what is Hamilton path and Euler path?
History of Euler path and Hamilton path
Vertex(node) and edge
Hamilton path and Hamilton circuit
Euler path and Euler circuit
Degree of vertex and comparison of Euler and Hamilton path
Solving a problem
Permutations involve arranging objects in a particular order, where order matters. There are a few different formulas to calculate the number of permutations depending on the situation. The basic formula for permutations of n objects taken r at a time is nPr = n!/(n-r)!. Some examples provided calculate the number of permutations when arranging letters, skiers in a competition, and people entering a cave.
This document discusses the Hamiltonian path problem in graph theory. A Hamiltonian path visits each vertex in a graph exactly once. The Hamiltonian path problem is determining if a Hamiltonian path exists in a given graph. It is computationally difficult to solve and several algorithms have been developed, including brute force search, dynamic programming, and Monte Carlo algorithms. Unconventional models of computing like DNA computers have also been used to attempt solving the Hamiltonian path problem by exploiting parallel chemical reactions.
- Karnaugh maps are used to simplify Boolean algebra expressions by grouping adjacent 1s in a two-dimensional grid.
- Groups must contain powers of 2 cells and cannot include any 0s. They can overlap and wrap around the map.
- The simplified expression is obtained by determining which variables stay the same within each group.
This document discusses trees and their applications in discrete mathematics. It begins with an introduction to trees, including defining different types of trees such as rooted trees, m-ary trees, and binary trees. It then covers applications of trees such as binary search trees, decision trees, and game trees. Next, it discusses different tree traversal algorithms like preorder, inorder, and postorder traversal and how they can be used to represent expressions. Finally, it provides examples of evaluating expressions represented as trees.
Python programming | Fundamentals of Python programming KrishnaMildain
Basic Fundamentals of Python Programming.
What is Python, History of python, Advantages, Disadvantages, feature of python, scope, and many more.
Data Structure using Python, Object Oriented Programming using
This document discusses graph coloring and its applications. It begins by defining graph coloring as assigning labels or colors to elements of a graph such that no two adjacent elements have the same color. It then provides examples of vertex coloring, edge coloring, and face coloring. The document also discusses the chromatic number and chromatic polynomial. It describes several real-world applications of graph coloring, including frequency assignment in cellular networks.
This document introduces network flows and provides examples of networks where flows occur, such as liquids in pipes and goods transported on roads. It defines key concepts like sources, sinks, flows, and cuts. It describes how to find a maximum flow and minimum cut in a network and presents theorems relating feasible flows and cut capacities. The maximum flow problem is to determine the largest amount of flow that can pass from sources to sinks in a network respecting edge capacities.
The document discusses graphs and Eulerian circuits and paths. It defines what a graph is composed of and defines an Eulerian circuit and path. It states that a graph must be connected and have all vertices visited once for an Eulerian circuit, or have two odd vertices for an Eulerian path. Fleury's algorithm is described for finding an Euler circuit or path by traversing edges without crossing bridges twice. The algorithm works by choosing the next edge such that bridges are only crossed if necessary. Examples are given to demonstrate the algorithm. Applications mentioned include the Chinese postman problem and communicating networks.
This document introduces some basic concepts in graph theory, including:
- A graph G is defined as a pair (V,E) where V is the set of vertices and E is the set of edges.
- Edges connect pairs of vertices and can be directed or undirected. Special types of edges include parallel edges and loops.
- Special graphs include simple graphs without parallel edges/loops, weighted graphs with numerical edge weights, and complete graphs where all vertex pairs are connected.
- Graphs can be represented by adjacency matrices and incidence matrices showing vertex-edge connections.
- Paths and cycles traverse vertices and edges, with Euler cycles passing through every edge once.
This document discusses graph coloring, which involves assigning colors to the vertices of a graph such that no two adjacent vertices have the same color. It provides examples of problems that can be modeled as graph coloring, such as scheduling committee meetings. The key points covered include defining graph coloring and chromatic number, discussing greedy algorithms and their limitations for graph coloring, and presenting the Welsh-Powell algorithm as an approach to graph coloring.
