-delta catalyst pre-adsorbed with CO indicating dissociative adsorption of NO which is crucial for NO reduction. Pd-0-NO band is initially formed over CO pre-adsorbed Ce0.73Sn0.25Pd0.02O2-delta which is red-shifted over time along with formation of Pd2+-NO band. Several intense bands related to nitrates and nitrites are observed after exposure of NO to fresh as well as CO pre-adsorbed Ce0.98Pd0.02O2-delta and Ce0.73Sn0.25Pd0.02O2-delta catalysts. Ramping the temperature in a DRIFTS cell upon NO and CO adsorption shows the formation of N2O and NCO surface species, and N2O-formation temperature is comparable with the reaction done in a reactor.

Dual Drug Delivery Microcapsules via Layer-by-Layer Self-Assembly

The integration of hydrophobic and hydrophilic drugs in the polymer microcapsule offers the possibility of developing a new drug delivery system that combines the best features of these two distinct classes of material. Recently, we have reported the encapsulation of an uncharged water-insoluble drug in the polymer membrane. The hydrophobic drug is deposited using a layer-by-layer (LbL) technique, which is based on the sequential adsorption of oppositely charged polyelectrolytes onto a charged substrate. In this paper, we report the encapsulation of two different drugs, which are invariably different in structure and in their solubility in water. We have characterized these dual drug vehicular capsules by confocal laser scanning microscopy, atomic force microscopy, visible microscopy, and transmission electron microscopy. The growth of a thin film on a flat substrate by LbL was monitored by UV−vis spectra. The desorption kinetics of two drugs from the thin film was modeled by a second-order rate model.

LbL-coated microcapsules as systems for encapsulation of optical brightening agent

We report the encapsulation of optical brightening agent (OBA) into hollow microcapsules prepared by the controlled Layer- by-Layer (LbL) self-assembly process, achieved by the sequential adsorption of oppositely charged polyelectrolytes using negatively charged silica template. Loading takes place by spontaneous deposition method which was proved by confocal laser scanning microscopy (CLSM) using rhodamine 6G (Rd6G) as a fluorescent probe. The loading of the OBA into the microcapsules was found to be dependent on the feeding concentration, pH of the medium, and loading temperature. The encapsulation efficiency of OBA decreased on increasing feeding concentration. Maximum loading was observed at pH 4 and amount of OBA loaded decreased with increase in pH. The loaded OBA was released in a sustained manner for 8 h. No degradation of the OBA was observed during the process of encapsulation and release. Polyelectrolyte capsules potentially offer an innovative way of encapsulating large amounts of active materials for a variety of applications. (c) 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 127: 1609-1614, 2013

Optimal sequential wireless relay placement on a random lattice path

Our work is motivated by impromptu (or ``as-you-go'') deployment of wireless relay nodes along a path, a need that arises in many situations. In this paper, the path is modeled as starting at the origin (where there is the data sink, e.g., the control center), and evolving randomly over a lattice in the positive quadrant. A person walks along the path deploying relay nodes as he goes. At each step, the path can, randomly, either continue in the same direction or take a turn, or come to an end, at which point a data source (e.g., a sensor) has to be placed, that will send packets to the data sink. A decision has to be made at each step whether or not to place a wireless relay node. Assuming that the packet generation rate by the source is very low, and simple link-by-link scheduling, we consider the problem of sequential relay placement so as to minimize the expectation of an end-to-end cost metric (a linear combination of the sum of convex hop costs and the number of relays placed). This impromptu relay placement problem is formulated as a total cost Markov decision process. First, we derive the optimal policy in terms of an optimal placement set and show that this set is characterized by a boundary (with respect to the position of the last placed relay) beyond which it is optimal to place the next relay. Next, based on a simpler one-step-look-ahead characterization of the optimal policy, we propose an algorithm which is proved to converge to the optimal placement set in a finite number of steps and which is faster than value iteration. We show by simulations that the distance threshold based heuristic, usually assumed in the literature, is close to the optimal, provided that the threshold distance is carefully chosen. (C) 2014 Elsevier B.V. All rights reserved.

Behaviour of methylmethacrylate-acrylonitrile random copolymers in dilute solution

Studies on the dilute solution properties of methylmethacrylate-acrylonitrile random copolymers of three different compositions, 0.236, 0.5 and 0.74 mole fraction (m.f.) of acrylonitrile (AN) designated as MAa, MAb and MAc, respectively, have been made in good solvents and theta solvents. MAa has been studied in benzene (Bz) and ethylacetate (EAc). MAb in acetonitrile (MeCN), dimethyl sulphoxide (DMSO) and a binary solvent mixture of Bz and dimentyl formamide (DMF) in the volume ratio 6.5:1 designated as BM1 and MAc in MeCN, DMSO and Bz + DMF in the volume ratio 1.667:1 designated as BM2. The Mark-Houwink exponent ‘a’ reveals that Bz is a theta solvent for MAa at 20°C. For MAb and MAc, BM1 and BM2, respectively have ‘a’ values of 0.5 at all three temperatures studied (30°, 40° and 50°C). It is not clear whether they represent theta states or preferential adsorption plays a role complicating the behaviour in solution. The values of A2 are very low in MeCN considering that it is a very good solvent for the copolymer, ‘a’ values for MAb and MAc being 0.75 and 0.7, respectively.

