01320nas a2200145   4500008004100000245011800041210006900159300000700228490000900235520082900244100002101073700001601094700002301110856004101133       2019                        eng d00aA Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks0 aNovel Antifragility Measure Based on Satisfaction and Its Applic  a100 v20193 aAntifragility is a property that enhances the capability of a system in response to external perturbations. Although the concept has been applied in many areas, a practical measure of antifragility has not been developed yet. Here we propose a simply calculable measure of antifragility, based on the change of ``satisfaction'' before and after adding perturbations, and apply it to random Boolean networks (RBNs). Using the measure, we found that ordered RBNs are the most antifragile. Also, we demonstrated that seven biological systems are antifragile. Our measure and results can be used in various applications of Boolean networks (BNs) including creating antifragile engineering systems, identifying the genetic mechanism of antifragile biological systems, and developing new treatment strategies for various diseases.1 aPineda, Omar, K.1 aKim, Hyobin1 aGershenson, Carlos  uhttps://doi.org/10.1155/2019/372862101997nas a2200145   4500008004100000245005000041210005000091520158700141100001401728700001401742700001501756700001901771700001901790856004201809       2002                        eng d00aNeural Net Model for Featured Word Extraction0 aNeural Net Model for Featured Word Extraction3 aSearch engines perform the task of retrieving information related to the user-supplied query words. This task has two parts; one is finding 'featured words' which describe an article best and the other is finding a match among these words to user-defined search terms. There are two main independent approaches to achieve this task. The first one, using the concepts of semantics, has been implemented partially. For more details see another paper of Marko et al., 2002. The second approach is reported in this paper. It is a theoretical model based on using Neural Network (NN). Instead of using keywords or reading from the first few lines from papers/articles, the present model gives emphasis on extracting 'featured words' from an article. Obviously we propose to exclude prepositions, articles and so on, that is , English words like "of, the, are, so, therefore, " etc. from such a list. A neural model is taken with its nodes pre-assigned energies. Whenever a match is found with featured words and user-defined search words, the node is fired and jumps to a higher energy. This firing continues until the model attains a steady energy level and total energy is now calculated. Clearly, higher match will generate higher energy; so on the basis of total energy, a ranking is done to the article indicating degree of relevance to the user's interest. Another important feature of the proposed model is incorporating a semantic module to refine the search words; like finding association among search words, etc. In this manner, information retrieval can be improved markedly.1 aDas, Atin1 aMarko, M.1 aProbst, A.1 aPorter, M., A.1 aGershenson, C.  uhttp://uk.arxiv.org/abs/cs.NE/0206001