The document discusses graph coloring and its applications. It defines graph coloring as assigning colors to graph vertices such that no adjacent vertices have the same color. It also discusses the four color theorem, which states that any planar map can be colored with four or fewer colors. Finally, it provides an overview of backtracking as an algorithmic approach for solving graph coloring problems.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
Graph coloring involves assigning colors to objects in a graph subject to constraints. Vertex coloring assigns colors to vertices such that no adjacent vertices share the same color. The chromatic number is the minimum number of colors needed. Graph coloring has applications in scheduling, frequency assignment, and register allocation. Register allocation with chordal graphs can be done in polynomial time rather than NP-complete, since programs in SSA form have chordal interference graphs. A greedy algorithm can color chordal graphs in linear time, enabling simple and optimal register allocation.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
The document provides an introduction to algorithms through a lecture on fundamentals of algorithm analysis. It defines an algorithm as a finite sequence of unambiguous instructions to solve a problem. Characteristics of algorithms like inputs, outputs, definiteness and finiteness are discussed. The document also describes various algorithm design techniques like brute force, divide and conquer and greedy algorithms. It explains steps to write algorithms using pseudo code and discusses validating, analyzing, testing programs and specifying algorithms through pseudo code and flowcharts.
A graph consists of vertices and edges, where vertices represent entities and edges represent relationships between vertices. Graphs can be represented sequentially using matrices like adjacency and incidence matrices, or linked using data structures like adjacency lists. Adjacency matrices allow fast addition/removal of edges but use more memory, while adjacency lists use less memory but are slower to modify. The best representation depends on whether the graph is dense or sparse.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
This document provides an overview of graph theory, including its history and key concepts. It defines a graph as a set of nodes connected by edges. Important early contributors are noted, such as Euler's work on the Seven Bridges of Königsberg problem. Exact definitions of graph types like simple, directed, and weighted graphs are given. Key graph concepts explained in less than three sentences include connectivity, degree, paths, cycles, and trees. The document also briefly discusses representing graphs through adjacency lists and matrices. It concludes by thanking the reader and listing references.
Heuristic Search in Artificial Intelligence | Heuristic Function in AI | Admi...RahulSharma4566
Heuristic search uses heuristic functions to help optimize problem solving by trying to find solutions in the fewest steps or lowest cost. A heuristic function estimates the cost of reaching the goal state from any given node. There are two types of heuristic functions: admissible functions, which never overestimate cost, and non-admissible functions, which may overestimate cost. Admissible heuristics help guide search towards optimal solutions.
This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
In graph theory, graph coloring is a special case of graph labeling; it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints. In its simplest form, it is a way of coloring the vertices of a graph such that no two adjacent vertices share the same color; this is called a vertex coloring. Similarly, an edge coloring assigns a color to each edge so that no two adjacent edges share the same color, and a face coloring of a planar graph assigns a color to each face or region so that no two faces that share a boundary have the same color.
This document is a project report submitted by S. Manikanta in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report discusses applications of graph theory. It provides an overview of graph theory concepts such as definitions of graphs, terminology used in graph theory, different types of graphs, trees and forests, graph isomorphism and operations, walks and paths in graphs, representations of graphs using matrices, applications of graphs in areas like computer science, fingerprint recognition, security, and more. The document also includes examples and illustrations to explain various graph theory concepts.
This document defines and provides examples of graphs and their representations. It discusses:
- Graphs are data structures consisting of nodes and edges connecting nodes.
- Examples of directed and undirected graphs are given.
- Graphs can be represented using adjacency matrices or adjacency lists. Adjacency matrices store connections in a grid and adjacency lists store connections as linked lists.
- Key graph terms are defined such as vertices, edges, paths, and degrees. Properties like connectivity and completeness are also discussed.
This document discusses approximation algorithms for solving NP-hard problems like the traveling salesman problem (TSP) and knapsack problem. It provides an overview of approximation algorithms, defining them as polynomial-time algorithms that provide good but not necessarily optimal solutions. The document then focuses on approximation algorithms for the TSP, describing greedy algorithms like nearest neighbor, minimum spanning tree based algorithms like Christofides, and local search heuristics like 2-opt and Lin-Kernighan. It concludes by noting some applications of approximating the TSP.
