This document provides an overview of medical physics and biophysics. It discusses key topics in systemology including the definition of a system, system classification, inputs and outputs, and modeling biological systems. The key points are:
1. Biophysics uses physical laws to explain phenomena in biology. It studies systems at the molecular, cellular, and whole organism levels.
2. A system is a set of interconnected elements that function as a whole. Systems can be simple or complex, deterministic or probabilistic.
3. Inputs enter a system from the environment and outputs exit the system, connecting it to the environment. Models are used to test and understand real systems.
Abstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand, especially, when new component types may appear and present component types may vanish com- pletely. Inspired by Fontana and Buss (Bull. Math. Biol., 56, 1–64) we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organisations, providing a new view on the system’s structure. The sec- ond part connects dynamics with the set of organisations, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of our theory is underlined by a theorem that says that given a dif- ferential equation describing the chemical dynamics of the network, then every stationary state is an instance of an organisation. For demonstration, the theory is applied to a small model of HIV-immune system interaction by Wodarz and Nowak (Proc. Natl. Acad. USA, 96, 14464–14469) and to a large model of the sugar metabolism of E. Coli by Puchalka and Kierzek (Biophys. J., 86, 1357–1372). In both cases organisations where uncovered, which could be related to functions.
The document provides an overview of cybernetics and its applications in biology and medicine. It discusses key concepts in cybernetics including feedback, control systems, information theory, and modeling living systems as cybernetic systems. The document also summarizes how cybernetics is applied in areas like biomedical engineering to model regulatory systems in the body and support medical applications.
This document defines systems and types of systems. It discusses:
- Systems have organized parts that work together towards an overall goal, with inputs, processes, outputs, and feedback.
- Types of systems include deterministic and probabilistic, open and closed, natural and manufactured, social and machine.
- Deterministic systems operate predictably while probabilistic systems have uncertain behavior. Open systems interact with the environment but closed systems do not.
THE ACTIVE CONTROLLER DESIGN FOR ACHIEVING GENERALIZED PROJECTIVE SYNCHRONIZA...ijait
This paper discusses the design of active controllers for achieving generalized projective synchronization (GPS) of identical hyperchaotic Lü systems (Chen, Lu, Lü and Yu, 2006), identical hyperchaotic Cai systems (Wang and Cai, 2009) and non-identical hyperchaotic Lü and hyperchaotic Cai systems. The synchronization results (GPS) for the hyperchaotic systems have been derived using active control method and established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active control method is very effective and convenient for achieving the GPS of the
hyperchaotic systems addressed in this paper. Numerical simulations are provided to illustrate the effectiveness of the GPS synchronization results derived in this paper.
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
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
This paper derives new results for the global chaos synchronization of identical hyperchaotic Qi systems (2008), identical hyperchaotic Jha systems (2007) and non-identical hyperchaotic Qi and Jha systems. Active nonlinear control is the method adopted to achieve the complete synchronization of the identical and different hyperchaotic Qi and Jha systems. Our stability results derived in this paper are established using Lyapunov stability theory. Numerical simulations are shown to validate and illustrate the effectiveness of the synchronization results derived in this paper.
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
1) The document describes using active nonlinear control to achieve global chaos synchronization between identical and non-identical hyperchaotic systems.
2) It specifically derives new results for synchronizing identical hyperchaotic Qi and Jha systems, as well as non-identical hyperchaotic Qi and Jha systems.
3) The synchronization is achieved through designing appropriate active nonlinear controllers and proving global exponential stability of the synchronization error dynamics using Lyapunov stability theory. Numerical simulations validate the theoretical results.
ANTI-SYNCHRONIZATION OF HYPERCHAOTIC BAO AND HYPERCHAOTIC XU SYSTEMS VIA ACTI...IJCSEIT Journal
This paper investigates the anti-synchronization of identical hyperchaotic Bao systems (Bao and Liu,
2008), identical hyperchaotic Xu systems (Xu, Cai and Zheng, 2009) and non-identical hyperchaotic Bao
and hyperchaotic Xu systems. Active nonlinear control has been deployed for the anti- synchronization of
the hyperchaotic systems addressed in this paper and the main results have been established using
Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active
nonlinear control method is very effective and convenient to achieve anti-synchronization of identical and
non-identical hyperchaotic Bao and hyperchaotic Xu systems. Numerical simulations have been provided to
validate and demonstrate the effectiveness of the anti-synchronization results for the hyperchaotic Cao and
hyperchaotic Xu systems.
Abstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand, especially, when new component types may appear and present component types may vanish com- pletely. Inspired by Fontana and Buss (Bull. Math. Biol., 56, 1–64) we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organisations, providing a new view on the system’s structure. The sec- ond part connects dynamics with the set of organisations, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of our theory is underlined by a theorem that says that given a dif- ferential equation describing the chemical dynamics of the network, then every stationary state is an instance of an organisation. For demonstration, the theory is applied to a small model of HIV-immune system interaction by Wodarz and Nowak (Proc. Natl. Acad. USA, 96, 14464–14469) and to a large model of the sugar metabolism of E. Coli by Puchalka and Kierzek (Biophys. J., 86, 1357–1372). In both cases organisations where uncovered, which could be related to functions.
