Abstract
Phenotypic plasticity is important in adaptation and shapes the evolution of organisms. However, we understand little about what aspects of the genome are important in facilitating plasticity. Eusocial insect societies produce plastic phenotypes from the same genome, as reproductives (queens) and nonreproductives (workers). The greatest plasticity is found in the simple eusocial insect societies in which individuals retain the ability to switch between reproductive and nonreproductive phenotypes as adults. We lack comprehensive data on the molecular basis of plastic phenotypes. Here, we sequenced genomes, microRNAs (miRNAs), and multiple transcriptomes and methylomes from individual brains in a wasp (Polistes canadensis) and an ant (Dinoponera quadriceps) that live in simple eusocial societies. In both species, we found few differences between phenotypes at the transcriptional level, with little functional specialization, and no evidence that phenotype-specific gene expression is driven by DNA methylation or miRNAs. Instead, phenotypic differentiation was defined more subtly by nonrandom transcriptional network organization, with roles in these networks for both conserved and taxon-restricted genes. The general lack of highly methylated regions or methylome patterning in both species may be an important mechanism for achieving plasticity among phenotypes during adulthood. These findings define previously unidentified hypotheses on the genomic processes that facilitate plasticity and suggest that the molecular hallmarks of social behavior are likely to differ with the level of social complexity.

Abstract
The new concept development is a critical stage of the innovation process that can be seen as a new knowledge creation process. This paper presents a new approach and a software tool for a collaborative new concept development. Our approach considers Collaborative Innovation Networks as ecosystems for new knowledge creation and integration, and Web Mashups as supporting platforms for the development of virtual co-learning and knowledge co-creation environments. The achieved results confirm the utility and efficacy of the software tool and allow foreseeing its suitability for use in educational contexts.

Abstract
The liberalization of energy markets has imposed several modifications in the electricity market environment. The paradigm of monopoly market ceased to exist, and new models have been put into practice. The new models have increased the incentive on competitiveness, making market players struggle to achieve the best outcomes out of market participation. Producers aim at reaching the maximum profit on the sale of energy, while consumers try to minimize their spending on electrical energy. The proposed methodology considers the optimization of players' participation in multiple market opportunities. Reference prices that are expected in each market type at each moment are achieved through the application of neural networks. Using the forecasted prices, the proposed portfolio optimization method allocates the sale and purchase of electrical energy to different markets throughout the time, with the aim at achieving the most advantageous participation profile. A particle swarm approach is used to reduce the execution time while guaranteeing the minimum degradation of the results. Results of the swarm methodology are compared to those of a deterministic approach, using real data from the Iberian electricity market - MIBEL.

Abstract

Abstract
Software reconfigurability became increasingly relevant to the architectural process due to the crescent dependency of modern societies on reliable and adaptable systems. Such systems are supposed to adapt themselves to surrounding environmental changes with minimal service disruption, if any. This paper introduces an engine that statically applies reconfigurations to (formal) models of software architectures. Reconfigurations are specified using a domain specific language-ReCooPLa-which targets the manipulation of software coordination structures, typically used in service-oriented architectures (soa). The engine is responsible for the compilation of ReCooPLa instances and their application to the relevant coordination structures. The resulting configurations are amenable to formal analysis of qualitative and quantitative (probabilistic) properties.

Abstract
Noncommutative features are introduced into a relativistic quantum field theory model of nuclear matter, the quantum hadrodynamics-I nuclear model (QHD-I). It is shown that the nuclear matter equation of state (NMEoS) depends on the fundamental momentum scale, eta, introduced by the phase-space noncommutativity (NC). Although it is found that NC geometry does not affect the nucleon fields up to O(eta(2)), it affects the energy density, the pressure and other derivable quantities of the NMEoS, such as the nucleon effective mass. Under the conditions of saturation of the symmetric NM under consideration, the estimated value for the noncommutative parameter is root eta approximate to 0.12 MeV/c.