Graph theory is the study of graphs, which are mathematical structures used to model pairwise relationships between objects. A graph consists of vertices and edges that connect the vertices. Graph theory is used in computer science to model problems that can be represented as networks, such as determining routes in a city or designing circuits. It allows analyzing properties of graphs like connectivity and determining the minimum number of resources required. A graph can be represented using an adjacency matrix or adjacency list to store information about its vertices and edges.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees of vertices, walks, paths, circuits, subgraphs, complete graphs, planar graphs, Eulerian graphs, and Hamiltonian graphs. Graph theory has many applications including modeling networks, optimization problems, and molecular structures. The origins of graph theory can be traced back to Euler's solution to the Königsberg bridge problem in 1736.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees, walks, paths, circuits, trees, planar graphs and non-planar graphs. Graph theory originated from Euler's solution to the Königsberg bridge problem in 1736. Important applications of graph theory include modeling networks, optimization problems, and molecular structures.
The document discusses graphs and Eulerian circuits and paths. It defines what a graph is composed of and defines an Eulerian circuit and path. It states that a graph must be connected and have all vertices visited once for an Eulerian circuit, or have two odd vertices for an Eulerian path. Fleury's algorithm is described for finding an Euler circuit or path by traversing edges without crossing bridges twice. The algorithm works by choosing the next edge such that bridges are only crossed if necessary. Examples are given to demonstrate the algorithm. Applications mentioned include the Chinese postman problem and communicating networks.
This document introduces some basic concepts in graph theory, including:
- A graph G is defined as a pair (V,E) where V is the set of vertices and E is the set of edges.
- Edges connect pairs of vertices and can be directed or undirected. Special types of edges include parallel edges and loops.
- Special graphs include simple graphs without parallel edges/loops, weighted graphs with numerical edge weights, and complete graphs where all vertex pairs are connected.
- Graphs can be represented by adjacency matrices and incidence matrices showing vertex-edge connections.
- Paths and cycles traverse vertices and edges, with Euler cycles passing through every edge once.
This document discusses graph coloring, which involves assigning colors to the vertices of a graph such that no two adjacent vertices have the same color. It provides examples of problems that can be modeled as graph coloring, such as scheduling committee meetings. The key points covered include defining graph coloring and chromatic number, discussing greedy algorithms and their limitations for graph coloring, and presenting the Welsh-Powell algorithm as an approach to graph coloring.
The document discusses graph coloring and its applications. It defines graph coloring as assigning colors to graph vertices such that no adjacent vertices have the same color. It also discusses the four color theorem, which states that any planar map can be colored with four or fewer colors. Finally, it provides an overview of backtracking as an algorithmic approach for solving graph coloring problems.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
Graph coloring involves assigning colors to objects in a graph subject to constraints. Vertex coloring assigns colors to vertices such that no adjacent vertices share the same color. The chromatic number is the minimum number of colors needed. Graph coloring has applications in scheduling, frequency assignment, and register allocation. Register allocation with chordal graphs can be done in polynomial time rather than NP-complete, since programs in SSA form have chordal interference graphs. A greedy algorithm can color chordal graphs in linear time, enabling simple and optimal register allocation.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
The document provides an introduction to algorithms through a lecture on fundamentals of algorithm analysis. It defines an algorithm as a finite sequence of unambiguous instructions to solve a problem. Characteristics of algorithms like inputs, outputs, definiteness and finiteness are discussed. The document also describes various algorithm design techniques like brute force, divide and conquer and greedy algorithms. It explains steps to write algorithms using pseudo code and discusses validating, analyzing, testing programs and specifying algorithms through pseudo code and flowcharts.
A graph consists of vertices and edges, where vertices represent entities and edges represent relationships between vertices. Graphs can be represented sequentially using matrices like adjacency and incidence matrices, or linked using data structures like adjacency lists. Adjacency matrices allow fast addition/removal of edges but use more memory, while adjacency lists use less memory but are slower to modify. The best representation depends on whether the graph is dense or sparse.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
This document provides an overview of graph theory, including its history and key concepts. It defines a graph as a set of nodes connected by edges. Important early contributors are noted, such as Euler's work on the Seven Bridges of Königsberg problem. Exact definitions of graph types like simple, directed, and weighted graphs are given. Key graph concepts explained in less than three sentences include connectivity, degree, paths, cycles, and trees. The document also briefly discusses representing graphs through adjacency lists and matrices. It concludes by thanking the reader and listing references.
