Use of the Nasicon/Na2SO4 couple in a solid state sensor for SO x (x=2,3)

The e.m.f. of a concentration cell for SO _x (x=2,3)-O₂ incorporating Nasicon as the main solid electrolyte has been measured in the temperature range 720 to 1080 K. The cell arrangement can be represented as, $$Pt, O'_2 + SO'_2 + SO'_3 \left| {Na_2 SO_4 \left\| {\left. {Nasicon} \right\|} \right.} \right.\left. {Na_2 SO_4 } \right|SO''_3 + SO''_2 + O''_2 , Pt$$ The Na₂SO₄ acts both as an auxiliary electrode, converting chemical potentials of SO _x and O₂ to equivalent sodium potentials, and as an electrolyte. The presence of Na₂SO₄ provides partial protection of Nasicon from chemical reaction with gas mixtures containing SO _x . The open circuit e.m.f. of the cell is in close agreement with values given by the Nernst equation. For certain fixed inlet gas compositions of SO₂+O₂, the e.m.f. varies non-linearly with temperature. The intrinsic response time of the cell to step changes in gas composition is estimated to vary from ～2.0 ksec at 723K to ～ 0.2 ksec at 1077K. The cell functions well for large differences in partial pressures of SO₃(p″_{SO 3}/p′_{SO 3}≈10⁴) at the electrodes.     

Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.

Nitrites Derived From Foneiculum Vulgare (Fennel) Seeds Promotes Vascular Functions

Abstract: Recent evidence has demonstrated that nitrites play an important role in the cardiovascular system. Fennel (Foneiculum vulgare) seeds are often used as mouth fresheners after a meal in both the Indian sub‐continent and around the world. The present study aims to quantify the nitrite and nitrates in fennel seeds as well as elucidating the effect of fennel derived‐nitrites on vascular functions. Results from our study show that fennel seeds contain significantly higher amount of nitrites when compared to other commonly used post‐meal seeds. Furthermore our study confirmed the functional effects of fennel derived‐nitrites using in vitro and ex vivo models that describe the promotion of angiogenesis, cell migration, and vasorelaxation. We also showed that chewing fennel seeds enhanced nitrite content of saliva. Thus our study indicates the potential role of fennel derived‐nitrites on the vascular system. Practical Application: This study is focused on determining the effect of fennel‐derived nitrites on angiogenesis (the formation of new blood vessels from pre‐existing ones), cell migration, and vasorelaxation (dilation of blood vessels) thereby preserving cardiovascular health.     

Salacia mulbarica leaf extract mediated synthesis of silver nanoparticles for antibacterial and ct‐DNA damage via releasing of reactive oxygen species

In this examination, we researched the advantages of DNA fragmentation and metallic nanoparticles well‐appointed with biomolecules. A novel interpretation of DNA damage by Silver Nano‐Clusters (AgNCs) which were developed by the utilization of green synthesis method was demonstrated. The green synthesis of AgNCs was accomplished by utilizing the leaf extract of Salacia mulbarica (SM). The preparation of SM‐AgNCs was developed by estimating surface plasmon resonance peak around 449 nm by using a UV–Visible spectrophotometer. The effect of phytochemicals in SM leaf extract on the development of stable SM‐AgNCs was confirmed by FTIR spectroscopy. The size of the fabricated SM‐AgNCs was estimated by dynamic light scattering and zeta‐sizer analysis and the morphology of the SM‐AgNCs was examined by transmission electron microscopy. The presence of clusters of Ag particles in the prepared SM‐AgNCs was recognized by energy dispersion X‐ray analysis. The results show that saponins, phytosterols, and phenolic compounds present in plant extract may play a great part in developing the SM‐AgNCs in their specialized particles. The succeeded SM‐AgNCs shows incredible anti‐bacterial action towards Escherichia coli and Bacillus subtilis. In‐light of the antibacterial study, these SM‐AgNCs were analyzed with calf thymus‐DNA and found significant damage to the strand of thymus‐DNA.Inspec keywords: visible spectra, surface plasmon resonance, transmission electron microscopy, DNA, nanofabrication, particle size, X‐ray chemical analysis, ultraviolet spectra, molecular biophysics, nanomedicine, microorganisms, nanoparticles, silver, X‐ray diffraction, antibacterial activity, Fourier transform infrared spectra, biomedical materialsOther keywords: stable SM‐AgNCs, silver nanoparticles, ct‐DNA damage, metallic nanoparticles, silver nanoclusters, Salacia mulbarica leaf extract, reactive oxygen species, DNA fragmentation, surface plasmon resonance, UV‐Visible spectrophotometer, Fourier transform infrared spectroscopy, dynamic light scattering, Zeta‐sizer analysis, transmission electron microscopy, energy dispersive X‐ray analysis, saponins, phytosterols, phenolic compounds, plant extract, Escherichia coli, Bacillus subtilis, Ag     

