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International audience; Hybrid aluminosilicate nanotubes (Imo-CH3) have the ability to trap small organic molecules inside their hydrophobic internal cavity while being dispersed in water owing to their hydrophilic external surface. They also display a curvature-induced polarization of their wall, which favors reduction outside the nanotubes and oxidation inside. Here, we coupled bare plasmonic gold nanoparticles (GNPs) with Imo-CH3 and analyzed for the first time the redox reactivity of these hybrid nano-reactors upon UV illumination. We show that the coupling between GNPs and Imo-CH3 significantly enhances the nanotube photocatalytic activity, with a large part of water reduction occurring directly on the gold surface. The coupling mechanism strongly influences the initial H2 production rate, which can go from ×10 to more than ×90 as compared to bare Imo-CH3 depending on the synthesis route of the GNPs. The present results show that this hybrid photocatalytic nano-reactor benefits from a synergy of polarization and confinement effects that facilitate efficient H2 production.

Previous pandemics in 1918, 1957, 1968, and the ongoing COVID-19 pandemic have provided sufficient evidence of health concerns caused by influenza pandemics. The existing health care system is overwhelmed by the surging demand of susceptible and infected individuals due to the COVID-19 outbreak. It is crucial to identify and isolate infected individuals to prevent pandemic spread. Thus, a mixed-integer linear programming model is proposed in this study for the location-allocation of health care facility networks (i.e., temporary testing laboratories). The objective of this study is to ensure that test samples from various geographical locations reach testing laboratories as soon as possible and at minimum cost to ensure timely testing. Hence, the proposed model has two objectives: (i) minimization of the total cost and (ii) minimization of the maximum travel time from a patient node to a testing facility. Furthermore, to prevent capacity underutilization, the capacity of temporary testing laboratories is tailored in the model. A case study in Maharashtra, India, is used to demonstrate the real-life applicability of the proposed model. The study results has interesting implications for decision- and policy-makers.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

International audience; Photocatalytic production of fuels, even in small quantities, from the mineralization of hazardous pollutants, is a promising and renewable way of recycling wastewater. In the present work, the potential of methyl functionalized inorganic aluminosilicate nanotubes (methyl imogolite/Imo-CH3) as photocatalytic nanoreactors for this application is demonstrated. Using the phototoxic polycyclic aromatic hydrocarbon dibenzo(a,h)anthracene (DBAN) as a model pollutant, we show that DBAN molecules can be efficiently trapped inside Imo-CH3 nanotubes from an aqueous medium to undergo subsequent oxidative photo-degradation under UV light. The kinetics of this photo-degradation were shown to depend strongly on both the initial DBAN concentration in the nanotubes and the presence/absence of dissolved dioxygen. The photo-degradation process followed a complex mechanistic pathway, consisting of combined photo-oxidation and photo-cycloaddition reactions, where detection of carbon dioxide (CO2) as a photo-oxidation product confirmed the mineralization of encapsulated DBAN. CO and CH4 molecules were also formed, however these could arise from the further photo-reduction of CO2 on the external surface of the nanotubes. Moreover, dihydrogen (H2) was produced upon UV illumination under anaerobic conditions due to water reduction reactions on the external surfaces of Imo-CH3 nanotubes. The possible mechanistic pathways of these processes are proposed, and the dual capability of Imo-CH3 nanotubes for simultaneous pollutant degradation and H2 production is then demonstrated – a rare feat for a single photocatalyst material.

Network motifs are recurrent and over-represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)-complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state-of-art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.

