996 resultados para Visible Difference Prediction
Resumo:
A combined computational and experimental polymorph search was undertaken to establish the crystal forms of 7-fluoroisatin, a simple molecule with no reported crystal structures, to evaluate the value of crystal structure prediction studies as an aid to solid form discovery. Three polymorphs were found in a manual crystallisation screen, as well as two solvates. Form I ( P2(1)/c, Z0 1), found from the majority of solvent evaporation experiments, corresponded to the most stable form in the computational search of Z0 1 structures. Form III ( P21/ a, Z0 2) is probably a metastable form, which was only found concomitantly with form I, and has the same dimeric R2 2( 8) hydrogen bonding motif as form I and the majority of the computed low energy structures. However, the most thermodynamically stable polymorph, form II ( P1 , Z0 2), has an expanded four molecule R 4 4( 18) hydrogen bonding motif, which could not have been found within the routine computational study. The computed relative energies of the three forms are not in accord with experimental results. Thus, the experimental finding of three crystalline polymorphs of 7- fluoroisatin illustrates the many challenges for computational screening to be a tool for the experimental crystal engineer, in contrast to the results for an analogous investigation of 5- fluoroisatin.
Resumo:
The three lowest (1(2)A('), 2(2)A('), and 1(2)A(')) potential-energy surfaces of the C2Cl radical, correlating at linear geometries with (2)Sigma(+) and (2)Pi states, have been studied ab initio using a large basis set and multireference configuration-interaction techniques. The electronic ground state is confirmed to be bent with a very low barrier to linearity, due to the strong nonadiabatic electronic interactions taking place in this system. The rovibronic energy levels of the (CCCl)-C-12-C-12-Cl-35 isotopomer and the absolute absorption intensities at a temperature of 5 K have been calculated, to an upper limit of 2000 cm(-1), using diabatic potential-energy and dipole moment surfaces and a recently developed variational method. The resulting vibronic states arise from a strong mixture of all the three electronic components and their assignments are intrinsically ambiguous. (c) 2005 American Institute of Physics.
Resumo:
The first three electronic states (1(2)A', 2(2)A', 1(2)A '') of the C2Br radical, correlating at linear geometries with (2)Sigma(+) and (2)Pi states, have been studied ab initio, using Multi Reference Configuration Interaction techniques. The electronic ground state is found to have a bent equilibrium geometry, R-CC = 1.2621 angstrom, R-CBr = 1.7967 angstrom, < CCBr 156.1 degrees, with a very low barrier to linearity. Similarly to the valence isoelectronic radicals C2F and C2Cl, this anomalous behaviour is attributed to a strong three-state non-adiabatic electronic interaction. The Sigma, Pi(1/2), Pi(3/2) vibronic energy levels and their absolute infrared absorption intensities at a temperature of 5K have been calculated for the (CCBr)-C-12-C-12-Br-79 isotopomer, to an upper limit of 2000 cm(-1), using ab initio diabatic potential energy and dipole moment surfaces and a recently developed variational method.
Resumo:
DIGE is a protein labelling and separation technique allowing quantitative proteomics of two or more samples by optical fluorescence detection of differentially labelled proteins that are electrophoretically separated on the same gel. DIGE is an alternative to quantitation by MS-based methodologies and can circumvent their analytical limitations in areas such as intact protein analysis, (linear) detection over a wide range of protein abundances and, theoretically, applications where extreme sensitivity is needed. Thus, in quantitative proteomics DIGE is usually complementary to MS-based quantitation and has some distinct advantages. This review describes the basics of DIGE and its unique properties and compares it to MS-based methods in quantitative protein expression analysis.
Resumo:
Robotic and manual methods have been used to obtain identification of significantly changing proteins regulated when Schizosaccharomyces pombe is exposed to oxidative stress. Differently treated S. pombe cells were lysed, labelled with CyDye (TM) and analysed by two-dimensional difference gel. electrophoresis. Gel images analysed off-line, using the DeCyder (TM) image analysis software [GE Healthcare, Amersham, UK] allowed selection of significantly regulated proteins. Proteins displaying differential expression were excised robotically for manual digestion and identified by matrix-assisted laser desorption/ionisation - mass spectrometry (MALDI-MS). Additionally the same set of proteins displaying differential expression were automatically cut and digested using a prototype robotic platform. Automated MALDI-MS, peak label assignment and database searching were utilised to identify as many proteins as possible. The results achieved by the robotic system were compared to manual methods. The identification of all significantly altered proteins provides an annotated peroxide stress-related proteome that can be used as a base resource against which other stress-induced proteomic changes can be compared.
Resumo:
The application of prediction theories has been widely practised for many years in many industries such as manufacturing, defence and aerospace. Although these theories are not new, their application has not been widely used within the building services industry. Collectively, the building services industry should take a deeper look at these approaches in comparison with the traditional deterministic approaches currently being practised. By extending the application into this industry, this paper seeks to provide the industry with an overview of how simplified stochastic modelling coupled with availability and reliability predictions using historical data compiled from various sources could enhance the quality of building services systems.
Resumo:
In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.
OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error
Resumo:
This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.
Resumo:
Abu-Saris and DeVault proposed two open problems about the difference equation x(n+1) = a(n)x(n)/x(n-1), n = 0, 1, 2,..., where a(n) not equal 0 for n = 0, 1, 2..., x(-1) not equal 0, x(0) not equal 0. In this paper we provide solutions to the two open problems. (c) 2004 Elsevier Inc. All rights reserved.
Resumo:
In this paper, we study the oscillating property of positive solutions and the global asymptotic stability of the unique equilibrium of the two rational difference equations [GRAPHICS] and [GRAPHICS] where a is a nonnegative constant. (c) 2005 Elsevier Inc. All rights reserved.
Resumo:
In this paper, we study the behavior of the positive solutions of the system of two difference equations [GRAPHICS] where p >= 1, r >= 1, s >= 1, A >= 0, and x(1-r), x(2-r),..., x(0), y(1-max) {p.s},..., y(0) are positive real numbers. (c) 2005 Elsevier Inc. All rights reserved.