The document provides an overview of cybernetics and its applications in biology and medicine. It discusses key concepts in cybernetics including feedback, control systems, information theory, and modeling living systems as cybernetic systems. The document also summarizes how cybernetics is applied in areas like biomedical engineering to model regulatory systems in the body and support medical applications.
This document defines systems and types of systems. It discusses:
- Systems have organized parts that work together towards an overall goal, with inputs, processes, outputs, and feedback.
- Types of systems include deterministic and probabilistic, open and closed, natural and manufactured, social and machine.
- Deterministic systems operate predictably while probabilistic systems have uncertain behavior. Open systems interact with the environment but closed systems do not.
THE ACTIVE CONTROLLER DESIGN FOR ACHIEVING GENERALIZED PROJECTIVE SYNCHRONIZA...ijait
This paper discusses the design of active controllers for achieving generalized projective synchronization (GPS) of identical hyperchaotic Lü systems (Chen, Lu, Lü and Yu, 2006), identical hyperchaotic Cai systems (Wang and Cai, 2009) and non-identical hyperchaotic Lü and hyperchaotic Cai systems. The synchronization results (GPS) for the hyperchaotic systems have been derived using active control method and established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active control method is very effective and convenient for achieving the GPS of the
hyperchaotic systems addressed in this paper. Numerical simulations are provided to illustrate the effectiveness of the GPS synchronization results derived in this paper.
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
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
This paper derives new results for the global chaos synchronization of identical hyperchaotic Qi systems (2008), identical hyperchaotic Jha systems (2007) and non-identical hyperchaotic Qi and Jha systems. Active nonlinear control is the method adopted to achieve the complete synchronization of the identical and different hyperchaotic Qi and Jha systems. Our stability results derived in this paper are established using Lyapunov stability theory. Numerical simulations are shown to validate and illustrate the effectiveness of the synchronization results derived in this paper.
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
1) The document describes using active nonlinear control to achieve global chaos synchronization between identical and non-identical hyperchaotic systems.
2) It specifically derives new results for synchronizing identical hyperchaotic Qi and Jha systems, as well as non-identical hyperchaotic Qi and Jha systems.
3) The synchronization is achieved through designing appropriate active nonlinear controllers and proving global exponential stability of the synchronization error dynamics using Lyapunov stability theory. Numerical simulations validate the theoretical results.
ANTI-SYNCHRONIZATION OF HYPERCHAOTIC BAO AND HYPERCHAOTIC XU SYSTEMS VIA ACTI...IJCSEIT Journal
This paper investigates the anti-synchronization of identical hyperchaotic Bao systems (Bao and Liu,
2008), identical hyperchaotic Xu systems (Xu, Cai and Zheng, 2009) and non-identical hyperchaotic Bao
and hyperchaotic Xu systems. Active nonlinear control has been deployed for the anti- synchronization of
the hyperchaotic systems addressed in this paper and the main results have been established using
Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active
nonlinear control method is very effective and convenient to achieve anti-synchronization of identical and
non-identical hyperchaotic Bao and hyperchaotic Xu systems. Numerical simulations have been provided to
validate and demonstrate the effectiveness of the anti-synchronization results for the hyperchaotic Cao and
hyperchaotic Xu systems.
This document discusses systems approach in geography. It defines a system as a set of interrelated elements that interact to maintain the system. The key elements of a system include inputs, outputs, processors, control, feedback, boundaries and environment. It also distinguishes between open systems, which exchange both matter and energy with the environment, and closed systems, which only exchange energy. An example of a system discussed is an ecosystem. The document aims to discuss systems thinking to better understand complex geographical phenomena.
Measuring Social Complexity and the Emergence of Cooperation from Entropic Pr...IJEAB
This document presents a theoretical framework for measuring social complexity based on the relationship between complexity, entropy, and evolutionary dynamics. It uses entropy principles to study the emergence of cooperation in social systems. The collapse of the Rapa Nui civilization is used as a case study. The framework defines social complexity using information-theoretic entropy measures and relates higher entropy production and cooperation to greater sustainability. Cooperation emerges when average fitness exceeds local fitness.
This document provides information about a unit on state-space analysis for an electrical engineering course. It includes the topics that will be covered such as state variables, state-space representation of transfer functions, state transition matrices, and controllability and observability. It defines key concepts like state, state vector, and state space. It explains the importance and advantages of state-space analysis over other methods like using transfer functions. The outcomes of the unit are to learn how to model systems in state-space form and analyze properties like controllability and observability.
1. Quantum mechanics seeks to describe how the state of a system changes over time in response to actions, like Newtonian mechanics. However, it describes systems at the microscopic scale of atoms and particles.
2. In Newtonian mechanics, the state is defined by static properties and dynamic variables, and equations of motion describe changes. In quantum mechanics, the state is described by a state function, and operators and equations of motion are used.
3. For an example particle, Newtonian mechanics would define momentum and kinetic energy using mass, position, and velocity. Quantum mechanics defines the probability of finding the particle using its state function and wave-like properties over time.