Inspired by a natural nano-mineral known as imogolite, aluminosilicate inorganic nanotubes are appealing systems for photocatalysis. Here, we studied two types of synthetic imogolites: one is completely hydrophilic (IMO-OH), while the other has a hydrophilic exterior and a hydrophobic interior (IMO-CH3), enabling the encapsulation of organic molecules. We combined UV-Vis diffuse reflectance spectroscopy of imogolite powders and X-ray photoelectron spectroscopy of deposited imogolite films and isolated nanotubes agglomerates to obtain not only the band structure, but also the quantitative intra-wall polarization of both synthetic imogolites for the first time. The potential difference across the imogolite wall was determined to be 0.7 V for IMO-OH and around 0.2 V for IMO-CH3. The high curvature of the nanotubes, together with the thinness of their wall, favors efficient spontaneous charge separation and electron exchange reactions on both the internal and external nanotube surfaces. In addition, the positions of their valence and conduction band edges make them interesting candidates for co-catalysts or doped catalysts for water splitting, among other possible photocatalytic reactions relevant to energy and the environment.

PurposeThe purpose of this paper is to examine the influence of culture on organizational readiness to change (ORC) within the context of merger and acquisition (M&A) in the banking sector in India.Design/methodology/approachA multisource approach is used to collect data from a public-sector bank in India for testing our hypothesis. A hierarchical approach based on higher-order modelling has been deployed for confirming the path model. The foundation of the study is based on power distance (PD) and uncertainty avoidance (UA) cultural dimensions of Hofstede (1984).FindingsEmployees in organizations with large PD and high UA index exhibit low readiness to change. Findings support a negative relationship of culture (large PD and high UA) with organizational readiness to change at the individual level.Research limitations/implicationsThe study has three major implications. First, measures and importance of change readiness at the individual level during corporate events such as M&A is elucidated in the study. Second, a paradigm for assessing higher-order models grounded in theoretical and methodological rigour for testing our hypothesis is presented in the paper. Last, the role of culture in M&A processes is highlightedvis-à-visfactors related to PD and UA on ORC.Practical implicationsThe findings of the research answer to the call for a study on factors that help in creating a synergy for successful M&A across all sectors especially in the banking sector. People representing high UA and large PD often look forward to direction and guidelines for guiding employee actions. Leaders therefore need to set clear agenda and effectively communicate the appropriateness of change to their employees for developing positive behaviour towards desirable organizational outcomes. This study touches upon this important perspective for its practical utilization.Originality/valueThe study adds to the limited literature on change which addresses the need for studying socio-cultural factors in the M&A process, especially in an emerging economies context.

Brand love is a phenomenon that is experienced by a group of satisfied consumers. The construct brand love is of great importance to academics and practitioners because a group of satisfied custome...

The biological network plays a key role in protein function annotation, protein superfamily classification, disease diagnosis, etc. These networks exhibit global properties like small-world property, power-law degree distribution, hierarchical modularity, robustness, etc. Along with these, the biological network also possesses some local properties like clustering and network motif. Network motifs are recurrent and statistically over-represented subgraphs in a target network. Operation of a biological network is controlled by these motifs, and they are responsible for many biological applications. Discovery of network motifs is a computationally hard problem and involves a subgraph isomorphism check which is NP-complete. In recent years, researchers have developed various tools and algorithms to detect network motifs efficiently. However, it is still a challenging task to discover the network motif within a practical time bound for the large motif. In this study, an efficient pattern-join based algorithm is proposed to discover network motif in biological networks. The performance of the proposed algorithm is evaluated on the transcription regulatory network of Escherichia coli and the protein interaction network of Saccharomyces cerevisiae. The running time of the proposed algorithm outperforms most of the existing algorithms to discover large motifs.

Using the 90–105 keV gamma-rays for determining Pu isotopic composition is studied for dilute Pu solutions (0.0001–0.05 μg/mm3) as well as Pu-U mixed solutions. It is shown that for concentrations higher than 0.001 μg/mm3 Pu, results match well with those of mass spectrometric results. However, in mixed solutions, beyond 0.005 mg/mm3 U concentration, the errors on isotopic compositions of Pu increased as U content increased.

