E-learning and e-evaluation: A Case Study

Abstract

Structure/Activity/Property Relationships (SARs, SPRs, and PARs) appears with the studies of Louis Plack HAMMETT in 1937 [1]. The most important applications of Hammett's equation were summarized in [2].Quantitative relationships (QSAR, QSPR, QPAR) occur when the property/activity is quantitative. Not all properties and activities of chemical compounds can be classified as quantitative. In fact, few properties meet all theoretical requirements to be quantitative [3]. From this reason in the last time are avoided to be used QSAR, QSPR, and QPAR, in their place being used (Q)SAR, (Q)SPR, and (Q)PAR, or more simple SAR, SPR, and PAR. Structure-based approaches have two levels (topological and geometrical). In the topological based level, an atom, a bond from a molecule can exist (and then are evidenced through electronic transitions and/or molecular vibrations and/or rotations) or not (being a matter of 0 and 1). Not so simple stays things related to molecular geometry (especially on liquid or gas phases). Heisenberg uncertainly principle [4] shows the uncertainly rules presented at micro level (molecular and atomic level). More than that, molecular geometry depends on the environment where the molecule is (vicinity of the molecule), temperature, pressure, so on, thus dealing with molecular geometry is both a matter of relativity and a matter of uncertainty. Thus, Structure-Property-Activity Relationships (SPARs) must deal with certainties (such as molecular topology), uncertainties (such as molecular geometry), relativities (such as biological activities) and evidences (such as physical and chemical quantitative properties MATERIALS:The hydrophobicity on Hessa et al. scale [ 10 ] of fifteen standard amino acids was the property of interest.The experimental values of hydrophobicity were as follows: alanine (0.11), asparagine (2.05), aspartate (3.49), cysteine (-0.13), glutamine (2.36), glutamate (2.68), glycine (0.74), isoleucine (-0.6), leucine (-0.55), lysine (2.71), methionine (-0.1), phenylalanine (-0.32), serine (0.84), threonine (0.52), and valine (-0.31). MDF-SPAR completion: MDF Calculator & MDF Predictor. RESULTS:The model with one and two descriptors, respectively proved to has estimated and predictive abilities: Ŷ mono =-0.58+iMDRoQg·8.53 Eq(1) Ŷ bi =-1.36+iMDRoQg·6.03+ISPDwQg·0.08 Eq(2) The application of the parameters presented in the table bellow leads to the results presented bellow: Param.Eq (1) Eq (2) o building (with HyperChem) of topological (2D) and geometrical (3D) through same choices as were build the selected set to obtain predicted value(s) for the property / activity of the new compounds, even if this (these) compound (s) were not yet synthesized, in order to see if the new structure (virtual compound at this time) has or not improvements in desired property/activity. CONCLUSION MDF method and MDF-SAR methodology proved to be a very good tool for design of chemical compounds.(6) MDF Calculator (7) MDF Predictor ACKNOWLEDGEMENTS: [MDF] The MDF project was supported through ET36 resear...

An architecture solution for E-Learning System — ESSA

“…The layers are known as the presentation tier, common service tier, ESchool System service tier and the resource tier [8]. The presentation tier handles the interaction between the user and the application.…”