Chaos theory deals with nonlinear and complex systems that are highly sensitive to initial conditions. These systems, while deterministic, are largely unpredictable due to this sensitivity. Lorenz discovered this "butterfly effect" through modeling atmospheric convection. Chaotic systems evolve toward attractors, which can be fixed points, limit cycles, or strange attractors exhibiting fractal geometry. This geometry is seen throughout nature. While chaotic systems cannot be precisely predicted, control methods like Ott-Grebogi-Yorke can influence their behavior. Chaos theory has applications across many domains.
Chaos theory is a mathematical field of study which states that non-linear dynamical systems
that are seemingly random are actually deterministic from much simpler equations. The
phenomenon of Chaos theory was introduced to the modern world by Edward Lorenz in 1972
with conceptualization of ‘Butterfly Effect’. As chaos theory was developed by inputs of
various mathematicians and scientists, it found applications in a large number of scientific
fields.
The purpose of the project is the interpretation of chaos theory which is not as familiar as
other theories. Everything in the universe is in some way or the other under control of Chaos
or product of Chaos. Every motion, behavior or tendency can be explained by Chaos Theory.
The prime objective of it is the illustration of Chaos Theory and Chaotic behavior.
This project includes origin, history, fields of application, real life application and limitations
of Chaos Theory. It explores understanding complexity and dynamics of Chaos.
Chaotic system and its Application in CryptographyMuhammad Hamid
A seminar on Chaotic System and Its application in cryptography specially in image encryption. Slide covers
Introduction
Bifurcation Diagram
Lyapnove Exponent
Chaos theory is a mathematical field of study which states that non-linear dynamical systems
that are seemingly random are actually deterministic from much simpler equations. The
phenomenon of Chaos theory was introduced to the modern world by Edward Lorenz in 1972
with conceptualization of ‘Butterfly Effect’. As chaos theory was developed by inputs of
various mathematicians and scientists, it found applications in a large number of scientific
fields.
The purpose of the project is the interpretation of chaos theory which is not as familiar as
other theories. Everything in the universe is in some way or the other under control of Chaos
or product of Chaos. Every motion, behavior or tendency can be explained by Chaos Theory.
The prime objective of it is the illustration of Chaos Theory and Chaotic behavior.
This project includes origin, history, fields of application, real life application and limitations
of Chaos Theory. It explores understanding complexity and dynamics of Chaos.
Chaotic system and its Application in CryptographyMuhammad Hamid
A seminar on Chaotic System and Its application in cryptography specially in image encryption. Slide covers
Introduction
Bifurcation Diagram
Lyapnove Exponent
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This was an Inter Collegiate and a State Level Contest named SIGMA '08. Won a special prize for this paper. This research emphasized on how simple concepts of Mathematics helps into constructing complex mathematical models for space programming and their individual importance in real time applications.
Its ability to deal with vague systems and its use of linguistic variables. Leads to faster and simpler program development of system controllers. It can be a decision support system tool for managers.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This was an Inter Collegiate and a State Level Contest named SIGMA '08. Won a special prize for this paper. This research emphasized on how simple concepts of Mathematics helps into constructing complex mathematical models for space programming and their individual importance in real time applications.
Its ability to deal with vague systems and its use of linguistic variables. Leads to faster and simpler program development of system controllers. It can be a decision support system tool for managers.
A brief slideshow on the Butterfly effect and its sister theories , the Chaos theory and The Catastrophe theory and how they affect the real world and its outcomes. Meant for beginners.
From last four decades of research it is well-established that all electrophysiological signals are nonlinear, irregular and aperiodic. Since those signals are used in everyday clinical practice as diagnostic tools (EMG, ECG, EEG), a huge progress in using it in making diagnostic more precise and
What does it mean for something to be a dynamical system What is .pdfvikasbajajhissar
What does it mean for something to be a \"dynamical system? What is a \"dynamical equation?
What does it mean if a system is \" sensitive to initial conditions\"? Give one example of a
dynamical system that has been shown to be sensitive to initial conditions.
Solution
Dynamical systems theory is an area of mathematics used to describe the behavior of complex
dynamical systems, usually by employing differential equations or difference equations.
Dynamical systems are mathematical objects used to model physical phenomena whose state
(or instantaneous description) changes over time. These models are used in financial and
economic forecasting, environmental modeling, medical diagnosis, industrial equipment
diagnosis, and a host of other applications.
So a simple, if slightly imprecise, way of describing chaos is \"chaotic systems are
distinguished by sensitive dependence on initial conditions and by having evolution through
phase space that appears to be quite random.\"
In particular, a chaotic dynamical system is generally characterized by
1. Having a dense collection of points with periodic orbits,
2. Being sensitive to the initial condition of the system (so that initially nearby points can evolve
quickly into very different states), a property sometimes known as the butterfly effect, and
3. Being topologically transitive.
Dynamical systems theory is an area of mathematics used to describe the behavior of complex
dynamical systems, usually by employing differential equations or difference equations.
Dynamical systems are mathematical objects used to model physical phenomena whose state
(or instantaneous description) changes over time. These models are used in financial and
economic forecasting, environmental modeling, medical diagnosis, industrial equipment
diagnosis, and a host of other applications.
So a simple, if slightly imprecise, way of describing chaos is \"chaotic systems are
distinguished by sensitive dependence on initial conditions and by having evolution through
phase space that appears to be quite random.\"
In particular, a chaotic dynamical system is generally characterized by
1. Having a dense collection of points with periodic orbits,
2. Being sensitive to the initial condition of the system (so that initially nearby points can evolve
quickly into very different states), a property sometimes known as the butterfly effect, and
3. Being topologically transitive..
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Chaos Theory
1.