Heuristic Search in Artificial Intelligence | Heuristic Function in AI | Admi...RahulSharma4566
Heuristic search uses heuristic functions to help optimize problem solving by trying to find solutions in the fewest steps or lowest cost. A heuristic function estimates the cost of reaching the goal state from any given node. There are two types of heuristic functions: admissible functions, which never overestimate cost, and non-admissible functions, which may overestimate cost. Admissible heuristics help guide search towards optimal solutions.
This document outlines greedy algorithms, their characteristics, and examples of their use. Greedy algorithms make locally optimal choices at each step in the hopes of finding a global optimum. They are simple to implement and fast, but may not always reach the true optimal solution. Examples discussed include coin changing, traveling salesman, minimum spanning trees using Kruskal's and Prim's algorithms, and Huffman coding.
In graph theory, graph coloring is a special case of graph labeling; it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints. In its simplest form, it is a way of coloring the vertices of a graph such that no two adjacent vertices share the same color; this is called a vertex coloring. Similarly, an edge coloring assigns a color to each edge so that no two adjacent edges share the same color, and a face coloring of a planar graph assigns a color to each face or region so that no two faces that share a boundary have the same color.
This document is a project report submitted by S. Manikanta in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report discusses applications of graph theory. It provides an overview of graph theory concepts such as definitions of graphs, terminology used in graph theory, different types of graphs, trees and forests, graph isomorphism and operations, walks and paths in graphs, representations of graphs using matrices, applications of graphs in areas like computer science, fingerprint recognition, security, and more. The document also includes examples and illustrations to explain various graph theory concepts.
This document defines and provides examples of graphs and their representations. It discusses:
- Graphs are data structures consisting of nodes and edges connecting nodes.
- Examples of directed and undirected graphs are given.
- Graphs can be represented using adjacency matrices or adjacency lists. Adjacency matrices store connections in a grid and adjacency lists store connections as linked lists.
- Key graph terms are defined such as vertices, edges, paths, and degrees. Properties like connectivity and completeness are also discussed.
This document discusses approximation algorithms for solving NP-hard problems like the traveling salesman problem (TSP) and knapsack problem. It provides an overview of approximation algorithms, defining them as polynomial-time algorithms that provide good but not necessarily optimal solutions. The document then focuses on approximation algorithms for the TSP, describing greedy algorithms like nearest neighbor, minimum spanning tree based algorithms like Christofides, and local search heuristics like 2-opt and Lin-Kernighan. It concludes by noting some applications of approximating the TSP.
Graph theory is the study of graphs, which are mathematical structures used to model pairwise relationships between objects. A graph consists of vertices and edges that connect the vertices. Graph theory is used in computer science to model problems that can be represented as networks, such as determining routes in a city or designing circuits. It allows analyzing properties of graphs like connectivity and determining the minimum number of resources required. A graph can be represented using an adjacency matrix or adjacency list to store information about its vertices and edges.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees of vertices, walks, paths, circuits, subgraphs, complete graphs, planar graphs, Eulerian graphs, and Hamiltonian graphs. Graph theory has many applications including modeling networks, optimization problems, and molecular structures. The origins of graph theory can be traced back to Euler's solution to the Königsberg bridge problem in 1736.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees, walks, paths, circuits, trees, planar graphs and non-planar graphs. Graph theory originated from Euler's solution to the Königsberg bridge problem in 1736. Important applications of graph theory include modeling networks, optimization problems, and molecular structures.
