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Influence of Solvent on Deformation Behavior of Hydrogels
Kulovaná, Eva ; Bartlová, Milada (referee) ; Mráček, Aleš (referee) ; Žídek, Jan (advisor)
The thesis deals with molecular dynamic simulation of the influence of water on the deformation of hydrogels. Hydrogels are model materials formed from macromolecular networks solvated with water. It was found that water can form bridges between macromolecules that take the form of temporary ionic crosslinks. These bridges affect the behavior of the network during deformation. Water bridges are water molecules that have a limited radius of motion in the space between two macromolecules. The concentration of the water bridges was regulated by a partial charge on the macromolecular chain in the organic network. Bridges are a type of interaction that is relatively strong but significantly delocalized. It is not possible to dissociate the water bridge, after dissociation it will be re-created in another place in a short time. The influence of water bridges was compared with other types of network crosslinks, especially covalent and physical bonds. Covalent crosslinks are modeled as a simple binding interaction between two macromolecules. They are undissociable and are local throughout the simulation. Physical bonds are modeled as micelles, where hydrophobic groups form the core and hydrophilic groups form the micelle shell. Physical bonds have the nature of dissociable bonds that are local. Different types of crosslinks have different effects on deformation properties. The deformation of a network containing a combination of two types of crosslinks was simulated: (i) physically-covalent, (ii) ionically-covalent, and (iii) physically-ionic networks and (iv) ternary physically-covalent-ion networks. For individual and combined networks, the behavior depending on simple networks was verified. The number of water bridges was fundamentally affected by the primary structure of the chains. When the PEG chain was replaced with hydrophobic polyoxymethylene (POM) or polyoxytrimethylene (POTM), their solvation and mechanical behavior deteriorated.
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Stock Markets Analysis Using New Genetic Annealed Neural Network
Verner, Robert ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The presented master thesis is focused on the stock markets returns analysis using a new type of neural network. First chapter of the thesis describes the underlying theory of the financial time series prediction, Efficient Market Hypothesis and conventional forecasting models. Following part illustrates biological framework, basic principles, functioning of neural networks, their architecture and several well-known learning algorithms such as Gradient descent, Levenberg-Marquardt algorithm or Conjugate gradient. It also mentions certain disadvantages which influence the performance and effectiveness of neural networks. Third chapter is devoted to two applied metaheuristic techniques, i.e. genetic algorithms and simulated annealing that were integrated into neural networks framework to eliminate above mentioned drawbacks. Next chapter describes details of presented hybrid network, whereas the last section is aimed at evaluation of overall results of all models. It shows that on the examined sample hybrid network clearly outperformed standard techniques as well as ordinary neural networks and in most cases achieved the least mean squared error among all explored methods. Keywords: stock returns analysis, neural networks, genetic algorithms, simulated annealing, hybrid networks JEL classification:...
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Influence of Solvent on Deformation Behavior of Hydrogels
Kulovaná, Eva ; Bartlová, Milada (referee) ; Mráček, Aleš (referee) ; Žídek, Jan (advisor)
The thesis deals with molecular dynamic simulation of the influence of water on the deformation of hydrogels. Hydrogels are model materials formed from macromolecular networks solvated with water. It was found that water can form bridges between macromolecules that take the form of temporary ionic crosslinks. These bridges affect the behavior of the network during deformation. Water bridges are water molecules that have a limited radius of motion in the space between two macromolecules. The concentration of the water bridges was regulated by a partial charge on the macromolecular chain in the organic network. Bridges are a type of interaction that is relatively strong but significantly delocalized. It is not possible to dissociate the water bridge, after dissociation it will be re-created in another place in a short time. The influence of water bridges was compared with other types of network crosslinks, especially covalent and physical bonds. Covalent crosslinks are modeled as a simple binding interaction between two macromolecules. They are undissociable and are local throughout the simulation. Physical bonds are modeled as micelles, where hydrophobic groups form the core and hydrophilic groups form the micelle shell. Physical bonds have the nature of dissociable bonds that are local. Different types of crosslinks have different effects on deformation properties. The deformation of a network containing a combination of two types of crosslinks was simulated: (i) physically-covalent, (ii) ionically-covalent, and (iii) physically-ionic networks and (iv) ternary physically-covalent-ion networks. For individual and combined networks, the behavior depending on simple networks was verified. The number of water bridges was fundamentally affected by the primary structure of the chains. When the PEG chain was replaced with hydrophobic polyoxymethylene (POM) or polyoxytrimethylene (POTM), their solvation and mechanical behavior deteriorated.
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Stock Markets Analysis Using New Genetic Annealed Neural Network
Verner, Robert ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The presented rigorosis thesis is focused on the stock markets returns analysis using a new type of neural network. First chapter of the thesis describes the underlying theory of the financial time series prediction, Efficient Market Hypothesis and conventional forecasting models. Following part illustrates biological framework, basic principles, functioning of neural networks, their architecture and several well-known learning algorithms such as Gradient descent, Levenberg-Marquardt algorithm or Conjugate gradient. It also mentions certain disadvantages which influence the performance and effectiveness of neural networks. Third chapter is devoted to two applied metaheuristic techniques, i.e. genetic algorithms and simulated annealing that were integrated into neural networks framework to eliminate above mentioned drawbacks. Next chapter describes details of presented hybrid network, whereas the last section is aimed at evaluation of overall results of all models. It shows that on the examined sample hybrid network clearly outperformed standard techniques as well as ordinary neural networks and in most cases achieved the least mean squared error among all explored methods. Keywords: stock returns analysis, neural networks, genetic algorithms, simulated annealing, hybrid networks JEL classification:...
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Stock Markets Analysis Using New Genetic Annealed Neural Network
Verner, Robert ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The presented master thesis is focused on the stock markets returns analysis using a new type of neural network. First chapter of the thesis describes the underlying theory of the financial time series prediction, Efficient Market Hypothesis and conventional forecasting models. Following part illustrates biological framework, basic principles, functioning of neural networks, their architecture and several well-known learning algorithms such as Gradient descent, Levenberg-Marquardt algorithm or Conjugate gradient. It also mentions certain disadvantages which influence the performance and effectiveness of neural networks. Third chapter is devoted to two applied metaheuristic techniques, i.e. genetic algorithms and simulated annealing that were integrated into neural networks framework to eliminate above mentioned drawbacks. Next chapter describes details of presented hybrid network, whereas the last section is aimed at evaluation of overall results of all models. It shows that on the examined sample hybrid network clearly outperformed standard techniques as well as ordinary neural networks and in most cases achieved the least mean squared error among all explored methods. Keywords: stock returns analysis, neural networks, genetic algorithms, simulated annealing, hybrid networks JEL classification:...
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