Optimizing Delay in Sequential Change Detection on Ad Hoc Wireless Sensor Networks

We consider the classical problem of sequential detection of change in a distribution (from hypothesis 0 to hypothesis 1), where the fusion centre receives vectors of periodic measurements, with the measurements being i.i.d. over time and across the vector components, under each of the two hypotheses. In our problem, the sensor devices ("motes") that generate the measurements constitute an ad hoc wireless network. The motes contend using a random access protocol (such as CSMA/CA) to transmit their measurement packets to the fusion centre. The fusion centre waits for vectors of measurements to accumulate before taking decisions. We formulate the optimal detection problem, taking into account the network delay experienced by the vectors of measurements, and find that, under periodic sampling, the detection delay decouples into network delay and decision delay. We obtain a lower bound on the network delay, and propose a censoring scheme, where lagging sensors drop their delayed observations in order to mitigate network delay. We show that this scheme can achieve the lower bound. This approach is explored via simulation. We also use numerical evaluation and simulation to study issues such as: the optimal sampling rate for a given number of sensors, and the optimal number of sensors for a given measurement rate

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Macroporous three-dimensional graphene oxide foams for dye adsorption and antibacterial applications

Several reports illustrate the wide range applicability of graphene oxide (GO) in water remediation. However, a few layers of graphene oxide tend to aggregate under saline conditions thereby reducing its activity. The effects of aggregation can be minimized by having a random arrangement of GO layers in a three dimensional architecture. The current study emphasizes the potential benefits of highly porous, ultralight graphene oxide foams in environmental applications. These foams were prepared by a facile and cost effective lyophilization technique. The 3D architecture allowed the direct use of these foams in the removal of aqueous pollutants without any pretreatment such as ultrasonication. Due to its macroporous nature, the foams exhibited excellent adsorption abilities towards carcinogenic dyes such as rhodamine B (RB), malachite green (MG) and acriflavine (AF) with respective sorption capacities of 446, 321 and 228 mg g(-1) of foam. These foams were also further investigated for antibacterial activities against E. coli bacteria in aqueous and nutrient growth media. The random arrangement of GO layers in the porous foam architecture allowed it to exhibit excellent antibacterial activity even under physiological conditions by following the classical wrapping-perturbation mechanism. These results demonstrate the vast scope of GO foam in water remediation for both dye removal and antibacterial activity.

Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain and the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the Fe. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. We show via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.

Orientational relaxation in a random dipolar lattice: Wave-number and frequency dependence

In order to understand the role of translational modes in the orientational relaxation in dense dipolar liquids, we have carried out a computer ''experiment'' where a random dipolar lattice was generated by quenching only the translational motion of the molecules of an equilibrated dipolar liquid. The lattice so generated was orientationally disordered and positionally random. The detailed study of orientational relaxation in this random dipolar lattice revealed interesting differences from those of the corresponding dipolar liquid. In particular, we found that the relaxation of the collective orientational correlation functions at the intermediate wave numbers was markedly slower at the long times for the random lattice than that of the liquid. This verified the important role of the translational modes in this regime, as predicted recently by the molecular theories. The single-particle orientational correlation functions of the random lattice also decayed significantly slowly at long times, compared to those of the dipolar liquid.

Adsorption and Desorption Kinetics of Anionic Dyes on Doped Polyaniline

In this study, we report an approach for the adsorption and desorption of anionic (sulfonated) dyes from aqueous solution by doped polyaniline. In this study, we have synthesized PANI with two dopants, namely, p-toluenesulfonic acid (PTSA) and camphorsulfonic acid (CSA), and used these to adsorb various dyes. It was found that the doped PANI selectively adsorbs anionic dyes and does not adsorb cationic dyes. The adsorption of anionic dyes causes the variation in electrical conductivity of PANI, indicating its potential as a conductometric sensor for these dyes at very low concentration. The adsorbed dyes were desorbed from the polymer by using a basic aqueous solution. The adsorption and desorption kinetics of the dye in the presence of doped PANI were also determined.

Stability of a Random Diffusion with Linear Drift

We consider a Linear system with Markovian switching which is perturbed by Gaussian type noise, If the linear system is mean square stable then we show that under certain conditions the perturbed system is also stable, We also shaw that under certain conditions the linear system with Markovian switching can be stabilized by such noisy perturbation.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.