This document section summarizes information about Euler paths and circuits as well as Hamilton paths and circuits. It discusses:
- Euler's solving of the Königsberg bridge problem and defining Euler paths/circuits
- Necessary and sufficient conditions for a graph to have an Euler path/circuit
- Algorithms for constructing Euler circuits
- Applications of Euler paths/circuits
- Defining Hamilton paths/circuits and discussing properties like Dirac's and Ore's theorems for Hamilton circuits
- Differences between Euler and Hamilton circuits
This document discusses the history and key concepts of graph theory. It begins by describing how Leonhard Euler established the foundations of the field with his solving of the Seven Bridges of Königsberg problem in 1736. Some important graph concepts introduced include vertices, edges, complete graphs, directed graphs, tournaments, Euler paths and circuits, and Hamiltonian circuits. The Chinese Postman problem is also briefly described. Key terms like node, circuit, and edge are then defined based on their Latin and English word origins.
The document discusses Hamilton paths and circuits, which are paths or circuits in a graph that visit each vertex exactly once. It provides examples of graphs that do and do not have Hamilton paths/circuits. It also discusses the number of Hamilton circuits in complete graphs KN, which is (N-1)!. Complete graphs have the maximum possible number of edges between N vertices.
The document discusses different concepts in graph theory including:
- Euler paths and circuits which travel along each edge of a graph once. An Euler circuit is a closed path.
- Hamiltonian paths and circuits which pass through each vertex of a graph once.
- Weighted graphs where weights are assigned to edges to represent attributes like distance or cost.
- Networks which are weighted graphs used to model real-world systems like transportation or computing.
- Critical paths which identify the minimum time needed to complete interconnected tasks in a network.
This document provides an introduction to graphs and graph terminology. It defines what a graph is composed of, including vertices and edges. It gives examples of graphs and has the student practice identifying vertices and edges. It also introduces common graph terminology like degree of a vertex, adjacent vertices, paths, circuits, bridges, Euler paths, and Euler circuits. Fleury's algorithm for finding an Euler circuit or path in a graph is described. The document uses examples and exercises to help students learn and practice applying these graph concepts.
The document discusses graph algorithms and their applications in bioinformatics. It introduces graph theory concepts like the Hamiltonian cycle problem and describes how it relates to finding knight tours on a chessboard. It also discusses how the traveling salesman problem and Hamiltonian cycle problem are connected and how graph algorithms can be used for problems in DNA sequencing and fragment assembly.
Graph algorithms have important applications in bioinformatics. Seymour Benzer used graph theory and interval graphs to study the structure of viral genomes. Through his experiment infecting bacteria with pairs of disabled bacteriophages, Benzer was able to show that the genome of these viruses was linear rather than branched based on whether the interval graph formed was interval or not. This provided insight into viral genome structure using mathematical concepts like graphs and intervals.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
This document defines and provides examples of Euler paths, Euler circuits, Hamilton paths, and Hamilton circuits. An Euler path uses every edge in a graph once but starts and ends at different vertices, while an Euler circuit both uses every edge once and starts and ends at the same vertex. Similarly, a Hamilton path visits each vertex once but starts and ends at different vertices, whereas a Hamilton circuit visits each vertex once and starts and ends at the same vertex. Step-by-step processes are provided for determining if an Euler or Hamilton path or circuit exists in a graph.
Fleury's algorithm is a method for finding an Euler circuit or path in a graph. It starts at a vertex and chooses edges to traverse such that it never chooses a bridge edge unless it is the only option, in order to avoid disconnecting the graph. This ensures an Eulerian path or circuit can be found if one exists. The algorithm labels the edges as it traverses them to reconstruct the path or circuit. Applications include finding efficient routes for mail carriers or tourists.
This document discusses Fleury's algorithm, a method for finding an Euler path or circuit in a graph. It begins by defining key graph theory concepts like bridges, odd and even vertices. It then formally presents Fleury's algorithm, which starts at an odd vertex and traverses edges while avoiding bridges. An example applies the algorithm to find an Euler circuit. The document notes Fleury's algorithm is exhaustive, optimal, and efficient. It provides additional examples applying the algorithm and discusses Eulerizing graphs by adding edges to make all vertices even.
The document describes the Konigsberg bridge problem and its solution by Euler. It discusses that Konigsberg (now Kaliningrad) had islands connected by seven bridges, and residents wondered if one could walk across each bridge once and return to their starting point. Euler proved this was impossible by modeling the problem as a graph with four vertices (land areas) and seven edges (bridges). He discovered all four vertices were odd degrees, meaning they were not traversable. The document also discusses how adding bridges could make the graph traversable and introduces basic graph theory concepts.