2. WHAT IS CHAOS THEORY?
• Branch of mathematics that deals with systems that appear to be orderly
but, in fact, harbor chaotic behaviors. It also deals with systems that appear
to be chaotic, but, in fact, have underlying order.
• Chaos theory is the study of nonlinear, dynamic systems that are highly
sensitive to initial conditions, an effect which is popularly referred to as the
butterfly effect.
• The deterministic nature of these systems does not make them predictable.
This behavior is known as deterministic chaos, or simply chaos.
3. • Edward Lorenz. “Deterministic Nonperiodic Flow”, 1963.
• Lorenz was a meteorologist who developed a mathematical model used to
model the way the air moves in the atmosphere. He discovered the
principle of Sensitive Dependence on Initial Conditions . “Butterfly Effect”.
• The basic principle is that even in an entirely deterministic system the
slightest change in the initial data can cause abrupt and seemingly random
changes in the outcome.
5. CHAOTIC SYSTEMS
Dynamic systems Deterministic systems
Chaotic systems are unstable since they tend
not to resist any outside disturbances but
instead react in significant ways.
6. • Dynamic system: Simplified model
for the time-varying behavior of an
actual system. These systems are
described using differential
equations specifying the rates of
change for each variable.
• Deterministic system: System in
which no randomness is involved in
the development of future states of
the system. This property implies
that two trajectories emerging
from two different close-by initial
conditions separate exponentially
in the course of time.
Chaotic systems are unstable since they tend not to resist any outside
disturbances but instead react in significant ways.
7. • Chaotic systems are common in
nature. They can be found, for
example, in Chemistry, in
Nonlinear Optics (lasers), in
Electronics, in Fluid Dynamics,
etc.
• Many natural phenomena can
also be characterized as being
chaotic. They can be found in
meteorology, solar system,
heart and brain of living
organisms and so on.
8.
9. ATTRACTORS
• In chaos theory, systems
evolve towards states called
attractors. The evolution
towards a specific state is
governed by a set of initial
conditions. An attractor is
generated within the system
itself.
• Attractor: Smallest unit which
cannot itself be decomposed
into two or more attractors
with distinct basins of
attraction.
10. TYPES OF ATTRACTORS
a) Point attractor: There is only one outcome for the system. Death is a point
attractor for living things.
b) Limit cycle or periodic attractor: Instead of moving to a single state as in a
point attractor, the system settles into a cycle.
c) Strange attractor or a chaotic
attractor: double spiral which never
repeats itself. Strange attractors are
shapes with fractional dimension;
they are fractals.
c)
b)
a)
11. FRACTALS
• Fractals are objects that have fractional
dimension. A fractal is a mathematical
object that is self-similar and chaotic.
• Fractals are pictures that result from
iterations of nonlinear equations. Using
the output value for the next input value,
a set of points is produced. Graphing
these points produces images.
12. • Benoit Mandelbrot
• Characteristics: Self-similarity and fractional dimensions.
• Self-similarity means that at every level, the fractal image repeats itself.
Fractals are shapes or behaviors that have similar properties at all levels of
magnification
• Clouds, arteries, veins, nerves, parotid gland ducts, the bronchial tree, etc
• Fractal geometry is the geometry that describes the chaotic systems we find
in nature. Fractals are a language, a way to describe this geometry.
13.
14. THE BUTTERFLY EFFECT
"Sensitive dependence on initial conditions.“
• Butterfly effect is a way of describing
how, unless all factors can be accounted
for, large systems remain impossible to
predict with total accuracy because there
are too many unknown variables to track.
• Ex: an avalanche. It can be provoked with
a small input (a loud noise, some burst of
wind), it's mostly unpredictable, and the
resulting energy is huge.
16. WAYS TO CONTROL CHAOS
The applications of controlling chaos are enormous, ranging from the control
of turbulent flows, to the parallel signal transmission and computation to the
control of cardiac fibrillation, and so forth.
Alter organizational
parameters so that
the range of
fluctuations is limited
Apply small
perturbations to the
chaotic system to try
and cause it to
organize
Change the
relationship between
the organization and
the environment
17.
18. APPLICATIONS OF CHAOS THEORY
Stock
market
Population
dynamics
Biology
Predicting
heart
attacks
Real time
applications
Music and
Arts
Climbing
Random
Number
Generation
19.
20. CHAOS THEORY IN NEGOTIATIONS
Richard Halpern, 2008. Impact of Chaos Theory and Heisenberg Uncertainty
Principle on case negotiations in law
Never rely on someone else's measurement to formulate
a key component of strategy. A small mistake can cause
huge repercussions, better do it yourself.
Keep trying something new, unexpected; sweep the
defence of its feet. Make the system chaotic.
If the process is going the way you wanted, simplify it
as much as possible. Predictability would increase and
chance of blunders is minimized.
If the tide is running against you, add new elements:
complicate. Nothing to lose, and with a little help from
Chaos, everything to gain. You might turn a hopeless
case into a winner.
21. CONCLUSIONS
• Everything in the universe is under control of Chaos or product of Chaos.
• Irregularity leads to complex systems.
• Chaotic systems are very sensitive to the initial conditions, This makes the
system fairly unpredictable. They never repeat but they always have some
order. That is the reason why chaos theory has been seen as potentially
“one of the three greatest triumphs of the 21st century.” In 1991, James
Marti speculated that ‘Chaos might be the new world order.’
• It gives us a new concept of measurements and scales. It offers a fresh way
to proceed with observational data.