BOOLEAN NETWORKS WITH MEMORY

2008 ◽  
Vol 18 (12) ◽  
pp. 3799-3814 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
LARRY BULL
Keyword(s):  
Boolean Networks ◽  
Time Step ◽  
Different Types ◽  
A Cell ◽  
With Memory ◽  
In Cells

In standard Boolean Networks (BN) the new state of a cell depends upon the neighborhood configuration only at the preceding time step. The effect of implementing memory of different types in cells of BN with different degrees of random rewiring is studied in this article.

EFFECT OF MEMORY ON BOOLEAN NETWORKS WITH DISORDERED DYNAMICS: THE K = 4 CASE

2007 ◽  
Vol 18 (08) ◽  
pp. 1313-1327 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
JUAN PABLO CÁRDENAS
Keyword(s):  
Cellular Automata ◽  
Boolean Networks ◽  
Ordered Structure ◽  
Time Step ◽  
Damage Spreading ◽  
A Cell ◽  
In Cells

In standard Cellular Automata (CA) and Boolean Networks (BN), the new state of a cell depends on the neighborhood configuration only at the preceding time step. The effect of implementing memory in cells on CA, CA on networks and BN with different degrees of random rewiring is studied in this paper paying attention to the particular case of four inputs. As a rule, memory in cells induces a moderation in the rate of changing cells and in the damage spreading, albeit in the latter case memory turns out ineffective in the control of the damage as the wiring network moves away of the ordered structure that features proper CA.

TWO-DIMENSIONAL CELLULAR AUTOMATA WITH MEMORY: PATTERNS STARTING WITH A SINGLE SITE SEED

2002 ◽  
Vol 13 (01) ◽  
pp. 49-65 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
MARGARITA MARTÍN
Keyword(s):  
Cellular Automata ◽  
Single Site ◽  
Two Dimensional ◽  
Time Step ◽  
A Cell ◽  
With Memory

Standard Cellular Automata (CA) are ahistoric (memoryless), i.e., the new state of a cell depends on its neighborhood configuration only at the preceding time step. The effect of keeping ahistoric memory of all past iterations in two-dimensional CA, featuring each cell by its most frequent state is analyzed in this work.

ONE-DIMENSIONAL r=2 CELLULAR AUTOMATA WITH MEMORY

2004 ◽  
Vol 14 (09) ◽  
pp. 3217-3248 ◽  
Author(s):  
RAMÓN ALONSO-SANZ
Keyword(s):  
Cellular Automata ◽  
Time Step ◽  
One Dimensional ◽  
The Past ◽  
A Cell ◽  
Factor Α ◽  
Update Rules ◽  
With Memory ◽  
Standard Framework ◽  
Dimensional Range

Standard Cellular Automata (CA) are ahistoric (memoryless): i.e. the new state of a cell depends on the neighborhood configuration only at the preceding time step. This article introduces an extension to the standard framework of CA by considering automata implementing memory capabilities. While the update rules of the CA remains the same, each site remembers a weighted mean of all its past states, with a decreasing weight of states farther in the past. The historic weighting is defined by a geometric series of coefficients based on a memory factor (α). This paper considers the time evolution of one-dimensional range-two CA with memory.

ONE-DIMENSIONAL CELLULAR AUTOMATA WITH MEMORY: PATTERNS FROM A SINGLE SITE SEED

2002 ◽  
Vol 12 (01) ◽  
pp. 205-226 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
MARGARITA MARTÍN
Keyword(s):  
Cellular Automata ◽  
Weighted Mean ◽  
Time Step ◽  
Memory Factor ◽  
One Dimensional ◽  
A Cell ◽  
Factor Α ◽  
Update Rules ◽  
With Memory ◽  
Standard Framework

Standard Cellular Automata (CA) are ahistoric (memoryless): i.e. the new state of a cell depends on the neighborhood configuration only at the preceding time step. This article introduces an extension to the standard framework of CA by considering automata implementing memory capabilities. While the update rules of the CA remain the same, each site remembers a weighted mean of all its past states. The historic weighting is defined by a geometric series of coefficients based on a memory factor (α). The time evolution of one-dimensional CA with memory starting with a single live cell is studied. It is found that for α ≤ 0.5, the evolution corresponds to the standard (nonweighted) one, while for α > 0.5, there is a gradual decrease in the width of the evolving pattern, apart from discontinuities which sometimes may occur for certain rules and α values.

THE EFFECT OF MEMORY IN CELLULAR AUTOMATA ON SCALE-FREE NETWORKS: THE $\overline{K}=4$ CASE

2008 ◽  
Vol 18 (08) ◽  
pp. 2477-2486 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
JUAN PABLO CÁRDENAS
Keyword(s):  
Cellular Automata ◽  
Time Step ◽  
Change Rate ◽  
Scale Free ◽  
Scale Free Networks ◽  
A Cell ◽  
In Cells

In standard cellular automata the new state of a cell depends on the neighborhood configuration solely at the preceding time step. This study analyzes the effect of implementing memory in cells of cellular automata on networks with scale-free topology. As a rule, embedding memory in cells induces a notable depletion in the cell change rate, but it is not so effective in controlling the spread of damage.