This document provides an introduction to system dynamics and mathematical modeling of dynamic systems. It defines key concepts such as:
- A system is made up of interacting components that work together to achieve an objective. It has inputs from the environment and outputs responses to those inputs.
- Dynamic systems have outputs that vary over time even if inputs are held constant, due to internal feedback loops within the system.
- Mathematical models of dynamic systems use equations, often differential equations, to describe the system's behavior based on physical laws. The accuracy of a model's predictions depends on how well it approximates the real system.
- Engineering systems like mechanical, electrical, thermal and fluid systems can all be modeled as dynamic systems using appropriate equations
Linguistics models for system analysis- Chuluundorj.BKhulan Jugder
This document discusses various models and concepts for analyzing systems, including:
1. It describes different types of systems including mechanistic, animate, social, and ecological systems.
2. It discusses open, closed, and semi-closed systems and how they interact with their environments.
3. Several mathematical and scientific concepts are proposed as models for linguistic and semantic analysis, such as group theory, topology, Hilbert spaces, and quantum mechanics.
4. The document suggests that these concepts from mathematics, physics, and other fields can provide frameworks for understanding semantic structures, mental representations, and cognitive processes.
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
1) The center of mass of a system of particles or solid body represents the point where all the mass can be considered to be concentrated and external forces are applied.
2) Newton's second law can be applied to the center of mass of a system, where the net external force equals the mass times the acceleration of the center of mass.
3) For a closed, isolated system where no external forces act, the total linear momentum of the system remains constant over time.
Abstract: Using Chemical Organisation Theory [1] we present here an analysis of two classical models of artificial chemistries: a system equivalent to AlChemy [2], and the Automata Chemistry [3]. We show that Chemical Organisation Theory is able to explain why AlChemy was un- able to evolve, while the Automata Chemistry would produce a stream of novelty that would on the one side explore the space of the possible molecules (and organisations) and on the other build upon the previous findings of the system. We relate to Suzuki’s et al. [4] ten necessary conditions for the evolutions of complex forms of life, by adding an 11th one.
Control system note for 6th sem electricalMustafaNasser9
This document provides an overview of the course EC 6405 – Control System Engineering. It includes 5 units that cover various topics in control systems including:
1) Control system modeling using block diagrams, transfer functions, and modeling different physical systems.
2) Time response analysis using concepts like impulse response, step response, and stability analysis tools.
3) Frequency response analysis using tools like Bode plots, Nyquist plots, and Nichol's chart.
4) Stability analysis using tools like the Routh-Hurwitz criterion and root locus analysis.
5) State variable analysis and digital control systems including discrete-time systems and sampled data control systems.
The document lists textbooks and references for
Global stabilization of a class of nonlinear system based on reduced order st...ijcisjournal
The problem of global stabilization for a class of nonlinear system is considered in this paper.The sufficient
condition of the global stabilization of this class of system is obtained by deducing thestabilization of itself
from the stabilization of its subsystems. This paper will come up with a designmethod of state feedback
control law to make this class of nonlinear system stable, and indicate the efficiency of the conclusion of
this paper via a series of examples and simulations at the end. Theresults presented in this paper improve
and generalize the corresponding results of recent works.
Two Types of Novel Discrete Time Chaotic Systemsijtsrd
In this paper, two types of one dimensional discrete time systems are firstly proposed and the chaos behaviors are numerically discussed. Based on the time domain approach, an invariant set and equilibrium points of such discrete time systems are presented. Besides, the stability of equilibrium points will be analyzed in detail. Finally, Lyapunov exponent plots as well as state response and Fourier amplitudes of the proposed discrete time systems are given to verify and demonstrate the chaos behaviors. Yeong-Jeu Sun ""Two Types of Novel Discrete-Time Chaotic Systems"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29853.pdf
Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/29853/two-types-of-novel-discrete-time-chaotic-systems/yeong-jeu-sun
The document discusses qualitative methods for analyzing socio-economic systems and processes. It defines key concepts related to dynamic systems including their properties and modes of behavior. Examples are given of describing the evolution of a cat's health as a dynamic system using variables, parameters, phase space and trajectories. Different software packages can be used for qualitative modeling and analysis of economic objects and processes.
HYBRID SYNCHRONIZATION OF LIU AND LÜ CHAOTIC SYSTEMS VIA ADAPTIVE CONTROLijait
This paper derives new results for the hybrid synchronization of identical Liu systems, identical Lü systems, and non-identical Liu and Lü systems via adaptive control method. Liu system (Liu et al. 2004) and Lü system (Lü and Chen, 2002) are important models of three-dimensional chaotic systems. Hybrid synchronization of the three-dimensional chaotic systems addressed in this paper is achieved through the synchronization of the first and last pairs of states and anti-synchronization of the middle pairs of the two systems. Adaptive control method is deployed in this paper for the general case when the system
parameters are unknown. Sufficient conditions for hybrid synchronization of identical Liu systems, identical Lü systems and non-identical Liu and Lü systems are derived via adaptive control theory and Lyapunov stability theory. Since the Lyapunov exponents are not needed for these calculations, the
adaptive control method is very effective and convenient for the hybrid synchronization of the chaotic systems addressed in this paper. Numerical simulations are shown to illustrate the effectiveness of the proposed synchronization schemes.
