Evolutionary algorithms are better than heuristic algorithms at finding protein complexes in protein-protein interaction networks (PPINs). Many of these algorithms depend on their standard frameworks, which are based on topology. Further, many of these algorithms have been exclusively examined on networks with only reliable interaction data. The main objective of this paper is to extend the design of the canonical and topological-based evolutionary algorithms suggested in the literature to cope with noisy PPINs. The design of the evolutionary algorithm is extended based on the functional domain of the proteins rather than on the topological domain of the PPIN. The gene ontology annotation in each molecular function, biological proce

The aim of this research is to construct an educational program in light of the theory of behavioral cognitive and its impact on the development of the efficient response to students affected by crises (centers of your right to education). To achieve the objectives of the research, two scales were developed by the researcher in addition to two equivalent hypotheses were formulated. The scale contains (26) items divided into five fields; for its validity and reliability were derived based on the measure of efficient response, an educational program based on the theory of behavioral cognition. The test and the educational program were applied to a sample of (60) students from the centers of your right to education, divided into experimenta

The use of piezocatalysis with photocatalysis in the degradation of organic pollutants has become an attractive research field due to the enhanced synergistic effect of the two processes. Herein, binary composites of AgBr decorated onto Br-doped g-C3N4 in different weight percentages (AgBr content was varied from 10 wt% to 40 wt%) have been built as heterojunction piezo-photo catalysts with a highly efficient performance. The 20 wt%AgBr/Br-g-C3N4 catalysts showed synergistically improved piezo-photo catalytic decomposition toward Methylene blue (MB) under visible-light and sonication exposure in a simple constructed split-plate airlift contactor, completing 99.7 % degradation of MB within 60 min. The heterogeneous junction established among

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co