Study of Methane Decomposition on Fe/MgO-Based Catalyst Modified by Ni,Co, and Mn Additives

Catalytic decomposition of methane (CDM) generates clean hydrogen and carbon nanomaterials. In this study, methane decomposition to hydrogen and carbon was investigated over Ni-, Co-, or Mn-doped Fe/MgO catalysts. The doping effect of different metals, varying from 3 to 10?wt%, was investigated. The catalytic performance of the obtained materials (noted 15%Fe+x%metal/MgO) revealed that the doping effect of Ni, Co, and Mn significantly improved the activity of Fe/MgO. Among the Ni-doped catalyst series, the 15%Fe+3%Ni/MgO catalyst performed better than the rest of the Ni catalysts. The 6%Co-containing catalyst remained the best in terms of activity in the Co-doped catalyst series and the 15%Fe+6%Mn/MgO solid showed better methane conversion for the Mn-doped series. Overall, 3%Ni-containing catalyst displayed the best catalytic performance among all Ni-, Co-, and Mn-doped catalysts. XRD, N₂ sorption, and H₂ temperature-programmed reduction (TPR), Laser–Raman spectroscopy, thermogravimetric analysis (TGA) under air, and temperature-programmed oxidation (TPO) were used for catalyst characterization. The results revealed that all the doped catalysts exhibited better metallic active site distribution than 15%Fe/MgO and proved that metal doping played a crucial role in catalytic performance.     

Sparse Representations to Replace TOFD Images in Non-destructive Testing of Materials

This paper describes a proposed method for the selection of relevant samples of ultrasonic signals during automatic material inspection. Instead of the well-known time of flight diffraction (TOFD) images, data are stored as a sparse matrix in which the elements only indicate whether a defect has been detected. This technique avoids storage of useless signals received during probe displacement in cases of low and high signal-to-noise ratios that correspond to coarse-grained and fine-grained materials, respectively. The approach is based on comparing the positions of maximum amplitudes, which are randomly located when signals only consist of noise but are in the same signal range when a defect is detected. The matrix elements are then applied as inputs to a self organizing map by neural networks to produce a normalized sparse matrix as the output, with a constant number of elements. This approach will be beneficial to enable the use of selected data in intelligent systems requiring a fixed number of inputs to characterize defects.     

Highly refined human action recognition model to handle intraclass variability & interclass similarity

Multimedia Tools and Applications - The proliferation of network cameras leads to the increase in the usage of cameras in day-to-day life, results in the growth rate of videos increases...     

Partial oxidation of methane on co-precipitated Ni–Mg/Al catalysts modified with copper or iron