MULTIFRACTAL PROPERTIES OF R90 CELLULAR AUTOMATON WITH MEMORY

2004 ◽  
Vol 15 (10) ◽  
pp. 1461-1470 ◽  
Author(s):  
JUAN R. SÁNCHEZ ◽  
RAMÓN ALONSO-SANZ
Keyword(s):  
Cellular Automata ◽  
Complex Behavior ◽  
Time Step ◽  
One Dimensional ◽  
Dynamical Characteristics ◽  
Historic Memory ◽  
A Cell ◽  
Update Rules ◽  
With Memory ◽  
Standard Framework

Standard Cellular Automata (CA) are ahistoric (memoryless Markov process), i.e., the new state of a cell depends on the neighborhood configuration only at the preceding time step. This article considers the fractal and multifractal properties of an extension to the standard framework of CA implemented by the inclusion of memory capabilities. Thus, in CA with memory, while the update rules of the CA remain unaltered, historic memory of all past iterations is retained by featuring each cell by a summary of all its past states. A study is made of the effect of historic memory on the multifractal dynamical characteristics of one-dimensional cellular automata operating under one of the most studied rules, rule 90, which is well known to display a rich complex behavior.

Glycans in scaffold design in tissue reconstruction

2021 ◽  
pp. 088391152199784
Author(s):  
Nipun Jain ◽  
Shashi Singh
Keyword(s):  
Cell Attachment ◽  
Low Cost ◽  
Growth Conditions ◽  
Downstream Signaling ◽  
Cell Carrier ◽  
Wide Range ◽  
Proliferation And Differentiation ◽  
Different Types ◽  
Tissue Reconstruction ◽  
A Cell

Development of an artificial tissue by tissue engineering is witnessed to be one of the long lasting clarified solutions for the damaged tissue function restoration. To accomplish this, a scaffold is designed as a cell carrier in which the extracellular matrix (ECM) performs a prominent task of controlling the inoculated cell’s destiny. ECM composition, topography and mechanical properties lead to different types of interactions between cells and ECM components that trigger an assortment of cellular reactions via diverse sensing mechanisms and downstream signaling pathways. The polysaccharides in the form of proteoglycans and glycoproteins yield better outcomes when included in the designed matrices. Glycosaminoglycan (GAG) chains present on proteoglycans show a wide range of operations such as sequestering of critical effector morphogens which encourage proficient nutrient contribution toward the growing stem cells for their development and endurance. In this review we discuss how the glycosylation aspects are of considerable importance in everyday housekeeping functions of a cell especially when placed in a controlled environment under ideal growth conditions. Hydrogels made from these GAG chains have been used extensively as a resorbable material that mimics the natural ECM functions for an efficient control over cell attachment, permeability, viability, proliferation, and differentiation processes. Also the incorporation of non-mammalian polysaccharides can elicit specific receptor responses which authorize the creation of numerous vigorous frameworks while prolonging the low cost and immunogenicity of the substance.

scholarly journals Design and interpretation of cell trajectory assays

2013 ◽  
Vol 10 (88) ◽  
pp. 20130630 ◽  
Author(s):  
Lucie G. Bowden ◽  
Matthew J. Simpson ◽  
Ruth E. Baker
Keyword(s):  
Cell Biology ◽  
Time Interval ◽  
Short Time Interval ◽  
Trajectory Data ◽  
Time Intervals ◽  
Different Types ◽  
Long Time ◽  
A Cell ◽  
Cell Trajectory ◽  
Short Time

Cell trajectory data are often reported in the experimental cell biology literature to distinguish between different types of cell migration. Unfortunately, there is no accepted protocol for designing or interpreting such experiments and this makes it difficult to quantitatively compare different published datasets and to understand how changes in experimental design influence our ability to interpret different experiments. Here, we use an individual-based mathematical model to simulate the key features of a cell trajectory experiment. This shows that our ability to correctly interpret trajectory data is extremely sensitive to the geometry and timing of the experiment, the degree of motility bias and the number of experimental replicates. We show that cell trajectory experiments produce data that are most reliable when the experiment is performed in a quasi-one-dimensional geometry with a large number of identically prepared experiments conducted over a relatively short time-interval rather than a few trajectories recorded over particularly long time-intervals.

scholarly journals Finite element analysis of heat sink in term of thermal and temperature distribution with different chip power input

2018 ◽  
Vol 7 (2.15) ◽  
pp. 90
Author(s):  
Mazlan Mohamed ◽  
Mohd Nazri Omar ◽  
Mohamad Shaiful Ashrul Ishak ◽  
Rozyanty Rahman ◽  
Muhamad Fahmi Mohd Roslan ◽  
...  
Keyword(s):  
Temperature Distribution ◽  
Heat Sink ◽  
3D Model ◽  
Power Input ◽  
Power Source ◽  
Experimental Setup ◽  
Time Step ◽  
Element Analysis ◽  
Different Types ◽  
Lower Temperature

This paper presents the simulation of heat sink by using Workbench 18.0 Software to simulate the temperature distribution at different chip power input. 3D model of heat sink is generated using Design Modeler using the same dimension with experimental setup. The study was made for a heat sink mounted on the power source (Chip) under different types of chip powers. The results are presented in terms of temperature distribution when chip powers have been increased from 1 W to 10 W. The temperature distribution is been observed and it was found that the temperature distribution of the heat sink has lower temperature when power source at 1 W and increase significantly when the power source rise up to 10 W. The increase the temperature of heat sink is from 30.8ºC up to 96.2ºC estimated to be 212% the increase of temperature. The simulation also been verify by using different time step use during the simulation and using grid independency test to ensure the simulation result is accurate. 

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