New insights into holonomic brain theory: implications for active consciousness

This pioneering research on how specific molecules deep inside our brains form a dynamic information holarchy in phase space, linking mind and consciousness, is not only provocative but also revolutionary. Holonomic is a dynamic encapsulation of the holonic view that originates from the word “holon” and designates a holarchical rather than a hierarchical, dynamic brain organization to encompass multiscale effects. The unitary nature of consciousness being interconnected stems from a multiscalar organization of the brain. We aim to give a holonomic modification of the thermodynamic approach to the problem of consciousness using spatiotemporal intermittency. Starting with quasiparticles as the minimalist material composition of the dynamical brain where interferences patterns between incoherent waves of quasiparticles and their quantum-thermal fluctuations constrain the kinetic internal energy of endogenous molecules through informational channels of the negentropically-derived quantum potential. This indicates that brains are not multifractal involving avalanches but are multiscalar, suggesting that unlike the hologram, where the functional interactions occur in the spectral domain, the spatiotemporal binding is multiscalar because of self-referential amplification occurring via long-range correlative information. The associated negentropic entanglement permeates the unification of the functional information architecture across multiple scales. As such, the holonomic brain theory is suitable for active consciousness, proving that consciousness is not fundamental. The holonomic model of the brain’s internal space is nonmetric and nonfractal. It contains a multiscalar informational structure decoded by intermittency spikes in the fluctuations of the negentropically-derived quantum potential. It is therefore, a more realistic approach than the platonic models in phase space.

Nonlinear dynamics of hourly ozone concentrations: nonparametric short term prediction

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To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis. Off-line time series analysis. Surgical intensive care unit of a teaching hospital. 19 patients requiring hemodynamic monitoring with a pulmonary artery catheter. None. Hemodynamic data were acquired in 1-min intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure, and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low-order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95% confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context. Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.

PurposeThis study aims to develop and validate a simple geometric model of the accelerator head, from which a particle phase space can be calculated for application to fast Monte Carlo dose calculation in real‐time adaptive photon radiotherapy. With this objective in view, the study investigates whether the phase space model can facilitate dose calculations which are compatible with those of a commercial treatment planning system, for convenient interoperability.Materials and methodsA dual‐source model of the head of a Versa HD accelerator (Elekta AB, Stockholm, Sweden) was created. The model used parameters chosen to be compatible with those of 6‐MV flattened and 6‐MV flattening filter‐free photon beams in the RayStation treatment planning system (RaySearch Laboratories, Stockholm, Sweden). The phase space model was used to calculate a photon phase space for several treatment plans, and the resulting phase space was applied to the Dose Planning Method (DPM) Monte Carlo dose calculation algorithm. Simple fields and intensity‐modulated radiation therapy (IMRT) treatment plans for prostate and lung were calculated for benchmarking purposes and compared with the convolution‐superposition dose calculation within RayStation.ResultsFor simple square fields in a water phantom, the calculated dose distribution agrees to within ±2% with that from the commercial treatment planning system, except in the buildup region, where the DPM code does not model the electron contamination. For IMRT plans of prostate and lung, agreements of ±2% and ±6%, respectively, are found, with slightly larger differences in the high dose gradients.ConclusionsThe phase space model presented allows convenient calculation of a phase space for application to Monte Carlo dose calculation, with straightforward translation of beam parameters from the RayStation beam model. This provides a basis on which to develop dose calculation in a real‐time adaptive setting.

Problem. The instruction of physical and mathematical disciplines in higher education institutions employing cutting-edge methods and universal approaches, which seamlessly integrate scientific and engineering activities of future specialists, remains one of the pertinent and priority tasks. The utilization of multidimensional or even infinite-dimensional spaces has become an effective and nearly indispensable tool in mathematical modeling of physical phenomena. This circumstance is directly linked to the increasing level of abstraction and the refinement of mathematical methods. The use of geometric methods inherent in vector algebra and vector analysis as the foundation for studying mechanics, despite their illustrative nature, is losing its relevance. Methods built upon matrix formalism are evolving as substitutes. The matrix framework enables the exploitation of phase space advantages to derive canonical equations of motion for continuous media and electromagnetic field equations in covariant forms. The electromagnetic potential acquires mechanical significance, allowing the utilization of electromechanical analogies at a fundamental level rather than on a merely formal basis, as done in classical electrodynamics. Goal. The aim of this research is to study and justify the advantages of matrix methods for deriving equations of motion for fluids and gases from the canonical equations of mechanics by utilizing a continuum model in phase and physical space. The research is directed towards demonstrating the connection between the internal microscopic and macroscopic motion of fluids and gases, as well as highlighting the potential of matrix formalism both in terms of modeling and the utilization of computational tools. Methodology. The methodological basis for selecting matrix methods lies in the application of tensor analysis and its generalizations for modeling the motion of fluids and gases in physical and phase space. Results. It has been demonstrated that the matrix method, previously applied to classical and relativistic mechanics, allows for the consideration of the molecular structure of the environment by folding the multidimensional phase space of the mechanical system, represented by a particle of the medium, and thereby deriving equations of motion for fluids and gases from the canonical equations of classical mechanics and the equations of momentum balance of the medium. Originality. The combination of canonical equations as a result of applying the conservation law of matter in the form of balance equations in phase space and momentum balance equations in physical space allows for elucidating the relationship between macroscopic and microscopic motions of the medium, as well as the mathematical structure of the equations of motion for fluids and gases. Practical value. The proposed method allows, while remaining within the standard mathematical training offered by technical educational institutions, to effectively formalize the equations of motion for fluids and gases and provide them with an invariant form. By guiding oneself through invariance, both fundamental laws (such as the law of universal gravitation) and partial laws (the generalized Newton's law) can be obtained.

