An approach to chiral amino acids via reductive amination of ketones: hydride reduction of 1-(S)-phenethyl amine derived Schiff bases of C-protected alpha-keto acids

Various 1-acyl-2,4,10-trioxaadamantanes were prepared from the corresponding 1-methoxycarbonyl derivatives, via conversion to the N-acylpiperidine derivatives followed by reaction with a Grignard reagent in refluxing THF. These alpha-keto orthoformates were converted to the corresponding imines with 1-(S)-phenethyl amine (TiCl4/Et3N/toluene/reflux), with the Schiff bases being reduced further with NaBH4 (MeOH/0 degrees C) into the corresponding 1-(S)-phenethyl amines (diastereomeric excess 91:9 by NMR). Hydrogenolysis of the phenethyl group (Pd-C/MeOH) finally led to the 1-(aminoalkyl)trioxaadamantanes, which are chiral C-protected alpha-amino acids, in excellent overall yields. (C) 2012 Elsevier Ltd. All rights reserved.

Premixed flame response to equivalence ratio fluctuations: Comparison between reduced order modeling and detailed computations

Combustion instability events in lean premixed combustion systems can cause spatio-temporal variations in unburnt mixture fuel/air ratio. This provides a driving mechanism for heat-release oscillations when they interact with the flame. Several Reduced Order Modelling (ROM) approaches to predict the characteristics of these oscillations have been developed in the past. The present paper compares results for flame describing function characteristics determined from a ROM approach based on the level-set method, with corresponding results from detailed, fully compressible reacting flow computations for the same two dimensional slot flame configuration. The comparison between these results is seen to be sensitive to small geometric differences in the shape of the nominally steady flame used in the two computations. When the results are corrected to account for these differences, describing function magnitudes are well predicted for frequencies lesser than and greater than a lower and upper cutoff respectively due to amplification of flame surface wrinkling by the convective Darrieus-Landau (DL) instability. However, good agreement in describing function phase predictions is seen as the ROM captures the transit time of wrinkles through the flame correctly. Also, good agreement is seen for both magnitude and phase of the flame response, for large forcing amplitudes, at frequencies where the DL instability has a minimal influence. Thus, the present ROM can predict flame response as long as the DL instability, caused by gas expansion at the flame front, does not significantly alter flame front perturbation amplitudes as they traverse the flame. (C) 2012 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Methodology for Pavement Design Reliability and Back Analysis Using Markov Chain Monte Carlo Simulation

Given the increasing cost of designing and building new highway pavements, reliability analysis has become vital to ensure that a given pavement performs as expected in the field. Recognizing the importance of failure analysis to safety, reliability, performance, and economy, back analysis has been employed in various engineering applications to evaluate the inherent uncertainties of the design and analysis. The probabilistic back analysis method formulated on Bayes' theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis-Hastings algorithm has proved to be highly efficient to address this issue. It is also quite flexible and is applicable to any type of prior information. In this paper, this method has been used to back-analyze the parameters that influence the pavement life and to consider the uncertainty of the mechanistic-empirical pavement design model. The load-induced pavement structural responses (e.g., stresses, strains, and deflections) used to predict the pavement life are estimated using the response surface methodology model developed based on the results of linear elastic analysis. The failure criteria adopted for the analysis were based on the factor of safety (FOS), and the study was carried out for different sample sizes and jumping distributions to estimate the most robust posterior statistics. From the posterior statistics of the case considered, it was observed that after approximately 150 million standard axle load repetitions, the mean values of the pavement properties decrease as expected, with a significant decrease in the values of the elastic moduli of the expected layers. An analysis of the posterior statistics indicated that the parameters that contribute significantly to the pavement failure were the moduli of the base and surface layer, which is consistent with the findings from other studies. After the back analysis, the base modulus parameters show a significant decrease of 15.8% and the surface layer modulus a decrease of 3.12% in the mean value. The usefulness of the back analysis methodology is further highlighted by estimating the design parameters for specified values of the factor of safety. The analysis revealed that for the pavement section considered, a reliability of 89% and 94% can be achieved by adopting FOS values of 1.5 and 2, respectively. The methodology proposed can therefore be effectively used to identify the parameters that are critical to pavement failure in the design of pavements for specified levels of reliability. DOI: 10.1061/(ASCE)TE.1943-5436.0000455. (C) 2013 American Society of Civil Engineers.

Sub-band envelope approach to obtain instants of significant excitation in speech

In this paper, we propose a new sub-band approach to estimate the glottal activity. The method is based on the spectral harmonicity and the sub-band temporal properties of voiced speech. We propose a method to represent glottal excitation signal using sub-band temporal envelope. Instants of maximum glottal excitation or Glottal Closure Instants (GCI) are extracted from the estimated glottal excitation pattern and the result is compared with a standard GCI computation method, DYPSA [1]. The performance of the algorithm is also compared for the noisy signal and it is shown that the proposed method is less variant to GCI estimation under noisy conditions compared to DYPSA. The algorithm is evaluated on the CMU-ARCTIC database.

