Non-algorithmic

Tsaparlis (117) carried out a correlation study of the role of various cognitive factors (variables) in the solution of non-algorithmic quantitative problems in elementary physical chemistry. The cognitive variables were scientific reasoning (developmental level), working-memory capacity functional mental capacity (M-capacity) and disembedding ability. 

Once an axiom is known, however, then the computation of halters and nonhalters for which sufficient axioms are known becomes an algorithmic problem. Therefore, the discovery of new axioms converts subsets of problems from non-algorithmic... 

This explanation actually fits surprisingly well it is non-algorithmic (it is determining unprovable axioms), it is incremental (each axiom gives more explanatory power), and it is weaker than a halting oracle. 

Nielsen, S.A. Borum, K.K. and Gundtoft, H.E (1995). Verifying an ultrasonic reconstruction algorithm for non-destructive tomography. Proc. of 1st World Congress on Ultrasonics, Berlin, Vol. 1, 446-450. 

A. Mohammad-Djafari H. Carfantan and. 1. Idier. A single site update algorithm for non linear diffraction tomography. ICASSP, 4, 1997. 

The algorithm for sizing of eraeks with complex cross-sections and unknown shapes based on the method was used in for sizing of cracks oriented perpendicularly to the applied field. This algoritlim is presented in Fig.3. In this paper, the same algorithm is applied readily to sizing of cracks with non-perpendicular orientation with respect to the applied field. 

Low and High frequency can be restored by use of a deconvolution algorithm that enhances the resolution. We operate an improvement of the spectral bandwidth by Papoulis deconvolution based essentially on a non-linear adaptive extrapolation of the Fourier domain. 

The simplest way to add a non-adiabatic correction to the classical BO dynamics method outlined above in Section n.B is to use what is known as surface hopping. First introduced on an intuitive basis by Bjerre and Nikitin [200] and Tully and Preston [201], a number of variations have been developed [202-205], and are reviewed in [28,206]. Reference [204] also includes technical details of practical algorithms. These methods all use standard classical trajectories that use the hopping procedure to sample the different states, and so add non-adiabatic effects. A different scheme was introduced by Miller and George [207] which, although based on the same ideas, uses complex coordinates and momenta. 

In molecular mechanics and molecular dynamics studies of proteins, assig-ment of standard, non-dynamical ionization states of protein titratable groups is a common practice. This assumption seems to be well justified because proton exchange times between protein and solution usually far exceed the time range of the MD simulations. We investigated to what extent the assumed protonation state of a protein influences its molecular dynamics trajectory, and how often our titration algorithm predicted ionization states identical to those imposed on the groups, when applied to a set of structures derived from a molecular dynamics trajectory [34]. As a model we took the bovine... 

C. G. Lambert, Multipole-based Algorithms in Molecular Biophysics and Non-parametric Statistics, Ph.D. Dissertation, Duke University Department of Computer Science, 1997. 

Other methods which are applied to conformational analysis and to generating multiple conformations and which can be regarded as random or stochastic techniques, since they explore the conformational space in a non-deterministic fashion, arc genetic algorithms (GA) [137, 1381 simulation methods, such as molecular dynamics (MD) and Monte Carlo (MC) simulations 1139], as well as simulated annealing [140], All of those approaches and their application to generate ensembles of conformations arc discussed in Chapter II, Section 7.2 in the Handbook. 

An important though deman ding book. Topics include statistical mechanics, Monte Carlo sim illation s. et uilibrium and non -ec iiilibrium molecular dynamics, an aly sis of calculation al results, and applications of methods to problems in liquid dynamics. The authors also discuss and compare many algorithms used in force field simulations. Includes a microfiche containing dozens of Fortran-77 subroutines relevant to molecular dynamics and liquid simulations. 

In order to use a derivative minimisation method it is obviously necessary to be able to calculate the derivatives of fhe energy wifh respecf to the variables (i.e. the Cartesian or interna] coordinates, as appropriate). Derivatives may be obtained either analytically or numerically. The use of analytical derivatives is preferable as fhey are exact, and because they can be calculated more quickly if only numerical derivatives are available then it may be more effective to use a non-derivative minimisation algorithm. The problems of calculating analytical derivatives with quantum mechanics and molecular mechanics were discussed in Sections 3.4.3 and 4.16, respectively. 

Townsend, P. and Webster, M. I- ., 1987. An algorithm for the three dimensional transient simulation of non-Newtonian fluid flow. In Pande, G. N. and Middleton, J. (eds). Transient Dynamic Analysis and Constitutive Laws for Engineering Materials Vul. 2, T12, Nijhoff-Holland, Swansea, pp. 1-11. 

Using the described algorithm the flow domain inside the cone-and-plate viscometer is simulated. Tn Figure 5.17 the predicted velocity field in the (r, z) plane (secondary flow regime) established inside a bi-conical rheometer for a non-Newtonian fluid is shown. 

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