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Jojoba Sun Protection Milk SPF 10 - 250 ml
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Tanning Capsules - 49 g
Tanning capsules supplement the protection of our skin against the sun and intensify and prolong a beautiful, natural tan. The so-called "carrot effect" is a widely-known, desired side-effect of the plant-based carotenoids. It gives the skin a healthy colour similar to a sun tan. Many customers therefore start taking Tanning Capsules several weeks prior to the holiday or the sunny season, or use them in winter to retain their natural sun tan. Copper is an ideal supplement to this effect as it has been proven to contribute towards a normal, healthy pigmentation of the skin. Copper also supports the retention of healthy connective tissue and protects the cells against oxidative stress, i.e. free radicals which can be generated through solar irradiation.
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Goji Berries - 500 g
The goji berry also known as chinese wolfberry, is the fruit of the lycium bush (barbarum lycium, buck-thorn). The highly situated and sun-spoiled province Ningxia in China is a typical landsape, in which are found ideal conditions for these red shining fruits. Goji berries have been popular in Asia for a long time. They are often nicknamed the "happy berry" and are considered to be the fruit of well-being. Modern time scientific examinations confirm the reputation of the goji berry as an exceptional gift of nature. Their high content of lutein, zeaxanthin and beta-carotene is remarkable. Our goji berries have been gently dried in the sun so that valuable substances and the natural colour are retained. They taste fruity-sharp, not too sweet and are wonderfully suitable as an addition to mueslis or simply as healthy alternative for snacking.
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Similar search terms for Automaton:
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What is a finite automaton?
A finite automaton is a mathematical model used to represent a system that processes input and transitions between different states based on that input. It consists of a set of states, a set of input symbols, a transition function that specifies how the automaton moves from one state to another based on the input, a start state, and a set of accepting states. Finite automata are used in computer science and theoretical computer science to model and analyze the behavior of systems that can be in a finite number of states and transition between them based on input. They are used in various applications such as lexical analysis in compilers, pattern matching in text processing, and modeling of digital circuits.
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How do you create a deterministic finite automaton from a non-deterministic pushdown automaton?
To create a deterministic finite automaton (DFA) from a non-deterministic pushdown automaton (PDA), we can use the subset construction method. This involves creating a state in the DFA for each possible combination of states in the PDA. The transitions in the DFA are determined by simulating the behavior of the PDA on each input symbol. If the PDA can be in multiple states at a given time, the DFA will have a state for each combination of those states. The final states of the DFA are determined by whether any of the PDA's possible states are final states. This process results in a deterministic finite automaton that simulates the behavior of the original non-deterministic pushdown automaton.
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How do you transfer the state transition diagram of an automaton into an automaton table?
To transfer the state transition diagram of an automaton into an automaton table, you need to list all the states of the automaton as rows in the table. Then, list all the input symbols as columns in the table. Fill in the table with the corresponding next state for each combination of current state and input symbol based on the transitions in the state transition diagram. Finally, indicate the initial state and any final states in the table. This table can then be used to simulate the behavior of the automaton for different input sequences.
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How do you enter the state transition diagram of an automaton into an automaton table?
To enter the state transition diagram of an automaton into an automaton table, you first list all the states of the automaton as rows in the table. Then, list all the input symbols as columns in the table. For each state and input symbol pair, fill in the corresponding cell with the next state that the automaton transitions to. Repeat this process for all state and input symbol pairs until the entire transition diagram is represented in the automaton table.
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How does a non-deterministic finite automaton work?
A non-deterministic finite automaton (NFA) is a theoretical model of computation that consists of a set of states, a set of input symbols, a transition function, an initial state, and a set of accepting states. Unlike a deterministic finite automaton (DFA), an NFA can have multiple possible transitions from a given state on a given input symbol. When processing input, an NFA can be in multiple states simultaneously and can transition to multiple states at once. It accepts a given input string if there exists at least one path through the states that leads to an accepting state. NFAs are often used in theoretical computer science to model certain types of computations and are a key concept in the theory of formal languages and automata.
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How does a deterministic finite automaton (DFA) work?
A deterministic finite automaton (DFA) is a mathematical model used to recognize patterns in strings of symbols. It consists of a finite set of states, a finite set of input symbols, a transition function that maps a state and an input symbol to another state, a start state, and a set of accepting states. The DFA starts in the start state and reads input symbols one at a time, transitioning between states according to the transition function. Once the input is fully processed, the DFA is in a final state, and if that state is an accepting state, the input is accepted; otherwise, it is rejected. DFAs are used in various applications, such as lexical analysis in compilers and pattern matching in text processing.
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What is a deterministic finite automaton in computer science?
A deterministic finite automaton (DFA) is a type of finite state machine in computer science. It consists of a set of states, a set of input symbols, a transition function that maps states and input symbols to other states, a start state, and a set of accepting states. DFAs are used to recognize patterns in input strings by transitioning between states based on the input symbols. They are simpler than nondeterministic finite automata (NFAs) as they have a unique transition for each input symbol in each state.
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What is a cellar automaton in theoretical computer science?
A cellar automaton is a type of cellular automaton in theoretical computer science. Cellular automata are discrete models studied in computer science and mathematics, consisting of a grid of cells, each of which can be in a finite number of states. The state of each cell evolves over time according to a set of rules based on the states of neighboring cells. In a cellar automaton, the grid is arranged in a three-dimensional lattice, and the state of each cell is updated based on the states of its six neighboring cells. Cellar automata are used to study complex systems and emergent behavior in various fields, including physics, biology, and computer science.
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How to create a deterministic finite automaton from a regular expression?
To create a deterministic finite automaton (DFA) from a regular expression, you can follow these steps: 1. Convert the regular expression into an equivalent non-deterministic finite automaton (NFA) using Thompson's construction algorithm. 2. Convert the NFA into a DFA using the subset construction algorithm. This involves creating a state in the DFA for each subset of states in the NFA. 3. Define the transitions of the DFA based on the transitions of the NFA. Each transition in the DFA corresponds to a set of transitions in the NFA. 4. Define the accepting states of the DFA based on the accepting states of the NFA. A state in the DFA is accepting if it contains an accepting state from the NFA. By following these steps, you can create a deterministic finite automaton that recognizes the language described by the original regular expression.
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Is this NFA also a DFA automaton at the same time?
No, this NFA is not a DFA automaton at the same time. While all DFAs are NFAs, not all NFAs are DFAs. The NFA may have multiple transitions for the same input symbol from a single state, while a DFA can only have one transition for each input symbol from a given state. Therefore, this NFA does not meet the criteria to be a DFA.
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How do you create a deterministic finite automaton from a regular expression?
To create a deterministic finite automaton (DFA) from a regular expression, you can follow these steps: 1. Convert the regular expression into an equivalent non-deterministic finite automaton (NFA) using Thompson's construction algorithm. 2. Convert the NFA into a DFA using the subset construction algorithm. This involves creating a state in the DFA for each possible subset of states in the NFA. 3. Define the transitions of the DFA based on the transitions of the NFA and the input symbols. This will result in a DFA that recognizes the same language as the original regular expression.
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What is the difference between a finite automaton and a Turing machine?
A finite automaton is a simpler model of computation compared to a Turing machine. Finite automata have a limited memory and can only recognize regular languages, while Turing machines have an infinite memory and can recognize context-sensitive languages. Turing machines are more powerful and versatile in terms of computation capabilities compared to finite automata.
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