Photoelectrochemical oxidation of p-nitrophenol on an expanded graphite-TiO2 electrode

In the quest for more efficient photoanodes in the photoelectrochemical oxidation processes for organic pollutant degradation and mineralisation in water treatment, we present the synthesis, characterisation and photoelectrochemical application of expanded graphite-TiO2 composite (EG-TiO2) prepared using the sol-gel method with organically modified silicate. The Brunauer-Emmett-Teller surface area analyser, ultraviolet-visible diffuse reflectance, scanning electron microscopy, energy dispersive spectroscopy, X-ray diffractometry, Raman spectrometry and X-ray photoelectron spectroscopy were employed for the characterisation of the composites. The applicability of the EG-TiO2 as photoanode material was investigated by the photoelectrochemical degradation of p-nitrophenol as a target pollutant in a 0.1 M Na2SO4 (pH 7) solution at a current density of 5 mA cm(-2). After optimising the TiO2 loading, initial p-nitrophenol concentration, pH and current density, a removal efficiency of 62% with an apparent kinetic rate constant of 10.4 x 10(-3) min(-1) was obtained for the photoelectrochemical process as compared to electrochemical oxidation and photolysis, where removal efficiencies of 6% and 24% were obtained respectively after 90 min. Furthermore, the EG-TiO2 electrode was able to withstand high current density due to its high stability. The EG-TiO2 electrode was also used to degrade 0.3 x 10(-4) M methylene blue and 0.1 x 10(-4) M Eosin Yellowish, leading to 94% and 47% removal efficiency within 120 reaction time. This confirms the suitability of the EG-TiO2 electrode to degrade other organic pollutants.

CAVVA: Computational Affective Video-in-Video Advertising

Advertising is ubiquitous in the online community and more so in the ever-growing and popular online video delivery websites (e. g., YouTube). Video advertising is becoming increasingly popular on these websites. In addition to the existing pre-roll/post-roll advertising and contextual advertising, this paper proposes an in-stream video advertising strategy-Computational Affective Video-in-Video Advertising (CAVVA). Humans being emotional creatures are driven by emotions as well as rational thought. We believe that emotions play a major role in influencing the buying behavior of users and hence propose a video advertising strategy which takes into account the emotional impact of the videos as well as advertisements. Given a video and a set of advertisements, we identify candidate advertisement insertion points (step 1) and also identify the suitable advertisements (step 2) according to theories from marketing and consumer psychology. We formulate this two part problem as a single optimization function in a non-linear 0-1 integer programming framework and provide a genetic algorithm based solution. We evaluate CAVVA using a subjective user-study and eye-tracking experiment. Through these experiments, we demonstrate that CAVVA achieves a good balance between the following seemingly conflicting goals of (a) minimizing the user disturbance because of advertisement insertion while (b) enhancing the user engagement with the advertising content. We compare our method with existing advertising strategies and show that CAVVA can enhance the user's experience and also help increase the monetization potential of the advertising content.

A NEAR OPTIMAL PROJECTION FOR SPARSE REPRESENTATION BASED CLASSIFICATION

Sparse representation based classification (SRC) is one of the most successful methods that has been developed in recent times for face recognition. Optimal projection for Sparse representation based classification (OPSRC)1] provides a dimensionality reduction map that is supposed to give optimum performance for SRC framework. However, the computational complexity involved in this method is too high. Here, we propose a new projection technique using the data scatter matrix which is computationally superior to the optimal projection method with comparable classification accuracy with respect OPSRC. The performance of the proposed approach is benchmarked with various publicly available face database.

Combinatorial Exploration of the Structural Landscape of Acid-Pyridine Cocrystals

The structural landscape of acid-pyridine cocrystals is explored by adopting a combinatorial matrix method with 4-substituted benzoic acids and 4-substituted pyridines. The choice of the system restricts the primary synthon to the robust acid-pyridine entity. This methodology accordingly provides hints toward the formation of secondary synthons. The pK(a) rule is validated in the landscape by taking all components of the matrix together and exploring it as a whole. Along with the global features, the exploration of landscapes reveals some local features. Apart from the identification of secondary synthons, it also sheds light on the propensity of hydration in cocrystals, synthon competition, and certain topological similarities. The method described here combines two approaches, namely, database analysis and high throughput crystallography, to extract more information with minimal extra experimental effort.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.

1,9-Pyrazoloanthrone as a Colorimetric and ``Turn-On'' Fluorometric Chemosensor: Structural Implications

A colorimetric and ``turn-on'' fluorescent chemosensor based on 1,9-pyrazoloanthrone specifically for cyanide and fluoride ion detection shows a remarkable solid state reaction when crystals of tetrabutylammonium cyanide and fluoride are brought in physical contact with 1,9-pyrazoloanthrone. X-ray crystal structures of 1,9-pyrazoloanthrone and complexes have been determined, and the ion sensing activity (detection limit of 0.2 and 2 ppb) has been inferred based on spectroscopic and structural features.

