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Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA’s performance in intermittent demand items with a risk of obsolescence.

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.

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.

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.

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.

The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.

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.

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes aremml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:miO/mml:mimml:mo(/mml:momml:min/mml:mimml:mo)/mml:mo/mml:mathandmml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:miO/mml:mimml:mo(/mml:momml:mn1/mml:mnmml:mo)/mml:mo/mml:math, respectively. The proposed algorithm has been tested on Protein-Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

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.

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.

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

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.

The Hybrid Monte Carlo method developed for attenuation correction has been extended for 500 ml cylindrical geometry. The method has been experimentally validated. Absolute efficiency studies for 500 ml aqueous, air and point source has been carried out using Monte Carlo simulation. It has been observed that point source efficiency is a good estimate of 500 ml source beyond sample-to-detector distance of 15 cm. It has been found that while HMC method for attenuation correction is valid at all sample-to-detector distances and over all transmittance range, the far-field and near-field formulae available in literature are valid only over a very narrow range of sample-to-detector distance.

paper, a novel hierarchical speaker identification method based on Latent Variable Decomposition (LVD) has been proposed. Firstly, we got a coarse decision by a fast scan all registered speakers using LVD based features and GMM classifier to find R possible target speakers, and then MFCC or PCA based features were used to make final decision. LVD has another advantage: reduction of the feature vectors dimensions, and the noise is removed from speech simultaneity. So, it can reduce the computational complexity and improve the performance of speaker identification. The experimental results showed that the proposed method could improve recognition accuracy of system remarkably and the system has better robustness by comparing with the traditional speaker identification method. General Terms

Isotopic composition of dilute Pu solutions (1–3900 μg/mL) has been determined by analysis of HPGe detector response function in the 90–105 keV region of gamma ray spectra. Results are in excellent agreement with that obtained from mass spectrometric measurements. The present method has been successfully applied for samples of low Pu concentrations, which otherwise is not possible using the conventional 120–415 keV region of plutonium γ ray spectra.

Assembled diglycolamides (DGAs) have a strong affinity toward f-element ions at high nitric acid concentrations. Small angle X-ray scattering studies revealed that nitric acid concentration dependent changes occur in the geometrical arrangement of the DGA units of monomeric methacryloyl-DGA and the corresponding polymeric DGA. Cylindrical aggregates of methacryloyl-DGA were formed in 10:1 n-dodecane:1-decanol (added for solubility reasons) upon equilibration with nitric acid. The lengths and diameters of the cylindrical methacryloyl-DGA aggregates increased on varying the nitric acid concentration from 3 to 4 mol L-1. This resulted in an increase of the distribution coefficient (D) of Eu3+ ions from 72 to 197. The physical structure of cross-linked (10 mol %) poly(methacryloyl-DGA) reorganized distinctly upon equilibration with nitric acid. In this case, also the DEu3+ values increased significantly from 147 mL g-1 at 1 mol L-1 HNO3 to ∼4000 mL g-1 at 4 mol L-1 HNO3. Hydrogen bonds between the outer sphere of Eu3+/Am3+/Pu4+ nitrate and DGA units provide stabilization in the hydrophobic environment. This results in enhancement of their extraction upon increasing nitric acid concentration both in the organic phase as well as in the polymer matrix. Though monomeric and polymeric methacryloyl-DGA are different in their physical assembling, the normalized DI values for a same f-element ion upon varying HNO3 concentrations show remarkably similar patterns in both forms. In addition, the unusual stoichiometry deduced from the slopes of the log D vs log[HNO3] curves at fixed nitrate concentration seems to suggest that the normal extraction mechanism may not be operating in the hydrogen bonded DGA assemblies. (Figure Presented).

The Hybrid Monte Carlo (HMC) method developed earlier for attenuation correction of non-collimated samples [Agarwal et al., 2008, Nucl. Instrum. Methods A 597, 198], has been extended to the segmented gamma ray assay of cylindrical samples. The method has been validated both experimentally and theoretically. For experimental validation, the results of HMC calculation have been compared with the experimentally obtained attenuation correction factors. The HMC attenuation correction factors have also been compared with the results obtained from literature available near-field and far-field formulae at two sample-to-detector distances (10.3 cm and 20.4 cm). The method has been found to be valid at all sample-to-detector distances over a wide range of transmittance. On the other hand, the literature available near-field and far-field formulae have been found to work over a limited range of sample-to detector distances and transmittances. The HMC method has been further extended to circular collimated geometries where analytical formula for attenuation correction does not exist.

We demonstrate for the first time the intrinsic role of nanoconfinement in facilitating the chemical reduction of metal ion precursors with a suitable reductant for the synthesis of metal nanoparticles, when the identical reaction does not occur in bulk solution. Taking the case of citrate reduction of silver ions under the unusual condition of [citrate]/[Ag(+)] ≫ 1, it has been observed that the silver citrate complex, stable in bulk solution, decomposes readily in confined nanodomains of charged and neutral matrices (ion-exchange film and porous polystyrene beads), leading to the formation of silver nanoparticles. The evolution of growth of silver nanoparticles in the ion-exchange films has been studied using a combination of (110m)Ag radiotracer, small-angle X-ray scattering (SAXS) experiments, and transmission electron microscopy (TEM). It has been observed that the nanoconfined redox decomposition of silver citrate complex is responsible for the formation of Ag seeds, which thereafter catalyze oxidation of citrate and act as electron sink for subsequent reduction of silver ions. Because of these parallel processes, the particle sizes are in the bimodal distribution at some stages of the reaction. A continuous seeding with parallel growth mechanism has been revealed. Based on the SAXS data and radiotracer kinetics, the growth mechanism has been elucidated as a combination of continuous autoreduction of silver ions on the nanoparticle surfaces and a sudden coalescence of nanoparticles at a critical number density. However, for a fixed period of reduction, the size, size distribution, and number density of thus-formed Ag nanoparticles have been found to be dependent on physical architecture and chemical composition of the matrix.

Different dimensional silver nanostructures were synthesized in the cation exchange ionomer matrix Nafion-117 by varying temperature. The nanostructures formed were different sizes spherical nanoparticles depending upon the in situ reduction temperature. Mesoscopic architecture of the nanocomposite thin films containing different dimensional silver nanostructures has been studied by small angle x-ray scattering (SAXS) in different counterionic environment. Investigation of the modifications in nanostructure pattern along with modification in self-assembling morphology of the ionomer in metal-ionomer nanocomposites in different post reduction counterionic environment was found to be unique. The post reduction neutralization by different counterions having different degree of hydration resulted to reorganization of the embedded nanostructures along with the crystalline polymer backbone of the ionomer. This influenced the polydispersity of the nanostructures significantly.

Speaker identification based on speech signal has been receiving enhanced attention from the research community. In this context the effect of dimension reduction of feature vectors using Principal Component Analysis (PCA) and Weighted Principal Component Analysis (WPCA) are compared for speaker identification in a noisy environment. MFCC feature vectors are used as original features and their dimension is reduced by PCA and WPCA techniques and then evaluated by GMM classifier. Speaker identification rate is calculated under different SNR to test the robustness of the speaker identification system. In low SNR, the speaker identification rate becomes double after reducing the dimension of feature vectors by 50% as compared to original one. The performance of WPCA is 10% to 20% better than PCA under different SNR.