This paper proposes a method of information entropy optimized parameters (IEOP) of pahse space recon struction. First, it establishes an information entropy optimum model in phase space for embedding dimension and delay time by using conditional entropy. It then solves these two parameters with genetic algorithm (GA) simultaneously. IEOP constructs an optimum phase space, which maintains independence of reconstruction coordinate and retains the dynamic characteristics of the original system. In the numerical simulations, results of the Lorenz system and Mackey-Glass system show that it not only determines two parameters at the same time, but also can obtains more information in the optimized phase space, there by improving the performance of chaotic time series prediction.

A general phase-space kinetic model for non-equilibrium charged particle transport through combined localised and delocalised states is presented that accounts for scattering, trapping/detrapping and recombination loss processes in organic and soft-condensed matter. The model takes the form of a generalised Boltzmann equation, for which an analytical solution is found in Fourier-Laplace space. A Chapman-Enskog-type perturbative solution technique is also applied, confirming the analytical results and highlighting the emergence of a density gradient series representation in the weak-gradient hydrodynamic regime. This representation validates Fick's law for this model, providing expressions for the flux transport coefficients of drift velocity and diffusion. By applying Fick's law, a generalised diffusion equation with a unique global time operator is shown to arise that coincides with both the standard diffusion equation and the Caputo fractional diffusion equation in the respective limits of normal and dispersive transport. A subordination transformation is used to efficiently solve the generalised diffusion equation by mapping from the solution of a corresponding classical diffusion equation. From the aforementioned density gradient expansion, we extend Fick's law to consider also the third-order transport coefficient of skewness. This extension is in turn applied to yield a corresponding generalised advection-diffusion-skewness equation. Negative skewness is observed and a physical interpretation is provided in terms of the processes of trapping and detrapping. By analogy with the generalised Einstein relation, a relationship between skewness, diffusion, mobility and temperature is also formed. The phase-space model is generalised further by introducing energy-dependence in the collision, trapping and loss frequencies. The solution of this resulting model is explored indirectly through balance equations for particle continuity, momentum and energy. Generalised Einstein relations (GER) are developed that enable the anisotropic nature of diffusion to be determined in terms of the measured field-dependence of the mobility. Interesting phenomena such as negative differential conductivity (NDC) and recombination heating/cooling are shown to arise from recombination loss processes and the localised and delocalised nature of transport. Fractional generalisations of the GER and mobility are also explored. Finally, a planar organic semiconductor device simulation is presented that makes use of the aforementioned generalised advection-diffusion equation to account for the trapping and detrapping of charge carriers. In this simulation, we use Poisson's equation to account for space-charge effects and Kirchhoff's circuit laws to account for RC effects. These considerations allow for a variety of charge transport experiments to be simulated in a planar geometry, including time of flight (TOF), charge extraction by linearly increasing voltage (CELIV) and resistance-dependent photovoltage (RPV) experiments. The simulation is used to explore a proposed experimental technique for the characterisation of the recombination coefficient, as well as to study what effects traps would have on the measured current.

A new analytical method based on phase space is proposed for cloud computing which with mass nodes and high coupling characteristics.By projecting the cloud computing system into parametric phase space,the changes of nodes parameters can be translated into the motion of points in the phase space.By making use of the resemblance between the motions of mass points which in parametric phase space and in thermodynamic system to define and analyze the general phase space thermodynamic parameters of cloud computing system,and also define the momentum phase space of cloud computing based on the general thermodynamic parameters.This paper establishes a basic theoretical model for the phase space analytical method of cloud computing and generates a phase space scheduling algorithm based on this model.The simulation experiment shows that the analytical model of phase space is helpful to analyze the service behaviour of cloud computing system and the scheduling algorithm is effective to manage the cloud computing system which can maintain the system in equilibrium state with low entropy in parametric phase space.