The document defines key concepts in graph theory including:
- Types of graphs such as simple graphs, connected graphs, and regular graphs.
- Graph terminology like vertices, edges, walks, paths, and subgraphs.
- Special graphs like Hamiltonian and Euler graphs.
- Graph coloring problems including vertex coloring and edge coloring.
- Examples are given to illustrate graph concepts and properties.
A Hamiltonian path is a path that visits each vertex of the graph exactly once.
A Hamiltonian circuit is a path that uses each vertex of a graph exactly once and returns to the starting vertex.
This document discusses graph theory and the Eulerian graph and Chinese Postman problems. It begins by describing the Seven Bridges of Konigsberg problem and how it can be represented by a graph. It then defines an Eulerian graph as a connected graph where every vertex has an even degree, allowing an Eulerian circuit that uses each edge once. The Chinese Postman problem is described as finding the shortest route to traverse all edges of a graph that may not be Eulerian by pairing odd degree vertices. An algorithm is provided to solve the Chinese Postman problem through modifying the graph and finding an Euler circuit. An example graph is used to illustrate the process.
The document discusses Euler and Hamiltonian graphs. It defines an Euler graph as one that contains an Euler circuit, which is a circuit containing every edge exactly once except the first and last vertex. An Euler path contains every edge once. It states the properties for an undirected graph to have an Euler path/circuit. It then defines a Hamiltonian graph as one containing a Hamiltonian circuit, which visits every vertex once except the first and last. Hamiltonian path is also defined. Properties of Hamiltonian graphs are discussed.
Odoo Inventory Rules and Routes v17 - Odoo SlidesCeline George
Odoo's inventory management system is highly flexible and powerful, allowing businesses to efficiently manage their stock operations through the use of Rules and Routes.
How to Set warnings for invoicing specific customers in odooCeline George
Odoo 16 offers a powerful platform for managing sales documents and invoicing efficiently. One of its standout features is the ability to set warnings and block messages for specific customers during the invoicing process.
The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
How to Manage Opening & Closing Controls in Odoo 17 POSCeline George
In Odoo 17 Point of Sale, the opening and closing controls are key for cash management. At the start of a shift, cashiers log in and enter the starting cash amount, marking the beginning of financial tracking. Throughout the shift, every transaction is recorded, creating an audit trail.
As of Mid to April Ending, I am building a new Reiki-Yoga Series. No worries, they are free workshops. So far, I have 3 presentations so its a gradual process. If interested visit: https://www.slideshare.net/YogaPrincess
https://ldmchapels.weebly.com
Blessings and Happy Spring. We are hitting Mid Season.
A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CBSE - Grade 8 - Science - Chemistry - Metals and Non Metals - WorksheetSritoma Majumder
Introduction
All the materials around us are made up of elements. These elements can be broadly divided into two major groups:
Metals
Non-Metals
Each group has its own unique physical and chemical properties. Let's understand them one by one.
Physical Properties
1. Appearance
Metals: Shiny (lustrous). Example: gold, silver, copper.
Non-metals: Dull appearance (except iodine, which is shiny).
2. Hardness
Metals: Generally hard. Example: iron.
Non-metals: Usually soft (except diamond, a form of carbon, which is very hard).
3. State
Metals: Mostly solids at room temperature (except mercury, which is a liquid).
Non-metals: Can be solids, liquids, or gases. Example: oxygen (gas), bromine (liquid), sulphur (solid).
4. Malleability
Metals: Can be hammered into thin sheets (malleable).
Non-metals: Not malleable. They break when hammered (brittle).
5. Ductility
Metals: Can be drawn into wires (ductile).
Non-metals: Not ductile.
6. Conductivity
Metals: Good conductors of heat and electricity.
Non-metals: Poor conductors (except graphite, which is a good conductor).
7. Sonorous Nature
Metals: Produce a ringing sound when struck.
Non-metals: Do not produce sound.
Chemical Properties
1. Reaction with Oxygen
Metals react with oxygen to form metal oxides.