This document provides information on various topics in physics including the definition of science, branches of science, the scientific method, and key concepts such as the difference between a theory and law. It also discusses measurement and units in physics. Specifically, it defines what a unit is, different systems of units including the SI system, and prefixes used to denote orders of magnitude. Further, it explains how mass and distance are measured, including inertial and gravitational mass. Key physics concepts such as fundamental and derived quantities are also introduced.
The document discusses the systems approach, which views organizations as complex systems influenced by internal and external factors. It examines how a systems approach analyzes the interactions between different parts of an administrative system and between the system and its external environment. The document also discusses how the systems approach has been applied to analyzing legal systems dealing with transportation law by clarifying objectives and coordinating functions.
Global Chaos Synchronization of Hyperchaotic Pang and Hyperchaotic Wang Syste...CSEIJJournal
This paper investigates the global chaos synchronization of identical hyperchaotic Wang systems, identical
hyperchaotic Pang systems, and non-identical hyperchaotic Wang and hyperchaotic Pang systems via
adaptive control method. Hyperchaotic Pang system (Pang and Liu, 2011) and hyperchaotic Wang system
(Wang and Liu, 2006) are recently discovered hyperchaotic systems. Adaptive control method is deployed
in this paper for the general case when the system parameters are unknown. Sufficient conditions for global
chaos synchronization of identical hyperchaotic Pang systems, identical hyperchaotic Wang systems and
non-identical hyperchaotic Pang and Wang systems are derived via adaptive control theory and Lyapunov
stability theory. Since the Lyapunov exponents are not required for these calculations, the adaptive control
method is very convenient for the global chaos synchronization of the hyperchaotic systems discussed in
this paper. Numerical simulations are presented to validate and demonstrate the effectiveness of the
proposed synchronization schemes.
On The Correct Formulation Of The Law Of The External Photoelectric Effect (I...ijifr
The document proposes a critical analysis of Einstein's formulation of the law of the external photoelectric effect. It argues that Einstein's formula violates logical laws because it relates quantities that characterize different material objects (photon, electron in metal, electron not in metal). The document then presents a new, correct mathematical formulation of the law based on the relationship between relative increments of the photon energy and emitted electron energy. This proportion relationship is argued to satisfy logical identity laws and correctly describe the photoelectric effect process.
IJIFR- Volume 4 Issue 1, September 2016 vikas sharma
The Journal aims to publish quality material that contributes to accumulate dynamic knowledge which is able to revitalize and foster the research and development carried out in different disciplines. IJIFR brings into the world the understanding of various faculties in one platform to have access to accurate information and new innovations & provide a rapid turn-around time possible for reviewing and publishing, and to disseminate the articles freely for teaching and reference purposes. Its multidisciplinary approach is deliberate to bring the worldwide eminent intellectuals on one platform to illumine the world of knowledge. The journal follows a Blind-Review Peer Review System in order to bring in a high-quality intellectual platform for researchers across the world thereby bringing in total transparency in its journal review system. It delivers eventual platform in order to have genuine, speculative, knowledgeable research which has the visualization to understand fact-finding experiences that describes significant developments of changing global scenarios. IJIFR provide access not only to ultimate research resources, but through its high-quality professionals are ambitious to bring in a significant transformation in the dominion of open access journals and online publishing which the globe is observing nowadays. All manuscripts are reviewed in fairness based on the intellectual content of the paper regardless of gender, race, ethnicity, religion, citizenry nor political values of author(s).the IJIFR provides free access to research information to the international community without financial, legal or technical barriers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and must not be under consideration for publication elsewhere. All the accepted papers of the journal will be processed for indexing into different citation databases that track citation frequency/data for each paper. Contributions will therefore be welcomed from practitioners, researchers, scholars and professional experts working in private, public and other organizations or industries.
This document presents an internship report on studying atomic dynamics out of thermal equilibrium using a three-level atom model. It first derives a Markovian master equation to describe the dynamics of open quantum systems interacting with environments. It then introduces the model of a system interacting with electromagnetic fields at and out of thermal equilibrium. Finally, it examines three-level atom configurations (ladder, Λ, and V) and how population inversion can be achieved when the environment is out of thermal equilibrium.
This document discusses systems approach in geography. It defines a system as a set of interrelated elements that interact to maintain the system. The key elements of a system include inputs, outputs, processors, control, feedback, boundaries and environment. It also distinguishes between open systems, which exchange both matter and energy with the environment, and closed systems, which only exchange energy. An example of a system discussed is an ecosystem. The document aims to discuss systems thinking to better understand complex geographical phenomena.
Measuring Social Complexity and the Emergence of Cooperation from Entropic Pr...IJEAB
This document presents a theoretical framework for measuring social complexity based on the relationship between complexity, entropy, and evolutionary dynamics. It uses entropy principles to study the emergence of cooperation in social systems. The collapse of the Rapa Nui civilization is used as a case study. The framework defines social complexity using information-theoretic entropy measures and relates higher entropy production and cooperation to greater sustainability. Cooperation emerges when average fitness exceeds local fitness.