Background: An asymmetric fission was reported by Andreyev et al. in \ensuremath{\beta}-delayed fission of $^{180}\mathrm{Tl}$ [Phys. Rev. Lett. 105, 252502 (2010)]. Subsequent theoretical calculations suggested that the asymmetric nature of the mass distribution is not restricted to the $^{180}\mathrm{Hg}$ only but is also expected for many other nuclei in the mass region $A\ensuremath{\approx}180$. Thus, it is important to investigate fission fragment mass distribution for different fissioning systems over a wide range of excitation energy in the mass region $A\ensuremath{\approx}180$.Purpose: Present measurements have been carried out to study the nature of the fission fragment mass distribution in the $^{32}\mathrm{S}+^{144}\mathrm{Sm}\ensuremath{\rightarrow}^{176}\mathrm{Pt}$ reaction in the compound nucleus excitation energy range of 38.7--47.5 MeV and investigate the role of multimodal fission.Method: Mass distributions have been determined from the time of flight (TOF) of the fission fragments, which was measured with respect to the beam pulse. Two multiwire proportional counters were placed at the folding angle to detect the fission fragments. Measured TOF of the fission fragments was used to obtain their velocities, which were further used to obtain the fission fragment mass distribution.Results: The fission fragment mass distributions at all three beam energies were observed to have flattop natures, which could not be fitted well by a one-Gaussian function. A fit using a two-Gaussian function significantly improved the ${\ensuremath{\chi}}^{2}$ values. The ratio of the most probable heavy to light fragment mass (${A}_{\mathrm{H}}/{A}_{\mathrm{L}}$) was observed as $\ensuremath{\approx}99.4/76.6$. A systematic study of the centroid values of asymmetric peaks in the mass distribution for different fissioning systems around mass region $A\ensuremath{\approx}180$ showed that heavier mass peak is centered around ${A}_{\mathrm{H}}\ensuremath{\approx}100$. Further analysis of ${A}_{\mathrm{H}}$ and ${A}_{\mathrm{L}}$ to obtain the corresponding neutron and proton numbers gave heavy fragment neutron number (${N}_{\mathrm{H}}$) around $\ensuremath{\approx}56$ and light fragment proton number (${Z}_{\mathrm{L}}$) in the range of $\ensuremath{\approx}34--36$.Conclusions: Observation of the flattop nature indicated the contributions from multimodal fission having both symmetric and asymmetric fission components, similar to those reported earlier in the mass region $A\ensuremath{\approx}180$, although it did not show a clear dip in symmetry as observed in some of the studies. The observed values of the neutron number in the heavy fragment (${N}_{\mathrm{H}}$) and proton number in the light fragment (${Z}_{\mathrm{L}}$) are consistent with the values recently proposed in Phys. Rev. C 100, 041602(R) (2019) and arXiv:2007.16184] for fissioning systems in the similar mass range. Comparison of the data on the width of the mass distribution from the present paper with those from similar fissioning systems with different ${Z}_{P}{Z}_{T}$ values suggested that the observed width of the mass distribution has a dependence on the neutron-proton configuration along with the entrance channel dynamics.

Galvanic replacement between metals has received notable research interest for the synthesis of heterometallic nanostructures. The growth pattern of the nanostructures depends on several factors such as extent of lattice mismatch, adhesive interaction between the metals, cohesive forces of the individual metals, etc. Due to the difficulties in probing ultrafast kinetics of the galvanic replacement reaction and particle growth in solution, real-time mechanistic investigations are often limited. As a result, the growth mechanism of one metal on the surface of another metal at the nanoscale is poorly understood so far. In the present work, we could successfully probe the galvanic replacement of silver ions with nickel nanoparticles, stabilized in a polymer membrane, using two complementary methods, namely, small-angle X-ray scattering (SAXS) and radiolabeling, and the results are supported by density functional theory (DFT) computations. The silver-nickel system has been chosen for the present investigation because of the high degree of bulk immiscibility caused by the large lattice mismatch (15.9%) and the weak adhesive interaction, which makes it a perfect model system for immiscible metal pairs. Membrane, as a host medium, plays a crucial role in retarding the kinetics of atomic and particle rearrangements (nucleation and growth) due to slower mobility of the atoms (monomers) and particles within the polymer network. This allowed us to examine the real-time concentration of silver monomers during galvanic replacement of silver ions with nickel nanoparticles and evolution of Ni/Ag nanoparticles. From combined experiment and DFT computations, it has been demonstrated, for the first time to the best of our knowledge, that the majority of silver atoms, which are produced on the nickel nanoparticle surface by galvanic reactions, do not form traditional core-shell nanostructures with nickel and undergo a self-governing sequential nucleation and growth of silver nanoparticles via formation of intermediate prenucleation silver clusters, leading to the formation of mixed metallic nanoparticles in the membrane. The surface of NiNPs has a heterogeneous effect on the silver nucleation pathway, which is evident from the reduced critical free energy barrier of nucleation (Δ

Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.

We report the H I bond activation on free and confined silver tetramer clusters using the first principles approach. The objective of this study is to underscore the effect of confinement on the chemical reactivity of Ag4. All calculations were carried out under the density functional theory formalism using both atomic and plane wave basis sets. We have used armchair carbon nanotube as a model system for confinement. The results show that the interaction energy is increased inside nanotube, leading to enhanced bond stretching. The origin of such effect has been demonstrated using molecular orbital theory and electronic density of state analysis.

This paper presents a standardless non-destructive method for simultaneous assay of uranium and plutonium in mixed samples relevant to nuclear safeguards, forensics and fuel cycle. The method is based on an in-situ absolute efficiency calibration of a γ-ray detector using plutonium γ-rays that can subsequently be used for quantification of uranium in the sample. The method was tested by assaying U–Pu samples with known amounts of U and Pu with varying mass, geometry, composition, reactor type, age and fissile isotope enrichment.

This study presents a method for estimating e-waste at the micro-level (ward-village level) using a real-life case of Delhi, India and an example of mobile phones. The study is motivated by the practical issues faced by the e-waste reverse logistics (RL) network, which requires micro-level estimation for various activities such as facility location, routing of vehicles, resource allocation, and capacity determination. Taking input as population, and product penetration, due to the unavailability of sales data at the micro-level, we propose an e-waste estimation approach based on the sales-stock-lifespan model. Considering product diffusion follows an 'S' shaped curve, the time-series expansion is modelled using the Gompertz curve and percentage change in mobile subscribers. We show that the waste from obsolete mobile phones increased exponentially from 2004 till 2019, i.e., 1147 in 2004–856483 in 2019, and is expected to be stable with less than 1% growth rate till 2030. We also show that Delhi will discard approximately 858611 mobile phones in the year 2020, and the top 5 e-waste generating wards-villages of Delhi will produce 13089, 6502, 5901, 5863, and 5742 obsolete mobile phones in 2020. The results of the study are of use for designing and operating the RL network.

Questions which are syntactically different, yet having the same intent, give a poor encounter to both the writer of an answer as well as the individual who searches for the answer in social Q&A online platforms such as Quora, Yahoo Answers, and StackOverflow. In order to maintain a rich and diverse database of answers, ensuring the uniqueness of every question on such Q&A platforms is of utmost necessity. The objective of this proposed work is to eliminate redundancy/duplicacy of userentered questions so as to increase the relevance of the answer(s) provided to semantically similar questions. This problem is a closed challenge taken from Kaggle, an online platform to learn and compete in data science challenges. The dataset worked upon in this paper has been obtained from Kaggle and LSTM with Euclidean Distance outperform other algorithms with log loss of 0.14.

In the present work, the algebraic reconstruction technique (ART) combined with Monte Carlo method has been proposed for image reconstruction in gamma ray transmission tomography, which is generally used for the nondestructive assay of special nuclear materials. The Monte Carlo method has been explored to generate the weight matrix, which is required for executing ART algorithm. The method has been successfully demonstrated using a two-dimensional tomographic set-up with circular sample. The advantage of the method is that it is generalized, with no assumptions regarding weight matrices and is easily extendable to three dimensional systems, where other classical approaches become cumbersome. Moreover, the method is computationally simple and less time consuming unlike full scale Monte Carlo simulations.