In this paper we solve the inverse problem for the Curie-Weiss model and its multispecies version when multiple thermodynamic states are present as in the low temperature phase where the phase space is clustered. The inverse problem consists of reconstructing the model parameters starting from configuration data generated according to the distribution of the model. We demonstrate that, without taking into account the presence of many states, the application of the inversion procedure produces very poor inference results. To overcome this problem, we use the clustering algorithm. When the system has two symmetric states of positive and negative magnetizations, the parameter reconstruction can also be obtained with smaller computational effort simply by flipping the sign of the magnetizations from positive to negative (or vice versa). The parameter reconstruction fails when the system undergoes a phase transition: In that case we give the correct inversion formulas for the Curie-Weiss model and we show that they can be used to measure how close the system gets to being critical.

A method of information entropy optimized time delays is proposed for the chaotic time series reconstruction. First, it establishes an information entropy optimum model in phase space for high-dimensional time delays by using conditional entropy. Then solved these parameters using genetic algorithm(GA). This method constructs an optimum phase space, which maintains independence of reconstruction coordinate and retains the dynamic characteristics of the original system. In the numerical simulations, results of the Lorenz system show that it could improve the performance of chaotic time series prediction.

A quark–antiquark effective model is studied in a toroidal topology at finite temperature. The model is described by a Schrödinger equation with linear potential which is embedded in a torus. The following aspects are analyzed: (i) the nonclassicality structure using the Wigner function formalism; (ii) finite temperature and size-effects are studied by a generalization of Thermofield Dynamics written in phase space; (iii) in order to include the spin of the quark, Pauli-like Schrödinger equation is used; (iv) analysis of the size-effect is considered to observe the fluctuation in the ground state. The size effect goes to zero at zero, finite and high temperatures. The results emphasize that the spin is a central aspect for this quark–antiquark effective model.

A simultaneous development of the fundamental research areas of the information theory is needed for efficient development in the information technologies. It is known that for the complicated macroscopic systems information evolution may be shaped on the basis of the principal thermodynamics laws (the second law of thermodynamics, etc). At the same time it is not known whether the fundamentals of the information theory for the macroscopic systems may be applicable to the microscopic systems. The study works out a mathematic model of the discrete phase space adapted to describing the evolution of information (entropy) of the microscopic systems. The discrete phase-space model rests on the indeterminacy principle and fundamental properties of the discrete continuous-time Markovian systems. The Kolmogorov equations represent the main mathematical tools technique. The suggested model refers to the smallest metric scale when the external macroscopic observation is possible. This scale can be viewed as a quasiclassical level. The research results are the following. The structure of the phase space of the elementary signal is revealed. It is demonstrated that the entropy of the microscopic systems increases, i.e. for the microscopic systems the second law of thermodynamics is true. There has been demonstrated transition from the microscopic model to the macroscopic one thus proving the former’s adequacy. The discrete phase-space model is promising in the aspect of further development. For example, it can be applied to the physical systems “particle – field”. The approach represented by the model will allow to study electromagnetic and gravity fields at the quasiclassical level. The above model of the discrete phase space and its application in the study of the evolution of the microscopic systems is a proprietary design of the authors.

The study of the process of changing information in microscopic systems is a rather complex and urgent scientific problem. The difficulty lies in the fact that, due to the microscopic nature of objects, it is impossible to use well-known thermodynamic methods based on the laws of large numbers. One of the possible methods for studying microscopic systems is the use of discrete Markov models with continuous time. Such models are based on the Heisenberg uncertainty principle and adequately describe the interaction between a macroscopic observer and a microscopic system at a quasi-classical level. At the same time, the use of semiclassical models makes it possible to avoid singularities, including those that cannot be eliminated, arising at small distances and high energies. In this paper, a discrete mathematical model of the phase space of an elementary particle will be used to study microscopic objects with a dimension of n=1. As a result of using the model, an original interpretation of the quark structure of hadrons was obtained. In particular, the dynamics of the functioning of quarks in the proton and neutron, the conditions for the formation of Δ resonances (Δ0, Δ+, Δ+, Δ++) are presented. The problem of the absence of free quarks (the phenomenon of confinement) is also considered. The paper considers only the first generation of quarks and the hadrons based on them. On the basis of known experimental data, numerical calculations have been carried out, showing sufficient adequacy of the model. The mathematical model of the discrete phase space, created to study the evolution of information in microscopic systems, is applicable to solving problems of elementary particle physics and can, in some cases, supplement the existing models of the quark structure of hadrons.