This document provides information about a unit on state-space analysis for an electrical engineering course. It includes the topics that will be covered such as state variables, state-space representation of transfer functions, state transition matrices, and controllability and observability. It defines key concepts like state, state vector, and state space. It explains the importance and advantages of state-space analysis over other methods like using transfer functions. The outcomes of the unit are to learn how to model systems in state-space form and analyze properties like controllability and observability.
1. Quantum mechanics seeks to describe how the state of a system changes over time in response to actions, like Newtonian mechanics. However, it describes systems at the microscopic scale of atoms and particles.
2. In Newtonian mechanics, the state is defined by static properties and dynamic variables, and equations of motion describe changes. In quantum mechanics, the state is described by a state function, and operators and equations of motion are used.
3. For an example particle, Newtonian mechanics would define momentum and kinetic energy using mass, position, and velocity. Quantum mechanics defines the probability of finding the particle using its state function and wave-like properties over time.
This document provides an introduction to system dynamics and mathematical modeling of dynamic systems. It defines key concepts such as:
- A system is made up of interacting components that work together to achieve an objective. It has inputs from the environment and outputs responses to those inputs.
- Dynamic systems have outputs that vary over time even if inputs are held constant, due to internal feedback loops within the system.
- Mathematical models of dynamic systems use equations, often differential equations, to describe the system's behavior based on physical laws. The accuracy of a model's predictions depends on how well it approximates the real system.
- Engineering systems like mechanical, electrical, thermal and fluid systems can all be modeled as dynamic systems using appropriate equations
Linguistics models for system analysis- Chuluundorj.BKhulan Jugder
This document discusses various models and concepts for analyzing systems, including:
1. It describes different types of systems including mechanistic, animate, social, and ecological systems.
2. It discusses open, closed, and semi-closed systems and how they interact with their environments.
3. Several mathematical and scientific concepts are proposed as models for linguistic and semantic analysis, such as group theory, topology, Hilbert spaces, and quantum mechanics.
4. The document suggests that these concepts from mathematics, physics, and other fields can provide frameworks for understanding semantic structures, mental representations, and cognitive processes.
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
1) The center of mass of a system of particles or solid body represents the point where all the mass can be considered to be concentrated and external forces are applied.
2) Newton's second law can be applied to the center of mass of a system, where the net external force equals the mass times the acceleration of the center of mass.
3) For a closed, isolated system where no external forces act, the total linear momentum of the system remains constant over time.
Abstract: Using Chemical Organisation Theory [1] we present here an analysis of two classical models of artificial chemistries: a system equivalent to AlChemy [2], and the Automata Chemistry [3]. We show that Chemical Organisation Theory is able to explain why AlChemy was un- able to evolve, while the Automata Chemistry would produce a stream of novelty that would on the one side explore the space of the possible molecules (and organisations) and on the other build upon the previous findings of the system. We relate to Suzuki’s et al. [4] ten necessary conditions for the evolutions of complex forms of life, by adding an 11th one.
Control system note for 6th sem electricalMustafaNasser9
This document provides an overview of the course EC 6405 – Control System Engineering. It includes 5 units that cover various topics in control systems including:
1) Control system modeling using block diagrams, transfer functions, and modeling different physical systems.
2) Time response analysis using concepts like impulse response, step response, and stability analysis tools.
3) Frequency response analysis using tools like Bode plots, Nyquist plots, and Nichol's chart.
4) Stability analysis using tools like the Routh-Hurwitz criterion and root locus analysis.
5) State variable analysis and digital control systems including discrete-time systems and sampled data control systems.
The document lists textbooks and references for
Global stabilization of a class of nonlinear system based on reduced order st...ijcisjournal
The problem of global stabilization for a class of nonlinear system is considered in this paper.The sufficient
condition of the global stabilization of this class of system is obtained by deducing thestabilization of itself
from the stabilization of its subsystems. This paper will come up with a designmethod of state feedback
control law to make this class of nonlinear system stable, and indicate the efficiency of the conclusion of
this paper via a series of examples and simulations at the end. Theresults presented in this paper improve
and generalize the corresponding results of recent works.
Two Types of Novel Discrete Time Chaotic Systemsijtsrd
In this paper, two types of one dimensional discrete time systems are firstly proposed and the chaos behaviors are numerically discussed. Based on the time domain approach, an invariant set and equilibrium points of such discrete time systems are presented. Besides, the stability of equilibrium points will be analyzed in detail. Finally, Lyapunov exponent plots as well as state response and Fourier amplitudes of the proposed discrete time systems are given to verify and demonstrate the chaos behaviors. Yeong-Jeu Sun ""Two Types of Novel Discrete-Time Chaotic Systems"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29853.pdf
Paper Url : https://www.ijtsrd.com/engineering/electrical-engineering/29853/two-types-of-novel-discrete-time-chaotic-systems/yeong-jeu-sun
The document discusses qualitative methods for analyzing socio-economic systems and processes. It defines key concepts related to dynamic systems including their properties and modes of behavior. Examples are given of describing the evolution of a cat's health as a dynamic system using variables, parameters, phase space and trajectories. Different software packages can be used for qualitative modeling and analysis of economic objects and processes.
