Describe the Voigt-Kelvin model with the aid of sketches. What are its strengths and limitations?

## Describe the Voigt-Kelvin model with the aid of sketches. What are its strengths and limitations?

For this model, the spring and dashpot are put … Read More...
Two wires #1 carries a current I, toward the top of the page and is held rigidly in place. wire #2 carries a current I to the left and is free to move. wire # 2 will experience a magnatic force which cause it to; 1) move down , 2) move up , 3) rotate clockwise , 4) rotate counterclockwise, 5) move right.

## Two wires #1 carries a current I, toward the top of the page and is held rigidly in place. wire #2 carries a current I to the left and is free to move. wire # 2 will experience a magnatic force which cause it to; 1) move down , 2) move up , 3) rotate clockwise , 4) rotate counterclockwise, 5) move right.

salt water contains n sodium ions (Na+) per cubic meter and n chloride ions (cI-) per cubic meter. A battery is connected to metal. A battery is connected to metal rods that dip into a narrow pipe full of the salt water. the cross sectional area of the pipe is A what is the direction of conventional current flow in the salt water? 1. to the right, 2. to the left, 3. there is no conventional current because the motion of the positive and negative ions cancel each other out.

## salt water contains n sodium ions (Na+) per cubic meter and n chloride ions (cI-) per cubic meter. A battery is connected to metal. A battery is connected to metal rods that dip into a narrow pipe full of the salt water. the cross sectional area of the pipe is A what is the direction of conventional current flow in the salt water? 1. to the right, 2. to the left, 3. there is no conventional current because the motion of the positive and negative ions cancel each other out.

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Figure 1: Examples of the 16-puzzle. The 16-puzzle consists of 15 tiles containing the numbers 1; 2; : : : ; 15 in a 4  4 grid, with an empty space left by the missing 16th tile. The goal of the 16-puzzle is to rearrange the tiles into order by sliding tiles to occupy an empty space. Figure 1 shows a sample board along with the goal con guration where the tiles are in order. A similar puzzle can be devised for any n  n board. On a board with N positions (including the empty space), the total number of possible con gurations is N!, since every arrangement of tiles can be encoded by a permutation of f1; : : : ;Ng (where the empty space is treated as an invisible tile marked with N), although some con gurations cannot be solved. The game graph for the N-puzzle contains vertices for each possible board, and an undirected edge connects every pair of boards which can be transformed into each other by one move. Since every move is reversible (that is, we can always move a tile back after the initial move), there is no need for directed edges. The game graph for the 4-puzzle contains only 4! = 24 states, and is shown in gure 2. The goal state is framed in green. The 9-puzzle has 9! = 362880 states, so it is possible to compute and store the entire game graph on a current machine. Graph algorithms can then be used to nd solutions to each board. For example, a path from a given board b to the goal con guration g (in which all tiles are in order and the empty space is at the lower right) represents a sequence of valid moves which solve b. If g is not reachable from b, then b has no solution. In general, the game graph of a puzzle may have several di erent connected components, and there may not be a goal state in each component. The game graph for an N-puzzle always has two components, and there is only one goal state. Algorithms for nding connected components can be used to nd all solvable con gurations of a puzzle. For the N-puzzle, it is also possible to determine whether a given board is solvable without traversing the game graph by using techniques from permutation theory (which is beyond the scope of this course). Figure 3 shows the neighbourhood of the goal state of the 9-puzzle. Algorithm 27 gives pseu- docode to build the game graph of an N puzzle. 1 Figure 2: The entire game graph for the 4-puzzle, with the goal state framed in green. Figure 3: A subset of the game graph for the 9-puzzle, with the goal state framed in green. 2 3

## Figure 1: Examples of the 16-puzzle. The 16-puzzle consists of 15 tiles containing the numbers 1; 2; : : : ; 15 in a 4  4 grid, with an empty space left by the missing 16th tile. The goal of the 16-puzzle is to rearrange the tiles into order by sliding tiles to occupy an empty space. Figure 1 shows a sample board along with the goal con guration where the tiles are in order. A similar puzzle can be devised for any n  n board. On a board with N positions (including the empty space), the total number of possible con gurations is N!, since every arrangement of tiles can be encoded by a permutation of f1; : : : ;Ng (where the empty space is treated as an invisible tile marked with N), although some con gurations cannot be solved. The game graph for the N-puzzle contains vertices for each possible board, and an undirected edge connects every pair of boards which can be transformed into each other by one move. Since every move is reversible (that is, we can always move a tile back after the initial move), there is no need for directed edges. The game graph for the 4-puzzle contains only 4! = 24 states, and is shown in gure 2. The goal state is framed in green. The 9-puzzle has 9! = 362880 states, so it is possible to compute and store the entire game graph on a current machine. Graph algorithms can then be used to nd solutions to each board. For example, a path from a given board b to the goal con guration g (in which all tiles are in order and the empty space is at the lower right) represents a sequence of valid moves which solve b. If g is not reachable from b, then b has no solution. In general, the game graph of a puzzle may have several di erent connected components, and there may not be a goal state in each component. The game graph for an N-puzzle always has two components, and there is only one goal state. Algorithms for nding connected components can be used to nd all solvable con gurations of a puzzle. For the N-puzzle, it is also possible to determine whether a given board is solvable without traversing the game graph by using techniques from permutation theory (which is beyond the scope of this course). Figure 3 shows the neighbourhood of the goal state of the 9-puzzle. Algorithm 27 gives pseu- docode to build the game graph of an N puzzle. 1 Figure 2: The entire game graph for the 4-puzzle, with the goal state framed in green. Figure 3: A subset of the game graph for the 9-puzzle, with the goal state framed in green. 2 3

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two current carrying wires . wire # 1carries a current I into the page . wire #2 carries a current I eighter into or out of page. If the net magnetic field at point A to the left . 1) I is in and I>I, 2) I is out and I>I, 3) I is in and I<I, 4) I is out and I<I, 5) I=I