Methane transformation to hydrogen and synthesis gas (CO + H₂) by heterogenous catalysts can play an important role to secure the supply of energy, chemicals and fuels in the future. Methane is the main constituent of natural gas and biogas and it is also found in crystalline hydrates at the continental slopes of many oceans. In view of this vast reserves and resources, the use of methane as chemical feedstock has to be intensified. In this present work, (NiMg)Al catalysts doped with Fe or Cu, prepared by co-precipitation method and characterized by different techniques, were studied in the partial oxidation of methane (T_reaction = 750 °C, CH₄/O₂ ratio = 2). The effect of catalyst composition and pre-treatment conditions of these catalysts were investigated. Also, these catalysts show a very high activity and selectivity in the partial oxidation reaction, which depends on the conditions of catalysts preparation. The obtained results indicated increasing of activity and selectivity with decreasing calcination temperature and increasing nickel and aluminium contents in the catalysts composition. The solid doped with iron constituted the best catalyst for the total oxidation of methane and for the water–gas shift reaction. On the other hand, the addition of copper was remarkably improved the catalytic performances of the (NiMg)Al solid. So, the presence of this element supported the partial oxidation of methane with production of syngas (CO + H₂). With the addition of iron or copper for the catalyst composition, we were observed (in our previous work) the possibility of formation of NiM (M = Fe or Cu) alloy which increased nickel particles dispersion. In the case of copper, the reducibility of NiO was also assisted (TPR results) which increased catalytic activity in partial oxidation of methane.     

A Hardware-Accelerated Quantum Monte Carlo framework (HAQMC) for N-body systems

Interest in the study of structural and energetic properties of highly quantum clusters, such as inert gas clusters has motivated the development of a hardware-accelerated framework for Quantum Monte Carlo simulations. In the Quantum Monte Carlo method, the properties of a system of atoms, such as the ground-state energies, are averaged over a number of iterations. Our framework is aimed at accelerating the computations in each iteration of the QMC application by offloading the calculation of properties, namely energy and trial wave function, onto reconfigurable hardware. This gives a user the capability to run simulations for a large number of iterations, thereby reducing the statistical uncertainty in the properties, and for larger clusters. This framework is designed to run on the Cray XD1 high performance reconfigurable computing platform, which exploits the coarse-grained parallelism of the processor along with the fine-grained parallelism of the reconfigurable computing devices available in the form of field-programmable gate arrays. In this paper, we illustrate the functioning of the framework, which can be used to calculate the energies for a model cluster of helium atoms. In addition, we present the capabilities of the framework that allow the user to vary the chemical identities of the simulated atoms.Program summaryProgram title: Hardware Accelerated Quantum Monte Carlo (HAQMC)Catalogue identifier: AEEP_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEEP_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 691 537No. of bytes in distributed program, including test data, etc.: 5 031 226Distribution format: tar.gzProgramming language: C/C++ for the QMC application, VHDL and Xilinx 8.1 ISE/EDK tools for FPGA design and developmentComputer: Cray XD1 consisting of a dual-core, dualprocessor AMD Opteron 2.2 GHz with a Xilinx Virtex-4 (V4LX160) or Xilinx Virtex-II Pro (XC2VP50) FPGA per node. We use the compute node with the Xilinx Virtex-4 FPGAOperating system: Red Hat Enterprise Linux OSHas the code been vectorised or parallelized?: YesClassification: 6.1Nature of problem: Quantum Monte Carlo is a practical method to solve the Schrödinger equation for large many-body systems and obtain the ground-state properties of such systems. This method involves the sampling of a number of configurations of atoms and averaging the properties of the configurations over a number of iterations. We are interested in applying the QMC method to obtain the energy and other properties of highly quantum clusters, such as inert gas clusters.Solution method: The proposed framework provides a combined hardware–software approach, in which the QMC simulation is performed on the host processor, with the computationally intensive functions such as energy and trial wave function computations mapped onto the field-programmable gate array (FPGA) logic device attached as a co-processor to the host processor. We perform the QMC simulation for a number of iterations as in the case of our original software QMC approach, to reduce the statistical uncertainty of the results. However, our proposed HAQMC framework accelerates each iteration of the simulation, by significantly reducing the time taken to calculate the ground-state properties of the configurations of atoms, thereby accelerating the overall QMC simulation. We provide a generic interpolation framework that can be extended to study a variety of pure and doped atomic clusters, irrespective of the chemical identities of the atoms. For the FPGA implementation of the properties, we use a two-region approach for accurately computing the properties over the entire domain, employ deep pipelines and fixed-point for all our calculations guaranteeing the accuracy required for our simulation.