HYBRID SYNCHRONIZATION OF LIU AND LÜ CHAOTIC SYSTEMS VIA ADAPTIVE CONTROLijait
This paper derives new results for the hybrid synchronization of identical Liu systems, identical Lü systems, and non-identical Liu and Lü systems via adaptive control method. Liu system (Liu et al. 2004) and Lü system (Lü and Chen, 2002) are important models of three-dimensional chaotic systems. Hybrid synchronization of the three-dimensional chaotic systems addressed in this paper is achieved through the synchronization of the first and last pairs of states and anti-synchronization of the middle pairs of the two systems. Adaptive control method is deployed in this paper for the general case when the system
parameters are unknown. Sufficient conditions for hybrid synchronization of identical Liu systems, identical Lü systems and non-identical Liu and Lü systems are derived via adaptive control theory and Lyapunov stability theory. Since the Lyapunov exponents are not needed for these calculations, the
adaptive control method is very effective and convenient for the hybrid synchronization of the chaotic systems addressed in this paper. Numerical simulations are shown to illustrate the effectiveness of the proposed synchronization schemes.
This document provides information on various topics in physics including the definition of science, branches of science, the scientific method, and key concepts such as the difference between a theory and law. It also discusses measurement and units in physics. Specifically, it defines what a unit is, different systems of units including the SI system, and prefixes used to denote orders of magnitude. Further, it explains how mass and distance are measured, including inertial and gravitational mass. Key physics concepts such as fundamental and derived quantities are also introduced.
The document discusses the systems approach, which views organizations as complex systems influenced by internal and external factors. It examines how a systems approach analyzes the interactions between different parts of an administrative system and between the system and its external environment. The document also discusses how the systems approach has been applied to analyzing legal systems dealing with transportation law by clarifying objectives and coordinating functions.
Global Chaos Synchronization of Hyperchaotic Pang and Hyperchaotic Wang Syste...CSEIJJournal
This paper investigates the global chaos synchronization of identical hyperchaotic Wang systems, identical
hyperchaotic Pang systems, and non-identical hyperchaotic Wang and hyperchaotic Pang systems via
adaptive control method. Hyperchaotic Pang system (Pang and Liu, 2011) and hyperchaotic Wang system
(Wang and Liu, 2006) are recently discovered hyperchaotic systems. Adaptive control method is deployed
in this paper for the general case when the system parameters are unknown. Sufficient conditions for global
chaos synchronization of identical hyperchaotic Pang systems, identical hyperchaotic Wang systems and
non-identical hyperchaotic Pang and Wang systems are derived via adaptive control theory and Lyapunov
stability theory. Since the Lyapunov exponents are not required for these calculations, the adaptive control
method is very convenient for the global chaos synchronization of the hyperchaotic systems discussed in
this paper. Numerical simulations are presented to validate and demonstrate the effectiveness of the
proposed synchronization schemes.
On The Correct Formulation Of The Law Of The External Photoelectric Effect (I...ijifr
The document proposes a critical analysis of Einstein's formulation of the law of the external photoelectric effect. It argues that Einstein's formula violates logical laws because it relates quantities that characterize different material objects (photon, electron in metal, electron not in metal). The document then presents a new, correct mathematical formulation of the law based on the relationship between relative increments of the photon energy and emitted electron energy. This proportion relationship is argued to satisfy logical identity laws and correctly describe the photoelectric effect process.
IJIFR- Volume 4 Issue 1, September 2016 vikas sharma
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2. Literature:
1. Paul Davidovits. Physics in Biology and Medicine 5th Edition. Academic Press, 2018.
2. Irving P. Herman. Physics of the Human Body. Springer, 2016.
3. Muhammed Maqbool. An Introduction to Medical Physics. Springer, 2017.
4. J Šetrajčić, D Mirjanić. Biofizičke osnove tehnike i medicine. ANURS, Banja Luka, 2012.
5. D Ristanović, J Simonović, J Vuković, R Radovanović. Biofizika. Medicinska knjiga.
Beograd, 1981.
6. S Stanković. Fizika ljudskog organizma. PMF Novi Sad, 2006.
3. 1. Systemology
Biophysics-1892 English statistician Karl Pearson.
Science that uses physical laws to explain phenomena in biology.
The object of research in biophysics is a living or biological system and the
methodology of research is reduced to the methodology of physical sciences.
It is an interdisciplinary science. According to the object of research, biophysics
can be divided into three areas: molecular, cellular and system biophysics.
4. Basics of systemology
1. Concept and definition of system
By studying the common characteristics of all systems (nervous, cardiovascular,...) the so-
called systemology (general theory of systems) was born.
A system is a set of objects, living objects, processes or phenomena, which are arranged
in a certain way and interconnected as a whole with a certain function, different from
the function of individual elements.
System components:
- elements (fig.) that make up the material base of the system,
-channels of connection between elements and directions of
information transfer (action) between them and related to the
relational characteristic of the system,
-system boundary.
Example: CNS-system, neurons-elements that are
interconnected and can act on each other.
5. System and reality
There is only one universal system (universe) and partial systems that do not exist and that is
an approximation of reality.
The figure shows an isolated system that does not exist in nature (all systems in nature are
open).