The rapid global spread of COVID-19 has caused disruptions in various supply chains and people's lives. At the same time, it has paved the way for drone technology (Aerial bots). With the countries gone into lockdown for an unspecified time, it is self-evident that people will run out of food, medicine, and other essentials because of the middleman's unavailability to move products from supply to demand point. Lack of medical infrastructure and distant testing laboratories is another challenge faced by the countries, which result in a delayed testing report leading to delay in medical treatment—such critical problems arising in the fight against COVID-19 highlight the need for improving the efficiency of supply chains. Recently used for commercial purposes, drone technology has already proved its utility in inventory and logistics management. Therefore, we argue that drones could be a viable option to improve the efficiency and effectiveness of the supply chains working for humanitarian aid to combat COVID-19. Specifically, the focus is on food, administrative, and healthcare supply chains that are the core to combat the pandemic. Moreover, in this article, we highlight various present and future application areas for drone technology, which could pave the way for future research and industry applications.

A catalytic nanoreactor with remarkable activity has been prepared by in-situ synthesis of colloidal silver nanoparticles of different sizes in a tailor made pore filled anion exchange membrane (AEM). Time resolved small angle X-ray scattering (SAXS) and field-emission scanning electron microscopy (FE-SEM) probe the evolution of size distribution of the nanoparticles during the course of synthesis. Borohydride reduction of para -nitrophenol to para -aminophenol has been used as a model reaction for catalysis throughout the study. Role of nanoparticle size vis-a-vis the role of nanoscale confinement on catalytic reaction has been investigated. It has been demonstrated that the overall catalytic performance of the silver nanoparticles is improved significantly in the nanoconfined environment as compared to that of the bare nanoparticles. The origin of such improved catalytic activity of spatially confined silver nanoparticles has been attributed to the effect of “nanoconfinement” that seems to accelerate the catalytic reduction. It has been shown that the catalytic rate constant is strongly dependent on the average occupancy of the para -nitrophenol molecule per pore when it is significantly less than one. This extraordinary effect may be attributed to the compartmentalization of the reactant molecules in nanoscale subvolumes, which essentially favours the forward reaction because of reduced molecular mixing of reactant with product.

Modification in optical responses of plasmonic metal nanoparticles by galvanic replacement reactions with metal ion contaminants has underpinned the development of Localised Surface Plasmon Resonance (LSPR) based sensors during past decade. However, the studies reported so far mostly use nanoparticles in the form of colloidal solution with inadequate understanding on the sensing mechanisms. We herein revisit the galvanic reaction between silver nanoparticles and Hg2+ ions with aims to develop a cellulose based optical test strip for selective assay of Hg2+ ions in aqueous samples as well as to comprehend the mechanisms of LSPR modification of AgNPs in interaction with Hg. The contact of cellulose membrane hosted AgNPs with Hg2+ ions follows classical galvanic reaction and a linear correlation between the measured absorbance of AgNPs with Hg2+ concentration has been found, which formed the basis of the quantitative assay. The performance of the sensor test strip has been verified for three real water samples of environmental and domestic origin. The results obtained are within 1–6 % accuracy with 94–106 % recovery. The detection limit of the present test strip has been found to be 0.3 ppb, which is lower than a large number of Hg2+ sensor developed so far. Using combined small angle X-ray scattering, calculated extinction coefficient and electronic structure calculations of Ag-Hg system by density functional theory computations, it has been demonstrated that at lower mercury concentration (below 1 ppm), the AgNPs undergo coalescence to form bigger nanoparticles. However at higher concentration (1 ppm and above), the Hg atoms undergo an exceptional “coat to droplet” conversion on AgNP surface. The associated changes in LSPR of AgNPs have been explained using these models.

