97 resultados para network formation
Resumo:
Surface texture plays an important role in the frictional behavior and transfer layer formation of contacting surfaces. In the present investigation, basic experiments were conducted using an inclined pin-on-plate sliding apparatus to better understand the role of surface texture on the coefficient of friction and the formation of a transfer layer. In the experiments, soft HCP materials such as pure Mg and pure Zn were used for the pins and a hardened 080 M40 steel was used for the plate. Two surface parameters of the steel plates—roughness and texture—were varied in tests that were conducted at a sliding speed of 2 mm/s in ambient conditions under both dry and lubricated conditions. The morphologies of the worn surfaces of the pins and the formation of the transfer layer on the counter surfaces were observed using a scanning electron microscope. In the experiments, the occurrence of stick-slip motion, the formation of a transfer layer, and the value of friction were recorded. With respect to the friction, both adhesion and plowing components were analyzed. Based on the experimental results, the effect of surface texture on the friction was attributed to differences in the amount of plowing. Both the plowing component of friction and the amplitude of stick-slip motion were determined to increase surface textures that promote plane strain conditions and decrease the textures that favor plane stress conditions.
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Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.
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The standard free energies of formation of CaO derived from a variety of high-temperature equilibrium measurements made by seven groups of experimentalists are significantly different from those given in the standard compilations of thermodynamic data. Indirect support for the validity of the compiled data comes from new solid-state electrochemical measurements using single-crystal CaF2 and SrF2 as electrolytes. The change in free energy for the following reactions are obtained: CaO + MgF2 --> MgO + CaF2 Delta G degrees = -68,050 -2.47 T(+/-100) J mol(-1) SrO + CaF2 --> SrF2 + CaO Delta G degrees = -35,010 + 6.39 T (+/-80) J mol(-1) The standard free energy changes associated with cell reactions agree with data in standard compilations within +/- 4 kJ mol(-1). The results of this study do not support recent suggestions for a major revision in thermodynamic data for CaO.
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This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.
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We present a technique for an all-digital on-chip delay measurement system to measure the skews in a clock distribution network. It uses the principle of sub-sampling. Measurements from a prototype fabricated in a 65 nm industrial process, indicate the ability to measure delays with a resolution of 0.5ps and a DNL of 1.2 ps.
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The nucleataon growth model of electrochemical phase formation is analysed for the hnear potential sweep input Apart from deducing diagnostic criteria and method~ of estimating model parameters, the predictions of the nucleation growth model are compared and contrasted with those of a sample adsorption model A dastlnCtlOn is made possible between adsorption and phase transition, which seems useful for understanding the nature of ECPF phenomena, especially underpotentlal deposition (UPD).
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The Gibbs energy of formation of V2O3-saturated spinel CoV2O4 has been measured in the temperature range 900–1700 K using a solid state galvanic cell, which can be represented as Pt, Co + CoV2O4 + V2O3/(CaO) ZrO2/Co + CoO, Pt. The standard free energy of formation of cobalt vanadite from component oxides can be represented as CoO (rs) + V2O3 (cor) → CoV2O4 (sp), ΔG° = −30,125 − 5.06T (± 150) J mole−1. Cation mixing on crystallographically nonequivalent sites of the spinel is responsible for the decrease in free energy with increasing temperature. A correlation between “second law” entropies of formation of cubic 2–3 spinels from component oxides with rock salt and corundum structures and cation distribution is presented. Based on the information obtained in this study and trends in the stability of aluminate and chromite spinels, it can be deduced that copper vanadite is unstable.
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Communication within and across proteins is crucial for the biological functioning of proteins. Experiments such as mutational studies on proteins provide important information on the amino acids, which are crucial for their function. However, the protein structures are complex and it is unlikely that the entire responsibility of the function rests on only a few amino acids. A large fraction of the protein is expected to participate in its function at some level or other. Thus, it is relevant to consider the protein structures as a completely connected network and then deduce the properties, which are related to the global network features. In this direction, our laboratory has been engaged in representing the protein structure as a network of non-covalent connections and we have investigated a variety of problems in structural biology, such as the identification of functional and folding clusters, determinants of quaternary association and characterization of the network properties of protein structures. We have also addressed a few important issues related to protein dynamics, such as the process of oligomerization in multimers, mechanism on protein folding, and ligand induced communications (allosteric effect). In this review we highlight some of the investigations which we have carried out in the recent past. A review on protein structure graphs was presented earlier, in which the focus was on the graphs and graph spectral properties and their implementation in the study of protein structure graphs/networks (PSN). In this article, we briefly summarize the relevant parts of the methodology and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks. The investigations of structural/biological problems are divided into two parts, in which the first part deals with the analysis of PSNs based on static structures obtained from x-ray crystallography. The second part highlights the changes in the network, associated with biological functions, which are deduced from the network analysis on the structures obtained from molecular dynamics simulations.
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With a short review of the work on the Lecher wire method of wavelength measurement, this paper describes in detail the wave form of current distribution along wires under a variety of terminal conditions of length and impedances.
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Drop formation at the conical tips of melting rods has been experimentally studied using the transparent wax-alcohol/acetonitrile system. The effects of cone angle, rod diameter, immersion depth, and bath temperature on the detached drop mass have been studied over a wide range, besides recording useful qualitative information based on visual observation. The experimental results suggest that the phenomenon of drop formation at the tip of melting rods has a close parallel with the drop formation at conical tips, at least on a qualitative basis. However, the results could not be quantified owing to difficulties in characterizing the physical properties of the system, despite efforts to minimize them.
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This paper is concerned with the integration of voice and data on an experimental local area network used by the School of Automation, of the Indian Institute of Science. SALAN (School of Automation Local Area Network) consists of a number of microprocessor-based communication nodes linked to a shared coaxial cable transmission medium. The communication nodes handle the various low-level functions associated with computer communication, and interface user data equipment to the network. SALAN at present provides a file transfer facility between an Intel Series III microcomputer development system and a Texas Instruments Model 990/4 microcomputer system. Further, a packet voice communication system has also been implemented on SALAN. The various aspects of the design and implementation of the above two utilities are discussed.
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We demonstrate the aptitude of supramolecular hydrogel formation using simple bile acid such as lithocholic acid in aqueous solution in the presence of various dimeric or oligomeric amines. By variation of the choice of the amines in such mixtures the gelation properties could be modulated. However, the replacement of lithocholic acid (LCA) by cholic acid or deoxycholic acid resulted in no hydrogel formation. FT-IR studies confirm that the carboxylate and ammonium residues of the two components are involved in the salt (ion-pair) formation. This promotes further assembly of the components reinforced by a continuous hydrogen bonded network leading to gelation. Electron microscopy shows the morphology of the internal organization of gels of two component systems which also depends significantly on the amine part. Variation of the amine component from the simple 1,2-ethanediamine (EDA) to oligomeric amines in such gels of lithocholic acid changes the morphology of the assembly from long one-dimensional nanotubes to three-dimensional complex structures. Single crystal X-ray diffraction analysis with one of the amine-LCA complexes suggested the motif of fiber formation where the amines interact with the carboxylate and hydroxyl moieties through electrostatic forces and hydrogen bonding. From small angle neutron scattering study, it becomes clear that the weak gel from LCA-EDA shows scattering oscillation due to the presence of non-interacting nanotubules while for gels of LCA with oligomeric amines the individual fibers come together to form complex three-dimensional organizations of higher length scale. The rheological properties of this class of two component system provide clear evidence that the flow behavior can be modulated varying the acid-amine ratio.