In order for a set of elements to represent a system, there must be a function that the system
needs to achieve and a degree of mutual connection between the elements.
Example: Two oxygen atoms represent a system of oxygen molecules that have new
characteristics compared to the properties of individual oxygen atoms.
6. 2. System classification
a) According to the degree of complexity:
Simple systems - a small number of elements connected in a simple way
Complex systems - a large number of complexly connected elements
Complicated (very complex) - the current level of knowledge cannot describe them
b) According to the nature and behavior of the system in the future:
Deterministic systems-elements interact with each other in a precisely predictable way.
Probabilistic (probable) system - elements act so that in the same way they can predict
the behavior of the system in the future (with a certain probability).
7. c) Combined system classification:
1) A simple deterministic system - it contains few elements and mutual connections, it is easy to
describe and its behavior in the future is easy to predict (Typewriter).
2) A complex system with deterministic behavior has a relatively complex structure, but despite
that, its behavior in the future can be unambiguously predicted (PC).
3) Complicated system with determined behavior, rarely encountered (Cosmos).
4) A simple system with random behavior is a simple system whose behavior can be easily
predicted by the laws of probability (a tossed coin).
5) A complex system with random behavior - its behavior in the future can only be predicted on
the basis of probability. Many biological systems boil down to this. Conditioned reflex - complex
system - a large number of neurons, and it is probabilistic because the consequences are not
always the same (if a dog is offered a bone, it is not certain that he will always to take it).
6) Complicated system with random behavior - future behavior cannot be described or predicted
(the mammalian brain).
8. System inputs and outputs.
A real system is always open. With the environment is connected by inputs (x) through
which the environment affects on system and outputs (y) through which the system affects
the environment.
An effect (stimulus, impulse, cause, disorder) is an effect the environment whose change (at
the input) can be transmitted to the system while it is reaction (response, consequence)
property of the system that changes transmitted to the environment through the system
output.
In order for the examination of the system to have an exact character, it is necessary
that the actions and reactions of the system are physical quantities (properties of the
environment and the system are subject to physical measurements).
Let x1, x2, ..., xn be input and y1, y2, ...., ym output values.
9. 3. Terms that define the system
The “black box” is shown in the figure as a rectangle with inputs and outputs. It is
determined by:
a) Input quantities as a function of time: x1(t), x2(t),...
b) Output quantities as a function of time y1(t), ......
c) Parameters (const) characterize the properties of the system (or the
environment) a1, a2, ....
d) Transfer functions, which show the law of system behavior (dependence of
input x(t), output values y(t) and parameters a. f1, f2, ...
The law of system behavior can be presented in the form of a system of functions
y1=f1(x1,x2,x3; a1,a2), y2=f2(x1,x2,x3; a1,a2), ......
10. 4. Basic tasks of systemology
a) Direct task: x(t), f, and a are given, y(t) is required (simple).
b) Indirect task of the first type: given y(t), a and f, x(t) is required.
c) Indirect task of the second type: x(t), y(t) and f are given, a is sought.
The doctor takes the anamnesis of the disease (input), then examines the
patient and determines the symptoms (output) and, knowing the relationship
between effects and reactions (input and output, which represents the law of
behavior of the system-patient), draws conclusions about the type and nature
of the disease (system parameters).
d) The "black box" or induction problem: x(t) and y(t) are given, f and a are
required.
This task is the most difficult to solve. These systems are classified into the so-
called zero-, first-, and second - order systems whose properties are well
known.
11. 5. Zero order system
System with an elastic spring -
a force F (x-input) acts on the end of the spring. This force causes the spring to stretch
by a length l (y-output). According to Hook's law, the stretching of the body l is
proportional to the force F = - k l
k- is the coefficient of elasticity (a-parameter of the system)
If the law of system behavior can be represented as an algebraic equation (does not
contain differentials and integrals), it is a zero-order system ( x = a y, there is a linear
dependence).
12. 6. First order system
Spring box filled with viscous oil. By stretching the spring under the action of
force F, its threads meet the resistance force of the medium Fre (oil).
Fre is proportional to the speed v of the movement of the end of the spring A, i.e.
Fre = - r v = - r dl/dt
r is the system parameter (friction coefficient).
The force with which we act on the spring in order to stretch it (input) will be
F = k l + r dl/dt
is a first-order differential equation (x = a y + b dy/dt).
Any system whose behavior law (transfer function) can be represented by a
first-order differential equation (output quantity) is called a first-order
system.
13. 7. For the system to be of second order, the transfer function must contain the
second derivative of the output quantity
x = a y + b (dy/dt) + c (d2y/dt2) (a, b, c, are system parameters)
An example is the process of generating an action potential on the cell membrane.
Due to the external stimulus, the membrane will first depolarize (from: - 85 mV
to 40 mV). After that, the membrane repolarizes to the initial value through a
series of damped oscillations.
14. 8. Cybernetic systems
Cybernetics - the science of managing complex systems.
The elements of the cybernetic system are the control system (its action on the
control object leads to the desired changes in it) and the control object (the desired
changes are realized in it). They are connected by connection channels through
which the control system acts on the control object. They can be open and closed
systems.