Size controlled synthesis of nanoparticles in a structured media, such as a membrane, has not yet been achieved successfully in comparison to that in solution due to the lack of mechanistic investigations on the nucleation and growth of nanoparticles in these media. Slower diffusion of precursor and monomer species inside these structured media complicates the nanoparticle formation mechanism. We herein report a novel experimental approach to reveal the mechanism of nucleation and growth during the synthesis of silver nanoparticles in a Nafion-117 membrane using radiolabeling and small angle X-ray scattering (SAXS). The study has been conducted under the conditions of continuous supply of precursor (silver citrate). Repetitive “LaMer type” nucleations have been found to occur in the membrane leading to the formation of polydispersed spherical nanoparticles as evident from time resolved small angle X-ray scattering. These repetitive nucleations have been shown to be responsible for continuous birth of new seeds, which grow to larger particles, mainly by random coagulation introducing non-uniformity in the growth profile of nanoparticles. The additional nucleation events have been successfully ceased by careful tuning of reaction temperature and precursor concentration, thereby eliminating the nanoparticle growth by random coagulation. This has led to the formation of silver nanoparticles with improved morphology and size distributions, which has been manifested in remarkable improvement in the optical quality of the silver nanoparticles. The present study is the first of its kind showing the crucial role of the membrane host in retarding the reaction kinetics which allowed successful probing of temporal variation of monomer concentration during nucleation and growth using a radiotracer. This was hitherto difficult to probe in solution due to its ultrafast kinetics. Additionally, using the experimental monomer concentrations during nucleation, the free energy of activation (ΔGcrit) and the critical radius (rcrit) for nucleation have been estimated and found to be 73 kJ mol−1 and 6.6 A, respectively. The present work validates the well known theoretical model by La Mer for the synthesis of nanoparticles in a membrane under continuous precursor supply.

Segmented gamma-ray scanning (SGS) is a traditional practice, globally, for the non-destructive assay of special nuclear materials (SNMs) in large volume radioactive waste drums. The conventional SGS is a relative two pass method and requires a standard drum of identical geometry. The present work is focused on identifying the limitations of traditional segmented gamma scanning methodology for the assay of waste drums containing plutonium lumps. It has been observed that, for drums containing Pu lumps, the conventional SGS methodology severely underestimates the assay results (~ 2–6 times depending on the gamma-ray energy) due to attenuation under-correction. An alternate single pass absolute efficiency approach following the principle of infinite energy extrapolation of apparent mass has been proposed for the assay of waste drums containing Pu lumps in various random and biased spatial distributions and has been found to agree within 1–10% with the actual value with a maximum uncertainty of 8%. The method has been further validated at higher collimator widths and it has been demonstrated that an increase in collimator width from 5.1 to 10.3 cm increases the throughput of the present system without much of losing the accuracy.

Large volume radioactive waste drums with low/intermediate level of alpha activity, generated in radiochemical laboratories, are in general screened for special nuclear materials (SNM) in a segmented gamma scanner (SGS) before disposal. The assay methodology traditionally requires a standard drum of identical geometry and thereby making the procedure relying on the availability of a true standard, which is often difficult to organize. Here, we report a non-conventional absolute segmented gamma scanning (ASGS) methodology for the assay of 200 L waste drums, avoiding the use of a standard drum. The present analysis employ the full energy peak (FEP) efficiency, ingeniously determined using a standard 152Eu point source. From combined experiment and Monte Carlo simulation, it has been established that, the FEP efficiencies of the detector for a 200 L cylindrical sample can be well reproduced using a point source. While verifying the applicability of the point source FEP efficiencies for the assay of plutonium in 200 L drums, an energy dependent bias has been seen, which confirms the presence of lump attenuation in addition to the general matrix attenuation. An infinite energy extrapolation of apparent mass approach has been adopted for the assay of large volume waste drums which takes care of the gamma-ray attenuation from all sources that is otherwise difficult to correct for in a sample drum of unknown history.