Conducting polymer-carbon black nanocomposite sensor for volatile organic compounds and correlating sensor response by molecular dynamics

Volatile organic compounds (VOCs) are present in our every day used products such as plastics, cosmetics, air fresheners, paint, etc. The determination of amount of VOC present in atmosphere can be carried out via various sensors. In this work a nanocomposite of a novel thiophene based conducting polymer and carbon black is used as a volatile organic compound sensor. The fabricated 2 lead chemiresistor sensor was tested for vapours of toluene, acetone, cylcohexane, and carbon tetrachloride. The sensor responds to all the vapours, however, exhibit maximum response to toluene vapours. The sensor was evaluated for various concentrations of toluene. The lower limit of detection of the sensor is 15 +/- 10 ppm. The study of the effect of humidity on senor response to toluene showed that the response decreases at higher humidity conditions. The surface morphology of the nanocomposite was characterized by scanning electron microscopy. Diffuse reflectance spectroscopy was used to investigate the absorption of vapours by the nanocomposite film. Contact angle measurements were used to present the effect of water vapour on the toluene response of nanocomposite film. Solubility parameter of the conducting polymer is predicted by molecular dynamics. The sensing behaviour of the conducting polymer is correlated with solubility parameter of the polymer. Dispersion interaction of conducting polymer with toluene is believed to be the reason for the selective response towards toluene. (C) 2014 Elsevier B.V. All rights reserved.

STM verification of the reduction of the Young's modulus of CdS nanoparticles at smaller sizes

We demonstrate the first STM evaluation of the Young's modulus (E) of nanoparticles (NPs) of different sizes. The sample deformation induced by tip-sample interaction has been determined using current-distance (I-Z) spectroscopy. As a result of tip-sample interaction, and the induced surface deformations, the I-z curves deviates from pure exponential dependence. Normally, in order to analyze the deformation quantitatively, the tip radius must be known. We show, that this necessity is eliminated by measuring the deformation on a substrate with a known Young's modulus (Au(111)) and estimating the tip radius, and afterwards, using the same tip (with a known radius) to measure the (unknown) Young's modulus of another sample (nanoparticles of CdS). The Young's modulus values found for 3 NP's samples of average diameters of 3.7, 6 and 7.5 nm, were E similar to 73%, 78% and 88% of the bulk value, respectively. These results are in a good agreement with the theoretically predicted reduction of the Young's modulus due to the changes in hydrostatic stresses which resulted from surface tension in nanoparticles with different sizes. Our calculation using third order elastic constants gives a reduction of E which scales linearly with 1/r (r is the NP's radius). This demonstrates the applicability of scanning tunneling spectroscopy for local mechanical characterization of nanoobjects. The method does not include a direct measurement of the tip-sample force but is rather based on the study of the relative elastic response. (C) 2014 Elsevier B.V. All rights reserved.

Effect of Line Defects on the Electrical Transport Properties of Monolayer MoS2 Sheet

We present a computational study on the impact of line defects on the electronic properties of monolayer MoS2. Four different kinds of line defects with Mo and S as the bridging atoms, consistent with recent theoretical and experimental observations, are considered herein. We employ the density functional tight-binding (DFTB) method with a Slater-Koster-type DFTB-CP2K basis set for evaluating the material properties of perfect and the various defective MoS2 sheets. The transmission spectra are computed with a DFTB-non-equilibrium Green's function formalism. We also perform a detailed analysis of the carrier transmission pathways under a small bias and investigate the phase of the transmission eigenstates of the defective MoS2 sheets. Our simulations show a two to four fold decrease in carrier conductance of MoS2 sheets in the presence of line defects as compared to that for the perfect sheet.

Mode coupling theory analysis of electrolyte solutions: Time dependent diffusion, intermediate scattering function, and ion solvation dynamics

A self-consistent mode coupling theory (MCT) with microscopic inputs of equilibrium pair correlation functions is developed to analyze electrolyte dynamics. We apply the theory to calculate concentration dependence of (i) time dependent ion diffusion, (ii) intermediate scattering function of the constituent ions, and (iii) ion solvation dynamics in electrolyte solution. Brownian dynamics with implicit water molecules and molecular dynamics method with explicit water are used to check the theoretical predictions. The time dependence of ionic self-diffusion coefficient and the corresponding intermediate scattering function evaluated from our MCT approach show quantitative agreement with early experimental and present Brownian dynamic simulation results. With increasing concentration, the dispersion of electrolyte friction is found to occur at increasingly higher frequency, due to the faster relaxation of the ion atmosphere. The wave number dependence of intermediate scattering function, F(k, t), exhibits markedly different relaxation dynamics at different length scales. At small wave numbers, we find the emergence of a step-like relaxation, indicating the presence of both fast and slow time scales in the system. Such behavior allows an intriguing analogy with temperature dependent relaxation dynamics of supercooled liquids. We find that solvation dynamics of a tagged ion exhibits a power law decay at long times-the decay can also be fitted to a stretched exponential form. The emergence of the power law in solvation dynamics has been tested by carrying out long Brownian dynamics simulations with varying ionic concentrations. The solvation time correlation and ion-ion intermediate scattering function indeed exhibit highly interesting, non-trivial dynamical behavior at intermediate to longer times that require further experimental and theoretical studies. (c) 2015 AIP Publishing LLC.