RelFinder - Visual Data Web Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in RDF data and makes these relationships interactively explorable. Highlighting and filtering features support visual analysis both on a global and detailed level. Check out the following links for some examples: The RelFinder can easily be configured to work with different RDF datasets. The RelFinder can also be more deeply integrated with your project: Integrating the RelFinder See the following examples of how the RelFinder is integrated into other projects: Ontotext applies the RelFinder to enable an exploration of relationships in the biomedical domain. The RelFinder is readily configured to access RDF data of the DBpedia project and only requires a Flash Player plugin to be executed (which is usually already installed in web browsers). All tools on this website are research prototypes that might contain errors.
Swarm intelligence Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Taxonomy of Swarm Intelligence Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Natural vs. Scientific vs. Natural/Scientific: Foraging Behavior of Ants Artificial/Scientific: Clustering by a Swarm of Robots Several ant species cluster corpses to form cemeteries. Natural/Engineering: Exploitation of collective behaviors of animal societies Artificial/Engineering: Swarm-based Data Analysis References E. J.
From SHIFT: My Three-Part Series on Complexity and Collapse Graphic courtesy of SHIFT Magazine (click on the graphic to view full-screen) The third and final part of my series of articles on complexity and collapse is now up on the SHIFT Magazine site. Here’s a synopsis of all three parts, with links to the online versions of the articles: Part One: The Energy Predicament A look at our global energy and resource systems, and the complex relationship between resource prices, regulation, exploration, supply and demand, and how they are pushing us towards disastrous resource exhaustion. Part Two: The Economic Predicament The complexities of our global economic systems, and an exploration of whether, although it won’t ‘save’ civilization, the dismantling or crumbling of our current industrial growth economy, sooner rather than later, might lessen the hardship and suffering of drastic climate change that we and our descendants are likely to face. Part Three: The Ecological Predicament
Twelve leverage points The twelve leverage points to intervene in a system were proposed by Donella Meadows, a scientist and system analyst focused on environmental limits to economic growth. The leverage points, first published in 1997, were inspired by her attendance at a North American Free Trade Agreement (NAFTA) meeting in the early 1990s where she realized that a very large new system was being proposed but the mechanisms to manage it were ineffective. Meadows, who worked in the field of systems analysis, proposed a scale of places to intervene in a system. Awareness and manipulation of these levers is an aspect of self-organization and can lead to collective intelligence. Her observations are often cited in energy economics, green economics and human development theory. She claimed we need to know about these shifts, where they are and how to use them. For example, one might consider a lake or reservoir, which contains a certain amount of water. Leverage points to intervene in a system[edit] 12. 11. 10.
Explaining biological strategy (2) The first step in understanding To be able to understand how biological systems can create order out of disorder, it is necessary to first shake off all the preconceived ideas that have been programmed into our minds by conventional education. To do this we have to go back almost a century (1909), to an abstract model first proposed by the great German mathematician, David Hilbert (1862-1943). This sounds ridiculous, because it doesn't seem possible that anyone can visualize a space with infinite dimensions, but, dimensions can also be called parameters. How this creates an order in this space can be imagined if you take any single parameter and imagine every item with that same parameter as being strung out in a line along it. This would apply to all parameters, so you can think of the space as being crisscrossed by an infinite number of parameter lines that can intersect with one another. The importance of this mental model is the paradigm shift it brings about. Nature's algorithm
Feedback "...'feedback' exists between two parts when each affects the other.. Feedback is also a synonym for: Feedback signal – the measurement of the actual level of the parameter of interest.Feedback mechanism – the action or means used to subsequently modify the gap.Feedback loop – the complete causal path that leads from the initial detection of the gap to the subsequent modification of the gap. History[edit] Self-regulating mechanisms have existed since antiquity, and the idea of feedback had started to enter economic theory in Britain by the eighteenth century, but it wasn't at that time recognized as a universal abstraction and so didn't have a name.[2] The verb phrase "to feed back", in the sense of returning to an earlier position in a mechanical process, was in use in the US by the 1860s,[3][4] and in 1909, Nobel laureate Karl Ferdinand Braun used the term "feed-back" as a noun to refer to (undesired) coupling between components of an electronic circuit.[5] Types[edit] Applications[edit]
Systems theory Systems theory is the interdisciplinary study of systems in general, with the goal of elucidating principles that can be applied to all types of systems at all nesting levels in all fields of research.[citation needed] The term does not yet have a well-established, precise meaning, but systems theory can reasonably be considered a specialization of systems thinking; alternatively as a goal output of systems science and systems engineering, with an emphasis on generality useful across a broad range of systems (versus the particular models of individual fields). A central topic of systems theory is self-regulating systems, i.e. systems self-correcting through feedback. Self-regulating systems are found in nature, including the physiological systems of our body, in local and global ecosystems, and in climate—and in human learning processes (from the individual on up through international organizations like the UN).[3] Overview[edit] Examples of applications[edit] Systems biology[edit]
Publications by subject - Marchetti Web Archive Marchetti, C., 1969Round Table on Direct Production of Hydrogen with Nuclear Heat, EUR/C-IS/1062/1/69.e., Commission of the European Community, EURATOM Joint Research Center, Ispra, Italy[Full text, part1: scan PDF 974 Kb] [part2: scan PDF 1369 Kb] [part3: scan PDF 1552 Kb] Marchetti, C., and de Beni, G., 1970Hydrogen, Key to the Energy Market, Scientific and Technical Review of the European Communities, Euro Spectra , IX (2):14--18[Full text, scan PDF 312 Kb] Marchetti, C., 1971Hydrogen, Master Key to the Energy Market, Scientific and Technical Review of the European Communities, Euro Spectra , X (4):117--130 Marchetti, C., Donea, J., and Simon, R., 1972Considerations on a Dual Purpose Dual Temperature Process for Producing D2O, Internal Report, Commission of the European Community, EURATOM Joint Research Center, Ispra, Italy Marchetti, C., 1973Hydrogen and Energy, Chemical Economy & Engineering Review, 5(1):7--15, January[Full text, scan PDF 968 Kb]