In this paper, we studied the liquid-vapor phase diagram and structural properties of discrete potential fluids using Gibbs ensemble simulations and integral equations theory. For this, we considered three discrete fluids, namely, the square well, square well-barrier, and square well-barrier-well. They represent simple models for fluids with competing interactions that exhibit a rich microscopic and macroscopic phase behavior depending on both the strength and range of the attractions and repulsions in the potential. Here, we emphasized a structural behavior near the liquid- vapor coexistence. For the square well-barrier fluid, we observed a possible scenario of a microscopic phase separation associated with a cluster-like formation near the critical region, which could be interpreted as a frustration mechanism of the liquid-vapor transition when either the strength or range of repulsion increases. This microscopic- like separation can be inhibited by suppressing the repulsion or adding an extra well to the interaction potential. However, for the square well fluid with long-range potential, we found evidence of a microscopic aggregation driven solely by attractions.

We report the improvement of five argon force fields by scaling Lennard-Jones {\bf 6-12 potential (LJP)} energy ($\epsilon$) and distance ($\sigma$) parameters to reproduce liquid-vapor phase diagram and surface tension simultaneously, with molecular dynamics. Original force fields reproduce only liquid-vapor phase diagram among other properties except surface tension. Results showed that all {\bf new} force fields {\bf obtained by scaling LJP parameters reproduce well the experimental surface tension and the liquid-vapor phase diagram}, also the LJP energy and distance parameters converge in a nearby region in the $\epsilon$-$\sigma$ phase space, which is different from the original values. This study gives the intervals where the numerical values of $\epsilon$ and $\sigma$ reproduce both properties mentioned above. {\bf Furthermore, a study to calculate surface tension to avoid finite size effects is shown.}

This research proposes a method for the detection of semantic similarities in text snippets; the method achieves an unsupervised extraction and comparison of semantic information by mimicking skills for the identification of clauses and possible verb conjugations, the selection of the most accurate organization of the parts of speech, and similarity analysis by a direct comparison on the parts of speech from a pair of text snippets. The method for the extraction of the parts of speech in each text exploits a knowledge base structured as a dictionary and a thesaurus to identify the possible labels of each word and its synonyms. The method consists of the processes of perception, debiasing, reasoning and assessment. The perception module decomposes the text into blocks of information focused on the elicitation of the parts of speech. The debiasing module reorganizes the blocks of information due to the biases that may be produced in the previous perception. The reasoning module finds the similarities between blocks from two texts through analyses of similarities on synonymy, morphological properties, and the relative position of similar concepts within the texts. The assessment generates a judgement on the output produced by the reasoning as the averaged similarity assessment obtained from the parts of speech similarities of blocks. The proposed method is implemented on an English language version to exploit a knowledge base in English for the extraction of the similarities and differences of texts. The system implements a set of syntactic and logical rules that enable the autonomous reasoning that uses a knowledge base regardless of the concepts and knowledge domains of the latter. A system developed with the proposed method is tested on the “test” dataset used on the SemEval 2017 competition on seven knowledge bases compiled from six dictionaries and two thesauruses. The results indicate that the performance of the method increases as the degree of completeness of concepts and their relations increase, and the Pearson correlation for the most accurate knowledge base is 77%.