No abstract

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

Supplementary data are available at Bioinformatics online.

No abstract

Some properties of social networks (e.g., the mixing patterns and the community structure) appear deeply influenced by the individual perception of people. In this work we map behaviors by considering similarity and popularity of people, also assuming that each person has his/her proper perception and interpretation of similarity. Although investigated in different ways (depending on the specific scientific framework), from a computational perspective similarity is typically calculated as a distance measure. In accordance with this view, to represent social network dynamics we developed an agent-based model on top of a hyperbolic space on which individual distance measures are calculated. Simulations, performed in accordance with the proposed model, generate small-world networks that exhibit a community structure. We deem this model to be valuable for analyzing the relevant properties of real social networks.

Innovation is a key ingredient for the evolution of several systems, including social and biological ones. Focused investigations and lateral thinking may lead to innovation, as well as serendipity and other random discovery processes. Some individuals are talented at proposing innovation (say innovators), while others at deeply exploring proposed novelties, at getting further insights on a theory, or at developing products, services, and so on (say developers). This separation in terms of innovators and developers raises an issue of paramount importance: under which conditions a system is able to maintain innovators? According to a simple model, this work investigates the evolutionary dynamics that characterize the emergence of innovation. In particular, we consider a population of innovators and developers, in which agents form small groups whose composition is crucial for their payoff. The latter depends on the heterogeneity of the formed groups, on the amount of innovators they include, and on an award-factor that represents the policy of the system for promoting innovation. Under the hypothesis that a "mobility" effect may support the emergence of innovation, we compare the equilibria reached by our population in different cases. Results confirm the beneficial role of "mobility", and the emergence of further interesting phenomena.Innovation 1 appears to be an ubiquitary concept, which applies to a variety of contexts, including economy, physics, sociology, ethology, biology, and linguistics 2-15 . A typical setting able to support innovation requires a component, e.g., a research group, whose specific goal is to produce a breakthrough -which in turn is a precondition to find out new technologies, services, and even forms of art 16 . As such, the capability of producing innovation becomes also an indicator of the wellness of a society [17][18][19] . On the other hand, in its pure form, innovation is in fact an unexpected outcome, most likely due to random guessing, lateral thinking or serendipity. The most prominent examples of this kind of mechanism can be found in science, where innovation is fundamental for promoting groundbreaking intuitions 20 . In this context innovation is motivated by the goal of dealing with unsolved problems and sometimes carries out, as side-effect, the emergence of new research fields. A relevant and recent example is constituted by the modern and vibrant field of complex networks [21][22][23][24] , which is deeply affecting several scientific sectors (just to cite few among many: social networks 25 , epidemiology 26 , genomics 27 , neuroscience 28,29 , and financial systems 30 ). Regardless from the underlying context, the ability of identifying objective measures for innovation is a primary issue. A relevant discipline for this purpose is scientometrics, which is concerned with studying, measuring and analysing science, technology and innovation. Narrowing to scientific publications, scientometrics is the reference discipline for measuring their impact on the corresponding rese...

Performance measures are used in various stages of the process aimed at solving a classifi-\ud cation problem. Unfortunately, most of these measures are in fact biased, meaning that they\ud strictly depend on the class ratio – i.e. on the imbalance between negative and positive samples.\ud After pointing to the source of bias for the best known measures, novel unbiased measures\ud are defined which are able to capture the concepts of discriminant and characteristic capability.\ud The combined use of these measures can give important information to researchers\ud involved in machine learning or pattern recognition tasks, in particular for classifier performance\ud assessment and feature selection

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such an evolution is pushing a paradigm shift that is one of the most important challenges in power network design: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system which includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks. The aim of this paper is to discuss, in an integrated and organized manner, the state of the art while focusing on the need for interaction between different disciplines and highlighting how innovative and future-proof outcomes of both research and practice can only emerge from a coordinated design of all the layers in the smart distribution network architecture.