We report the preparation of an optical test strip for quantitative assay of hydrogen peroxide (H2O2) in aqueous solution. Silver nanoparticles(AgNPs) with good optical quality are synthesized by in-situ reduction of silver ions in Nafion-117 membrane previously absorbed with ascorbate ion (HA(-)). The nanocomposite membrane exhibits a narrow Localized Surface Plasmon Resonance (LSPR) band at 413 nm. The extent of decrease in intensity of the LSPR band in presence of H2O2 solution gives quantitative estimate of H2O2 concentration. The detector has been found to show a good analytical response towards H2O2 detection at pH 7 over a wide concentration range. The detection limit has been calculated to be 2.6 x 10(-8) mol L-1, which is lower than the conventional enzyme based biosensors. The present test strip has been succesfully employed for the quantitative assay of H2O2 in milk. The mechanism of change in LSPR in presence of H2O2 (1.0 x 10(-3) mol L-1) has been explored for the first time using time resolved Small-Angle X-ray Scattering and radiotracer techniques. The results show that at this concentration of H2O2, its sharp oxidative sawing action is responsible for cutting off the nanoparticles into smaller fragments resulting in rapid reduction of absorption crossection. (C) 2017 Elsevier B.V. All rights reserved.

Music Transcription has been a core field of Music Information Retrieval. Most of the development in MIR is dependent on how efficiently and accurately the notes are extracted. Various methods have been used for Automatic Music Transcription. The most effective ones have been based on Spectral factorization technique for which Non-Negative Matrix Factorization has been profoundly used. Here we use a variant of NMF which is based on Accelerated Multiplicative Update with some predefined templates. The method provides good results on the Disklavier Dataset.

In this paper we propose a composite silence removal technique comprising of short time energy and statistical method. The performance of the proposed algorithm is compared with the Short Time Energy (STE) algorithm and the statistical method with varying Signal to Noise Ratio (SNR). In the presence of low SNR the performance of proposed algorithm is highly appreciable in compare to STE and statistical method. We have applied the proposed algorithm in the pre processing stage of speaker identification system. A comparison between the speaker identification rate including and excluding the silence removal technique shows around 20% increase in identification rate by the application of this proposed algorithm. Index Terms—End point detection, short time energy, Gaussian distribution, signal to noise ratio, speaker identification, mel frequency cepstral coefficient, Gaussian mixture model.

Formation of highly stable metal nanostructures in a Nafion® membrane with various aspect ratios has been of considerable research interest in recent years. However, there is a need for a proper understanding of the growth mechanism of such nanostructures in Nafion® (sometimes larger than the size of water–sulfonate ionic clusters of the membrane). In this work, the early growth kinetics of silver nanoparticles (NPs) in Nafion®-117 ion-exchange membrane during in situ L-ascorbic acid reduction of Ag+ ions by time resolved in situ small-angle X-ray scattering (SAXS) using synchrotron radiation with a time resolution of 50 ms are revealed for the first time. The SAXS analyses, corroborated by transmission electron microscopy, showed that the sizes of NPs increase rapidly together with their number density until they attain a certain size that could be accommodated in the ∼5 nm water–sulfonate ionic clusters. Further growth takes place either by self-agglomeration of the particles ejected out from the water–sulfonic acid clusters or by continuous reduction of metal ions on the existing NP surfaces (uniformly or on a specific plane) leading to formation of bigger nanostructures with various aspect ratios. The time resolved information of NP growth provides an opportunity for the controlled synthesis of metal NPs with a definite size, shape and size distribution for a specific application. The catalytic properties of Ag NPs formed in the membrane were examined using borohydride reduction of a model dye methylene blue. It was observed that smaller Ag NPs with a mean diameter ∼3 nm, confined in the hydrophilic clusters of the Nafion® matrix, have reasonably good catalytic activity and a lower lag time for the onset of reduction.

Interference of high amount of americium in the plutonium isotopic composition analysis has been studied by simulating gamma-ray spectra for Am-Pu samples over a wide composition range (5-97% (241)Am) for both power and research reactor grade plutonium. An alternate way for isotopic composition analysis has been proposed by correlating the isotopic ratios available in our old database with the experimentally obtained (241)Pu/(239)Pu isotopic ratio. The proposed method has been validated using simulated spectra of known isotopic compositions.