121,110 research outputs found
Stronger Nanoscale EM and BEM Solutions by CICT Phased Generators
open1noThe addiction to IC (Infinitesimal Calculus), in the mathematical treatment of EM (electromagnetic) and BEM (bioelectromagnetic) modeling problems, is such that, since the digital computer requires an algebraic formulation of physical laws, it is preferred to discretize the differential equations, rather than considering other more convenient tools for problem mathematical description like, for instance, FDC (Finite Differences Calculus) or more sophisticated algebraic methods. Unfortunately, even traditional FDC, FDTD, etc., approaches are unable to conserve overall system information description. As a matter of fact, current Number Theory and modern Numeric Analysis still use mono-directional interpretation for numeric group generator and relations, so information entropy generation cannot be avoided in current computational algorithm and application. Furthermore, traditional digital computational resources are unable to capture and to manage not only the full information content of a single Real Number R, but even Rational Number Q is managed by information dissipation (e.g. finite precision machine, truncating, rounding, etc.). CICT PG approach can offer an effective and convenient "Science 2.0" universal framework, by considering information not only on the statistical manifold of model states but also on the combinatorial manifold of low-level discrete, phased generators and empirical measures of noise sources, related to experimental high-level overall perturbation. We present an effective example; how to unfold the full information content hardwired into Rational OpeRational (OR) representation (nano-microscale discrete representation) and to relate it to acontinuum framework (meso-macroscale) with no information dissipation. This paper is a relevant contribute towards arbitrary multi-scale computer science and systems biology modeling, to show how CICT PG approach can offer a powerful, effective and convenient "Science 2.0" universal framework to develop innovative, antifragile application and beyond.Fiorini, RodolfoFiorini, Rodolf
Elaboration of the New Paradigm of Interdisciplinary Investigations
In the article, the problem of construction a meta-theory for approaching the complex phenomena of Reality is discussed. The integrated information system is formulated. Such postulate is a suggested basis for creation of a unified methodology of cognition (investigation) which makes it possible to elaborate a new paradigm of interdisciplinary investigations as a separate scientific discipline which has its own methods and special objects. The article will be of interest to philosophers and methodologists of scienc
Physical properties of voltage gated pores
Experiments on single ionic channels have contributed to a large extent to our current view on the function of cell membrane. In these experiments the main observables are the physical quantities: ionic concentration, membrane electrostatic potential and ionic fluxes, all of them presenting large fluctuations. The classical theory of GoldmanâHodkingâKatz assumes that an open channel can be well described by a physical pore where ions follow statistical physics. Nevertheless real molecular channels are active pores with open and close dynamical states. By skipping the molecular complexity of real channels, here we present the internal structure and calibration of two active pore models. These models present a minimum set of degrees of freedom, specifically ion positions and gate states, which follow Langevin equations constructed from a unique potential energy functional and by using standard rules of statistical physics. Numerical simulations of both models are implemented and the results show that they have dynamical properties very close to those observed in experiments of Na and K molecular channels. In particular a significant effect of the external ion concentration on gating dynamics is predicted, which is consistent with previous experimental observations. This approach can be extended to other channel types with more specific phenomenology.Peer ReviewedPostprint (published version
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
Complex Systems: A Survey
A complex system is a system composed of many interacting parts, often called
agents, which displays collective behavior that does not follow trivially from
the behaviors of the individual parts. Examples include condensed matter
systems, ecosystems, stock markets and economies, biological evolution, and
indeed the whole of human society. Substantial progress has been made in the
quantitative understanding of complex systems, particularly since the 1980s,
using a combination of basic theory, much of it derived from physics, and
computer simulation. The subject is a broad one, drawing on techniques and
ideas from a wide range of areas. Here I give a survey of the main themes and
methods of complex systems science and an annotated bibliography of resources,
ranging from classic papers to recent books and reviews.Comment: 10 page
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