8.1. Open cybernetic systems, information is brought to the input of the system and
the connection is one-way (automatic machines, computers). They are not of interest
in biomedicine.
15. 8.2. Closed cybernetic systems, there is also a feedback channel in the opposite
direction. It enables the feedback effect of the output value on the control system,
which is used to regulate it. They are widely used in biomedicine. They compensate for
a possible disturbance in the functioning of the system, caused by some undesirable
external effect.
Example:
Blood pressure is partly regulated by sensors in the kidney. Increased pressure
damages blood vessels also in the kidney where the sensors are. Damage reduces
pressure and an increase signal is sending again. Further increase leads to greater
damage to all vessels and to a new demands to increase the pressure. "Vicious Circle".
16. There are two types of feedback systems:
8.2.1. Tracking systems, pointer spring (control object) is stretched. The control
system is a person whose task is to position the pointer (output size Y) equate to
the position of the second pointer. The difference in the positions of the pointers
represents an error (noise, Y = Yi - Yo) which should be compensated. We stretch
the spring by hand and reduce the error. It is a cybernetic feedback system.
17. 8.2.2. Regulatory systems
Biological systems are open thermodynamic systems that communicate with the environment.
In order to avoid permanent disturbances, biological systems neutralize these influences
through regulatory systems. They achieve this through numerous regulatory systems.
Example: The hypothalamus ("thermostat") regulates the temperature in the body (37 0.5 oC).
If the thermostat is "set" to a higher temperature (by introducing toxic substances), a feeling of
coldness appears until we reach it.
By returning the thermostat to normal function (medicaments), the body is released from excess
heat until it reaches a normal temperature that corresponds to the state of homeostasis.
If the organism is in a state of low temperature, the hypothalamus activates shaking and thus
increases the temperature.
If the hypothalamus is not able to keep body
temperatures at very low temperatures, it will
protect vital organs (brain, heart) in that way,
reducing blood flow through the extremities.
18. At the cellular level, regulatory systems play a vital role in homeostasis (maintenance
of constant conditions in the environment of the cell-extracellular fluid).
A disturbance in the composition of this liquid leads to the destruction of the cell. That
is why a large number of regulatory systems neutralize changes.
The respiratory system regulates the concentration of carbon dioxide
Liver and pancreas regulate glucose concentration
Kidneys regulate the concentration of hydrogen ions, potassium (K), sodium (Na),
phosphorus (P), ...
19. Example: Regulation of the opening of the pupil of the eye.
The pupil changes its diameter L depending on the amount of light falling on the retina.
If is the amount of light reaching the retina, I is the light intensity and S is the area
of the pupil opening, then = a I S where a is the proportionality coefficient. The
response of the eye to an increase in light intensity is to reduce the pupil opening so
that the amount of light on the retina does not change, which means that there is a
feedback loop.
20. 9. Examination of Biological Systems
Basic principles and steps in system testing
The first step is to observe the biological system and
observe the behavioral characteristics.
Then, by induction (from the individual to the general),
we isolate the general characteristics of the structure
and behavior of the system, after which a hypothesis is
put forward that predicts the behavior of the system
under certain conditions.
After the hypothesis, an experiment should be
performed on the system to test the hypothesis.
Biological systems are very complicated, so we will
not do the tests right away on a living organism.
A system is designed on which an experiment is
performed to test the hypothesis. The results often
indicate incorrect settings, which leads to the
redesign of the experimental system. The system
obtained in this way represents the final model of the
biological system that we are examining, and the
procedure for obtaining the model is modeling. Only
now we can look for answers to the questions raised,
by numerical simulation or experimentally, then by
testing on animals and only at the end of clinical trials.
21. 10. Definition of the model. Principles of modeling.
Models are systems with all system characteristics. They are used to test the
functioning of real systems. The process of constructing a model, which is analogous
to a real system under investigation, is modeling.
The basic principles in the modeling process are: the principle of isomorphism, the
principle of homomorphism and the principle of analogy.
The principle of isomorphism. Two systems of an different nature are said to be
isomorphic, if when replacing one system with another, it gets the same answer for any
input quantity. It does not exist in the living world.
The principle of homomorphism. If systems have the same behavior with respect to
a finite number of properties, they are isomorphic with respect to those properties, but
may differ with respect to other properties. Such two partially identical systems are
homomorphic. Application in complicated biological systems when monitoring the
response of a part of the organism to the effect of an input quantity.
Principle of analogy. Various systems can function in the same way (the working
principle is the same). Analogy - a mechanical pump that causes fluid to circulate
through a tube and a heart that forces blood to circulate through blood vessels. The
results obtained in one system can be used to forecast another analog system.
22. Classification of models
Descriptive models - the simplest class that describes the system qualitatively. They
are divided into verbal and pictorial.
Mathematical models-data are in the form of equations and mathematical
expressions. If we mathematically express the transfer function of a system, then the
response that that system gives to a certain input quantity must have the same value as
the mathematically obtained solution of the equation representing the transfer function.
Physical models are material models that are realized in the form of a machine,
electrcircuit, etc. An example of a physical model is a electrical circuit that simulates
the behavior of a biogenerator of electric current in the human body.