994 resultados para Extended states


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The reaction of 4,5-bis(2'-cyanoethylsulfanyl)-4',5'-dipropylthiotetrathiafulvalene with Pt(phen)Cl-2 (phen = 1,10-phenanthroline) with CsOH as base in CH3OH-THE affords the target complex I in 44% yield. This complex crystallizes in the monoclinic space group P2(1)/c, M = 790.01, a = 12.1732(12), b = 15.851(2), c = 14.5371(16) angstrom, beta = 107.693(12)degrees, V = 2672.4(5) angstrom(3) and Z = 4. It undergoes two reversible single-electron oxidation and two irreversible reduction processes. An intense electronic absorption band at 15200 cm(-1) (658 nm) in CH2Cl2 is assigned to the intramolecular mixed metal/ligand-to-ligand charge transfer (LLCT) from a tetrathiafulvalene-extended dithiolate-based HOMO to a phenanthroline-based LUMO. This band shifts hypsochromically with increasing solvent polarity. Systematic changes in the optical spectra upon oxidation allow precise tuning of the oxidation states of 1 and reversible control over its optical properties. Irradiation of 1 at 15625 cm(-1) (640 nm) in glassy solution below 150K results in emission from the (LLCT)-L-3 excited state. GRAPHICS (C) 2013 Elsevier Ltd. All rights reserved.

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It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

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Initially, service sector was defined as complementary to manufacturing sector. This situation has changed in recent times; services growth has resulted in a dominance of employment and economic activity in most developed nations and is becoming a key process for the competitiveness of their industrial sectors. New services related to commodities have become a strategy to differentiate their value proposition (Robinson et al., 2002). The service sector's importance is evident when evaluating its share in the gross domestic product. According to the World Bank (2011), in 2009, 74.8% of GDP in the euro area and 77.5% in United States were attributed to services. Globalization and use of information and communication technology has accelerated dissemination of knowledge and increasing customer expectations about services available worldwide. Innovation becomes essential to ensure that service organizations respond with appropriate products and services for each market segment. Customized and placed on time-tomarket new services require a more developed innovation process. Service innovation and new service development process are cited as one of the priorities for academic research in the following years (Karniouchina et al., 2005) This paper has the following objectives: -To present a model for the analysis of innovation process through the service value network, -To verify its applicability through an empirical research, and -To identify the path and mode of innovation for a group of studied organizations and to compare it with previous studies.

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The effective mass Schrodinger equation of a QD of parallelepipedic shape with a square potential well is solved by diagonalizing the exact Hamiltonian matrix developed in a basis of separation-of-variables wavefunctions. The expected below bandgap bound states are found not to differ very much from the former approximate calculations. In addition, the presence of bound states within the conduction band is confirmed. Furthermore, filamentary states bounded in two dimensions and extended in one dimension and layered states with only one dimension bounded, all within the conduction band which are similar to those originated in quantum wires and quantum wells coexist with the ordinary continuum spectrum of plane waves. All these subtleties are absent in spherically shaped quantum dots, often used for modeling.

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By using a simplified model of small open liquid-like clusters with surface effects, in the gas phase, it is shown how the statistical thermodynamics of small systems can be extended to include metastable supersaturated gaseous states not too far from the gas–liquid equilibrium transition point. To accomplish this, one has to distinguish between mathematical divergence and physical convergence of the open-system partition function.

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I attempt to reconcile apparently conflicting factors and mechanisms that have been proposed to determine the rate constant for two-state folding of small proteins, on the basis of general features of the structures of transition states. Φ-Value analysis implies a transition state for folding that resembles an expanded and distorted native structure, which is built around an extended nucleus. The nucleus is composed predominantly of elements of partly or well-formed native secondary structure that are stabilized by local and long-range tertiary interactions. These long-range interactions give rise to connecting loops, frequently containing the native loops that are poorly structured. I derive an equation that relates differences in the contact order of a protein to changes in the length of linking loops, which, in turn, is directly related to the unfavorable free energy of the loops in the transition state. Kinetic data on loop extension mutants of CI2 and α-spectrin SH3 domain fit the equation qualitatively. The rate of folding depends primarily on the interactions that directly stabilize the nucleus, especially those in native-like secondary structure and those resulting from the entropy loss from the connecting loops, which vary with contact order. This partitioning of energy accounts for the success of some algorithms that predict folding rates, because they use these principles either explicitly or implicitly. The extended nucleus model thus unifies the observations of rate depending on both stability and topology.

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At head of title: 93d Congress, 1st session, Senate, Executive report ; no. 93-16.

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Mode of access: Internet.

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"This report was prepared for the Unemployment Insurance Service, U.S. Department of Labor under Contract Number 99-7-0805-04-138-01 with Mathematica Policy Research, Inc. The authors of this report are John L. Czajka, Sharon K. Long and Walter Nicholson ..."

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Along most of the U.S. east and gulf coasts from Long Island to the Mexican Border, bottom profiles extending over the Inner Continental Shelves normal from the coast display a characteristic two-sector shape. Near the coast, the 'shoreface' profile sector is steep and concave-up; the seaward 'ramp' sector is planar with a gradual slope away from the coast. As part of the Beach Evaluation Program at this Center, 9 profiles extending from the coast 30.5 km (19 miles) seaward at each of 49 localities were averaged to mathematically characterize the profiles and to develop and test criteria for discriminating among groups of profiles. Results indicate Inner Continental Shelf profiles can be mathematically defined by 4 parameters: a = ramp slope (0 - 0.00107); b = depth of the ramp at the shoreline, when the ramp is extended as a straight line below the shoreface sector (0 - 24.7 meters, 0 - 81 feet); c = distance from the shoreline to the shoreface-ramp boundary (0.2 - 20.6 km, 0.12 - 12.9 miles); and f = index of concavity of the shoreface sector (0.21 - 1.72). Values in parentheses are the range of values obtained for the 49 averaged profiles. An equation was developed to define bottom depth as a function of distance from shore incorporating these four parameters. Computed depths using the equation were found to be generally within 5% of actual profile depths. In most cases, no relationship was found between the geometric characteristics of the shoreface and the ramp.

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Mode of access: Internet.

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Includes bibliographical references (p. 61).