Production of a diluted solid tracer by dry co-grinding in a tumbling ball mill

This paper presents a study on the production by co-grinding of a diluted solid tracer, sized less than 10 mm and containing less than 2 wt. % of active product, used in the field of grounds contamination and decontamination. Co-grinding was performed in a tumbling ball mill and permits to produce easily a diluted tracer without implementing several apparatus. The two products were ground separately first and then together. The follow-up of the particles size and morphology, as well as the modelling of the grinding kinetics have permitted to propose a mechanism by which the diluted solid tracer is produced. The influence of the operating conditions (nature and initial size of the diluting medium, ball and powder filling rates, proportion of the polluting tracer) on products grinding was studied. Thus, we have defined optimum co-grinding conditions permitting to produce a tracer offering the required properties. These ones are classical for tumbling ball mills. This kind of mill is very interesting since its sizes can easily be extrapolated to answer to an industrial demand

Evolution: Complexity, uncertainty and innovation

Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above. This could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-level – and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short term – but will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly

The prisoners dilemma on a stochastic non-growth network evolution model

We investigate the evolution of cooperation on a non - growth network model with death/birth dynamics. Nodes reproduce under selection for higher payoffs in a prisoners dilemma game played between network neighbours. The mean field characteristics of the model are explored and an attempt is made to understand the size dependent behaviour of the model in terms of fluctuations in the strategy densities. We also briefly comment on the role of strategy mutation in regulating the strategy densties.Comment: 8 pages, 8 figure

Learning in evolutionary environments

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Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Simulation models of technological innovation: A Review

The use of simulation modelling techniques in studies of technological innovation dates back to Nelson and Winter''s 1982 book "An Evolutionary Theory of Economic Change" and is an area which has been steadily expanding ever since. Four main issues are identified in reviewing the key contributions that have been made to this burgeoning literature. Firstly, a key driver in the construction of computer simulations has been the desire to develop more complicated theoretical models capable of dealing with the complex phenomena characteristic of technological innovation. Secondly, no single model captures all of the dimensions and stylised facts of innovative learning. Indeed this paper argues that one can usefully distinguish between the various contributions according to the particular dimensions of the learning process which they explore. To this end the paper develops a taxonomy which usefully distinguishes between these dimensions and also clarifies the quite different perspectives underpinning the contributions made by mainstream economists and non-mainstream, neo-Schumpeterian economists. This brings us to a third point highlighted in the paper. The character of simulation models which are developed are heavily influenced by the generic research questions of these different schools of thought. Finally, attention is drawn to an important distinction between the process of learning and adaptation within a static environment, and dynamic environments in which the introduction of new artefacts and patterns of behaviour change the selective pressure faced by agents. We show that modellers choosing to explore one or other of these settings reveal their quite different conceptual understandings of "technological innovation".economics of technology ;

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Adventures in Time and Space: What Shapes Behavioural Decisions in Drosophila melanogaster?

Variation in behaviour can be observed both between individuals, based on their condition and experience as well as between populations due to sources of heterogeneity in the environment. These behavioural differences have evolved as a result of natural and sexual selection where different strategies may be favoured depending on the costs and benefits associated with those behaviours. In this thesis I examine two sources of heterogeneity within the environment and their behavioural consequences: how spatial complexity mediates sexual selection over time, and how inter and intraspecific signals and individual condition influence social oviposition behaviour. By increasing spatial complexity, we were able to manipulate male-female interaction rate which in turn influenced courtship behaviour and male-induced harm, the consequence of this was an increase in female fecundity especially in the later days of the assay and no change in offspring fitness. These results supported the idea that spatial complexity is able to mediate sexual selection through decreased harm to females. Oviposition decisions are of high consequence to an individual’s fitness and can be shaped by many environmental conditions. Instead of expending energy to evaluate all their different costs and benefits of the conditions of potential oviposition sites females can chose to rely on the signals left by others, in this case it would be beneficial for females to identify signals most like themselves. While we found females oviposited with individuals of the same species and diet, when given the option they showed more interest in and laid more eggs on media that previously held virgin males, bringing into question many assumptions of copying behaviour. In Drosophila melanogaster the only control females have over their offspring is who they mate with and where they oviposit their eggs, thus, these two factors can have a long-lasting impact on individual fitness for future generations. It is also important to consider how the standard lab environment may be shaping these behaviours, and the consequences this has for the evolutionary trajectory of lab populations