These metal oxides are usually basic.
Non-metals react with oxygen to form non-metallic oxides.
These oxides are usually acidic.
2. Reaction with Water
Metals:
Some react vigorously (e.g., sodium).
Some react slowly (e.g., iron).
Some do not react at all (e.g., gold, silver).
Non-metals: Generally do not react with water.
3. Reaction with Acids
Metals react with acids to produce salt and hydrogen gas.
Non-metals: Do not react with acids.
4. Reaction with Bases
Some non-metals react with bases to form salts, but this is rare.
Metals generally do not react with bases directly (except amphoteric metals like aluminum and zinc).
Displacement Reaction
More reactive metals can displace less reactive metals from their salt solutions.
Uses of Metals
Iron: Making machines, tools, and buildings.
Aluminum: Used in aircraft, utensils.
Copper: Electrical wires.
Gold and Silver: Jewelry.
Zinc: Coating iron to prevent rusting (galvanization).
Uses of Non-Metals
Oxygen: Breathing.
Nitrogen: Fertilizers.
Chlorine: Water purification.
Carbon: Fuel (coal), steel-making (coke).
Iodine: Medicines.
Alloys
An alloy is a mixture of metals or a metal with a non-metal.
Alloys have improved properties like strength, resistance to rusting.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
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The ever evoilving world of science /7th class science curiosity /samyans aca...Sandeep Swamy
The Ever-Evolving World of
Science
Welcome to Grade 7 Science4not just a textbook with facts, but an invitation to
question, experiment, and explore the beautiful world we live in. From tiny cells
inside a leaf to the movement of celestial bodies, from household materials to
underground water flows, this journey will challenge your thinking and expand
your knowledge.
Notice something special about this book? The page numbers follow the playful
flight of a butterfly and a soaring paper plane! Just as these objects take flight,
learning soars when curiosity leads the way. Simple observations, like paper
planes, have inspired scientific explorations throughout history.
2. Things we are going to discuss
History of Euler path and Hamilton path
Vertex(node) and edge
Hamilton path and Hamilton circuit
Euler path and Euler circuit
Degree of vertex and comparison of Euler and Hamilton path
Solving a problem
3. History of Euler Path
Euler path was first discussed by
Leonhard Euler while solving the famous
Seven Bridges of Konigsberg problem
in 1736. The problem can be stated
mathematically like this:
a
b
c
d
4. History of Hamilton Path
Hamiltonian paths and cycles
are named after William Rowan
Hamilton who invented the
ICOSIAN game, now also
known as Hamilton's puzzle,
which involves finding a
Hamiltonian cycle in the edge
graph of the dodecahedron
6. Edge:
An edge is a line segment that
joins two vertices
Edge
Edge
7. Hamiltonian Path and circuits
In graph theory, an Hamilton path is a path which visits
every vertex exactly once. Similarly, an Hamilton circuit is a
circuit which starts and ends on the same vertex.
8. This has a Hamilton Path and a
Hamilton circuit
This has a Hamilton Path , but does not
have a Hamilton circuit
This has no Hamilton Path
9. Euler path and circuit
In graph theory, an Euler path is a path
which visits every edge exactly once.
Similarly, an Eulerian circuit or Eulerian
cycle is an Eulerian trail which starts and
ends on the same vertex.
10. This has an Euler Path and an Euler
circuit
This has an Euler Path , but does not
have an Euler circuit
This has no Hamilton Path
12. Comparison of Euler and Hamilton circuits
Property Euler Hamilton
Repeated visits to a
given node allowed?
Repeated traversals of a
given edge allowed?
Omitted nodes allowed?
Omitted edges allowed?
YES NO
NO NO
NO NO
NO YES
13. in the image, is it possible to
construct a path ( a path starting and
ending on the same vertex) which
visits each edge exactly once?
Seven Bridges Problem
A
B
C
D
a
b
c
d
14. Solve
We know that ,
An Euler Circuit is possible if every vertex has
even degree.
An Euler path is possible but Euler Circuit is
possible ,if exactly two vertices have odd
degree.
No Euler Path is possible if more than two
vertices have odd degree.
a
b
c
d
THERE IS NO EULERS PATH