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Fractal Space - incredible adventures in the first person. Beware of lasers, avoid moving saws and Dodge giant crushers. At your disposal there is a special Taser gun and a jetpack. With the help of which you can bypass dangerous areas with maximum safety. Mysterious space station, full of dangerous traps and has lots of rooms that you must pass to your hero.Features:Fly around spaceUse a Taser Gun to activate switches remotelyFind orphaned records, to unravel the mystery of the space stationFull controller support

The cheapest graphics card you can play it on is an NVIDIA GeForce GT 720. But, according to the developers the recommended graphics card is an NVIDIA GeForce GTX 750. In terms of game file size, you will need at least 4 GB of free disk space available. To play Fractal Space you will need a minimum CPU equivalent to an Intel Core 2 Duo Q6867. The minimum memory requirement for Fractal Space is 2 GB of RAM installed in your computer. If possible, make sure your have 4 GB of RAM in order to run Fractal Space to its full potential.

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Classic Space Filling Curves (SFC), originally described by the Italian mathematician G. Peano in 1890, is based on the fractal theory. The space was divided into adjacent Sierpinski triangles by fractal method until each point in the space can be expressed in a continuous line (0, 1) by space filling curves. The space filling curve represents multidimensional space with one-dimensional space line. The applications of fractal theory have played an important role in image processing and multidimensional data index and so forth.

There are many kinds of space filling curves, and the widely used are Sierpinski SFC and Hilbert SFC. Sierpinski SFC is based on the Sierpinski triangle subdivision, and the Hilbert SFC is based on rectangular segmentation. Honeycomb structure is a new fractal hexagonal lattice structure which can cover any two-dimensional space with seamless lattice [12, 13]. The geometry of honeycomb structure has been extensively studied and has been proven to have high mechanical stability and high thermal efficiency and covering efficiency, which has been applied in many fields, such as nanostructure materials and structures.

For any two-dimensional space, we can get the complete coverage for it by different level of honeycomb structure with proper radius of hexagon cell. The honeycomb structure can be divided into 7 blocks. We give each block an ID: the central block is 0 and six surrounding blocks are 1 to 6, respectively; with fractal method, we can give each cell an ID, as shown in Figure 2. With the cell ID, we can easily find the exact block, sub-block and the cell position at the whole space. The hierarchic management of honeycomb is shown in Figure 3.

This paper proposes a fractal hierarchic honeycomb structure based HOA to solve VRP. The algorithm first divides the whole space into sequenced cells by honeycomb structure to get the initial solution of VRP, uses GA to improve an initial solution, and gets the final solution for VRP. Experimental software is developed based on the proposed HOA, and computational experiments are conducted based on Solomon benchmark. The test results demonstrate the superiority of the proposed algorithm.

Fight for dominance on a collection of interconnected space platforms. The path from the center forks in a fractal pattern, ensuring both an engaging battle for territorial expansion and fair play for all players.

The Hilbert curve (also known as the Hilbert space-filling curve) is a continuous fractal space-filling curve first described by the German mathematician David Hilbert in 1891,[1] as a variant of the space-filling Peano curves discovered by Giuseppe Peano in 1890.[2]

In the past, in order to achieve higher data transmission capacity, communication networks have embraced different modulation and multiplexing schemes1,2. Commonly used multiplexing techniques in optical fiber communication today include space-division multiplexing (SDM) or spatial multiplexing, wavelength-division multiplexing (WDM) using disjoint frequency bins, orthogonal frequency division multiplexing (OFDM) or coherent WDM (CoWDM) using spectrally overlapping yet orthogonal subcarriers, and polarization-division multiplexing (PDM) using both orthogonal polarizations supported by a single-mode fiber for independent bit streams1,3. In real applications, multiplexing techniques are combined to further increase the channel capacity4,5,6. Among these approaches, SDM has recently drawn sufficient attention as the space dimension is still not fully developed7,8, particularly with free-space optical (FSO) communication systems.

In this paper, we demonstrate the novel paradigm of diffractal space-division multiplexing (DSDM). Diffractals are the waves that have encountered fractals, which are visually-complex iterations of simple patterns23,24,25,26. Fractal geometries and diffractal scattering have attracted widespread attention in many branches of science and engineering such as digital image processing, especially image compression27,28 and antenna design29,30,31,32,33. Such applications exploit a high level of information redundancy and sparsity, which stems from the self-similar geometry of fractals or diffractals.

A Hilbert curve (also known as a Hilbert space-filling curve) is a continuous fractal space-filling curve first described by the German mathematician David Hilbert in 1891. It is a variant of the space-filling Peano curves discovered by Giuseppe Peano in 1890.

This app recursively generates both 2D and 3D Hilbert space-filling fractal pseudo curves. The curve level is specified by the user and a delay factor can also be supplied which results in an animation like curve generation. The scale factor along the X and Y need not be the same.

This is the first detailed account of a new approach to microphysics based on two leading ideas: (i) the explicit dependence of physical laws on scale encountered in quantum physics, is the manifestation of a fundamental principle of nature, scale relativity. This generalizes Einstein's principle of (motion) relativity to scale transformations; (ii) the mathematical achievement of this principle needs the introduction of a nondifferentiable space-time varying with resolution, i.e. characterized by its fractal properties.

The author discusses in detail reactualization of the principle o