Theorem Proving Based on Semantics of DNA Strand Graph

Because of several technological limitations of traditional silicon based computing, for past few years a paradigm shift, from silicon to carbon, is occurring in computational world. DNA computing has been considered to be quite promising in solving computational and reasoning problems by using DNA strands. Resolution, an important aspect of automated theorem proving and mathematical logic, is a rule of inference which leads to proof by contradiction technique for sentences in propositional logic and first-order logic. This can also be called refutation theorem-proving. In this paper we have shown how the theorem proving with resolution refutation by DNA computation can be represented by the semantics of process calculus and strand graph.

Prediction of Radiation Fog by DNA Computing

In this paper we propose a wet lab algorithm for prediction of radiation fog by DNA computing. The concept of DNA computing is essentially exploited for generating the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to existing forms of fuzzy reasoning by bringing it down to nanoscale. We exploit the concept of massive parallelism of DNA computing by designing this new classifier in the wet lab. This newly designed classifier is very much generalized in nature and apart from prediction of radiation fog this methodology can be applied to other types of data also. To achieve our goal we first fuzzify the given observed parameters in a form of synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning.