This slide give the basic introduction about UML diagram and it's types, and brief intro about Activity Diagram, use of activity diagram in object oriented programming language..
This document provides an overview of trees as a non-linear data structure. It begins by discussing how trees are used to represent hierarchical relationships and defines some key tree terminology like root, parent, child, leaf, and subtree. It then explains that a tree consists of nodes connected in a parent-child relationship, with one root node and nodes that may have any number of children. The document also covers tree traversal methods like preorder, inorder, and postorder traversal. It introduces binary trees and binary search trees, and discusses operations on BSTs like search, insert, and delete. Finally, it provides a brief overview of the Huffman algorithm for data compression.
This document provides an overview of 3D computer animation, including a brief history, definitions, benefits, software used, careers, and examples. It discusses how 3D animation works by storing individual images that are played back at 30 frames per second to create movement. Common software includes Maya, 3DS Max, and Softimage. The field is growing rapidly and is used extensively in movies, games, and visual effects. Careers include 3D modelers, animators, and art directors. Pixar, Disney, and Dreamworks are examples of companies that produce computer animated films like Toy Story, Shrek, and Harry Potter.
The document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements that consider both the elements and their relationships. It classifies data structures as either primitive or non-primitive. Primitive structures are directly operated on by machine instructions while non-primitive structures are built from primitive ones. Common non-primitive structures include stacks, queues, linked lists, trees and graphs. The document then discusses arrays as a data structure and operations on arrays like traversal, insertion, deletion, searching and sorting.
There are 6 types of CSS selectors: simple, class, generic, ID, universal, and pseudo-class selectors. Simple selectors apply styles to single elements. Class selectors allow assigning different styles to the same element on different occurrences. ID selectors define special styles for specific elements. Generic selectors define styles that can be applied to any tag. Universal selectors apply styles to all elements on a page. Pseudo-class selectors give special effects like focus and hover.
Clipper circuits are used to remove parts of a signal that are above or below a defined reference level. There are several types of clipper circuits: unbiased positive and negative clippers which clip either the positive or negative portions of a signal, and biased positive and negative clippers which use an external bias voltage to adjust the clipping level. Unbiased clippers cut off either the positive or negative half of the input waveform based on the diode configuration. Biased clippers allow changing the clipping level by adjusting the bias voltage applied in series with the input signal and diode.
Waste management involves the collection, transport, treatment, and disposal of waste, as well as monitoring and regulation. It also includes the legal framework around guidance for recycling. Modern concepts of waste management focus on reducing, reusing, and recycling waste over disposal. Improper waste management can lead to environmental contamination of air, soil, surface water and groundwater. It can also negatively impact public health and the economy. However, proper waste management through recycling saves resources, prevents pollution, and creates jobs and economic opportunities.
Data Structure & aaplications_Module-1.pptxGIRISHKUMARBC1
This document provides information about the course "Data Structures and Applications". The course aims to explain fundamentals of data structures and their applications for programming. It will cover linear and non-linear data structures like stacks, queues, lists, trees and graphs. Students will learn sorting and searching algorithms and how to select suitable data structures for application development and problem solving.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
The document discusses applications of stacks, including reversing strings and lists, Polish notation for mathematical expressions, converting between infix, prefix and postfix notations, evaluating postfix and prefix expressions, recursion, and the Tower of Hanoi problem. Recursion involves defining a function in terms of itself, with a stopping condition. Stacks can be used to remove recursion by saving local variables at each step.
Conversion of Infix to Prefix and Postfix with Stacksahil kumar
The document discusses different notations for mathematical expressions - infix, prefix, and postfix. It explains that infix notation places the operator between operands, prefix puts the operator before operands, and postfix places it after. The document also covers converting between notations using a stack and explains operator precedence with parentheses having highest and addition/subtraction lowest precedence. Examples are given of converting infix expressions to postfix and prefix using a stack.
The document discusses arrays in data structures using C programming language. It defines what an array is and describes different types of arrays like one-dimensional, two-dimensional, and multi-dimensional arrays. It also explains array operations such as insertion, deletion, traversal, reversing, sorting, and searching. Additionally, it covers merging of arrays, arrays of pointers, and using arrays to represent polynomials.
The document discusses binary trees and their representations and operations. It defines binary trees as trees where each node has at most two child nodes. It also defines complete binary trees as trees where every node has two children except leaf nodes. The document discusses array and linked representations of binary trees and various traversal operations like preorder, inorder and postorder traversals. It also provides code snippets for inserting and deleting nodes from a binary tree.
Binary trees are a non-linear data structure where each node has at most two children, used to represent hierarchical relationships, with nodes connected through parent-child links and traversed through preorder, inorder, and postorder methods; they can be represented through arrays or linked lists and support common operations like search, insert, and delete through comparing node values and restructuring child pointers.
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
Linked lists are linear data structures where each node contains a data field and a pointer to the next node. There are two types: singly linked lists where each node has a single next pointer, and doubly linked lists where each node has next and previous pointers. Common operations on linked lists include insertion and deletion which have O(1) time complexity for singly linked lists but require changing multiple pointers for doubly linked lists. Linked lists are useful when the number of elements is dynamic as they allow efficient insertions and deletions without shifting elements unlike arrays.
The document discusses abstract data types (ADTs). It defines an ADT as a collection of data together with a set of operations on that data. An ADT specifies what data can be stored and what operations can be performed. Simple ADTs are predefined types like integers, while complex ADTs like lists are user-defined. Lists are presented as an example complex ADT, with elements ordered in a sequence and operations to insert, find, delete and traverse elements.
Each node in a doubly linked list contains two pointers - one pointing to the next node and one pointing to the previous node. This allows traversal in both directions through the list. A doubly linked list supports common operations like insertion and deletion at both ends of the list as well as anywhere in the list by updating the relevant pointer fields of the nodes. While requiring more space per node, doubly linked lists allow easy traversal backwards through the list and reversing of the list.
Breadth First Search & Depth First SearchKevin Jadiya
The slides attached here describes how Breadth first search and Depth First Search technique is used in Traversing a graph/tree with Algorithm and simple code snippet.
The document describes the bubble sort algorithm. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order until the list is fully sorted. Each pass of bubble sort bubbles up the largest remaining element to its correct place near the end of the list. It takes multiple passes, with fewer comparisons each time, to fully sort the list from lowest to highest.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
The document discusses stacks and queues. It defines stacks as LIFO data structures and queues as FIFO data structures. It describes basic stack operations like push and pop and basic queue operations like enqueue and dequeue. It then discusses implementing stacks and queues using arrays and linked lists, outlining the key operations and memory requirements for each implementation.
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.
A queue is a non-primitive linear data structure that follows the FIFO (first-in, first-out) principle. Elements are added to the rear of the queue and removed from the front. Common operations on a queue include insertion (enqueue) and deletion (dequeue). Queues have many real-world applications like waiting in lines and job scheduling. They can be represented using arrays or linked lists.
The document discusses Python's four main collection data types: lists, tuples, sets, and dictionaries. It provides details on lists, including that they are ordered and changeable collections that allow duplicate members. Lists can be indexed, sliced, modified using methods like append() and insert(), and have various built-in functions that can be used on them. Examples are provided to demonstrate list indexing, slicing, changing elements, adding elements, removing elements, and built-in list methods.
This document provides information about priority queues and binary heaps. It defines a binary heap as a nearly complete binary tree where the root node has the maximum/minimum value. It describes heap operations like insertion, deletion of max/min, and increasing/decreasing keys. The time complexity of these operations is O(log n). Heapsort, which uses a heap data structure, is also covered and has overall time complexity of O(n log n). Binary heaps are often used to implement priority queues and for algorithms like Dijkstra's and Prim's.
Python Programming Course Lecture by IoT Code Lab Training.
Discussed Topic:
Chapter 1: Basic Programming
* Operator
* Arithmetic Operation Example
* Assignment Operation Example
* Built in Function Example
* Math Module Example
* Other Operators
* Practice Problem 1.1
* Practice Problem 1.2
* Practice Problem 1.3
The document discusses functions in C++. It begins by showing an example of copying and pasting code to compute 3^4 and 6^5, which is inefficient. It then demonstrates defining a function called raiseToPower() that takes a base and exponent as arguments and computes the power in a reusable way. The document explains the syntax of defining functions, including the return type, arguments, body, and return statement. It also covers topics like function overloading, function prototypes, recursion, and global versus local variables.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
The document discusses applications of stacks, including reversing strings and lists, Polish notation for mathematical expressions, converting between infix, prefix and postfix notations, evaluating postfix and prefix expressions, recursion, and the Tower of Hanoi problem. Recursion involves defining a function in terms of itself, with a stopping condition. Stacks can be used to remove recursion by saving local variables at each step.
Conversion of Infix to Prefix and Postfix with Stacksahil kumar
The document discusses different notations for mathematical expressions - infix, prefix, and postfix. It explains that infix notation places the operator between operands, prefix puts the operator before operands, and postfix places it after. The document also covers converting between notations using a stack and explains operator precedence with parentheses having highest and addition/subtraction lowest precedence. Examples are given of converting infix expressions to postfix and prefix using a stack.
The document discusses arrays in data structures using C programming language. It defines what an array is and describes different types of arrays like one-dimensional, two-dimensional, and multi-dimensional arrays. It also explains array operations such as insertion, deletion, traversal, reversing, sorting, and searching. Additionally, it covers merging of arrays, arrays of pointers, and using arrays to represent polynomials.
The document discusses binary trees and their representations and operations. It defines binary trees as trees where each node has at most two child nodes. It also defines complete binary trees as trees where every node has two children except leaf nodes. The document discusses array and linked representations of binary trees and various traversal operations like preorder, inorder and postorder traversals. It also provides code snippets for inserting and deleting nodes from a binary tree.
Binary trees are a non-linear data structure where each node has at most two children, used to represent hierarchical relationships, with nodes connected through parent-child links and traversed through preorder, inorder, and postorder methods; they can be represented through arrays or linked lists and support common operations like search, insert, and delete through comparing node values and restructuring child pointers.
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
Linked lists are linear data structures where each node contains a data field and a pointer to the next node. There are two types: singly linked lists where each node has a single next pointer, and doubly linked lists where each node has next and previous pointers. Common operations on linked lists include insertion and deletion which have O(1) time complexity for singly linked lists but require changing multiple pointers for doubly linked lists. Linked lists are useful when the number of elements is dynamic as they allow efficient insertions and deletions without shifting elements unlike arrays.
The document discusses abstract data types (ADTs). It defines an ADT as a collection of data together with a set of operations on that data. An ADT specifies what data can be stored and what operations can be performed. Simple ADTs are predefined types like integers, while complex ADTs like lists are user-defined. Lists are presented as an example complex ADT, with elements ordered in a sequence and operations to insert, find, delete and traverse elements.
Each node in a doubly linked list contains two pointers - one pointing to the next node and one pointing to the previous node. This allows traversal in both directions through the list. A doubly linked list supports common operations like insertion and deletion at both ends of the list as well as anywhere in the list by updating the relevant pointer fields of the nodes. While requiring more space per node, doubly linked lists allow easy traversal backwards through the list and reversing of the list.
Breadth First Search & Depth First SearchKevin Jadiya
The slides attached here describes how Breadth first search and Depth First Search technique is used in Traversing a graph/tree with Algorithm and simple code snippet.
The document describes the bubble sort algorithm. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order until the list is fully sorted. Each pass of bubble sort bubbles up the largest remaining element to its correct place near the end of the list. It takes multiple passes, with fewer comparisons each time, to fully sort the list from lowest to highest.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
The document discusses stacks and queues. It defines stacks as LIFO data structures and queues as FIFO data structures. It describes basic stack operations like push and pop and basic queue operations like enqueue and dequeue. It then discusses implementing stacks and queues using arrays and linked lists, outlining the key operations and memory requirements for each implementation.
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.
A queue is a non-primitive linear data structure that follows the FIFO (first-in, first-out) principle. Elements are added to the rear of the queue and removed from the front. Common operations on a queue include insertion (enqueue) and deletion (dequeue). Queues have many real-world applications like waiting in lines and job scheduling. They can be represented using arrays or linked lists.
The document discusses Python's four main collection data types: lists, tuples, sets, and dictionaries. It provides details on lists, including that they are ordered and changeable collections that allow duplicate members. Lists can be indexed, sliced, modified using methods like append() and insert(), and have various built-in functions that can be used on them. Examples are provided to demonstrate list indexing, slicing, changing elements, adding elements, removing elements, and built-in list methods.
This document provides information about priority queues and binary heaps. It defines a binary heap as a nearly complete binary tree where the root node has the maximum/minimum value. It describes heap operations like insertion, deletion of max/min, and increasing/decreasing keys. The time complexity of these operations is O(log n). Heapsort, which uses a heap data structure, is also covered and has overall time complexity of O(n log n). Binary heaps are often used to implement priority queues and for algorithms like Dijkstra's and Prim's.
Python Programming Course Lecture by IoT Code Lab Training.
Discussed Topic:
Chapter 1: Basic Programming
* Operator
* Arithmetic Operation Example
* Assignment Operation Example
* Built in Function Example
* Math Module Example
* Other Operators
* Practice Problem 1.1
* Practice Problem 1.2
* Practice Problem 1.3
The document discusses functions in C++. It begins by showing an example of copying and pasting code to compute 3^4 and 6^5, which is inefficient. It then demonstrates defining a function called raiseToPower() that takes a base and exponent as arguments and computes the power in a reusable way. The document explains the syntax of defining functions, including the return type, arguments, body, and return statement. It also covers topics like function overloading, function prototypes, recursion, and global versus local variables.
The document describes a Haskell program that translates characters in one string to characters in another string. It defines a translate function that maps characters from the first string (set1) to the corresponding characters in the second string (set2). A translateString function applies the translate function to a given string, and the main function gets the set1 and set2 strings from arguments, reads stdin, applies translateString, and writes the result to stdout, catching any errors.
The document discusses converting expressions from infix to postfix notation. It explains that stacks are used to perform the conversion by pushing and popping operators. The algorithm scans the expression from left to right, pushes operands to the output and compares operator priorities to determine when to push or pop from the stack.
The document outlines the steps to formulate an optimization problem including identifying design variables, formulating constraints, formulating the objective function, and setting variable bounds. It then provides an example of optimizing the cross-sectional areas of members in a seven bar truss structure to minimize weight while satisfying stress, stability, stiffness, and other constraints. Classical optimization techniques are summarized, such as single variable methods like bracketing and interval halving, and multi-variable methods including handling equality and inequality constraints.
The document discusses converting expressions from infix to postfix notation. It explains that stacks are used in compilers to perform this conversion. An infix expression contains operators between operands, while a postfix expression has the operator following its operands. The algorithm scans the infix expression left to right, pushing operands and operators onto a stack based on precedence. Operators are popped off and output once higher precedence operators are encountered.
Elixir is a functional programming language that is well-suited for building scalable and fault-tolerant applications. The document provides an introduction to Elixir by discussing its roots in Erlang and how it builds upon Erlang's strengths like concurrency, distribution, and fault tolerance. It also demonstrates some basic Elixir concepts like functions, pattern matching, recursion, and the BEAM virtual machine. Finally, it provides examples of real-world applications of Elixir like building Phoenix web applications and developing embedded hardware projects with Nerves.
The document shows examples of using lambda functions and functional programming techniques in various languages like JavaScript, Python, C#, and Java. It demonstrates how to define anonymous functions, map, filter and reduce collections, use closures, and more. Key examples include summing a list of integers with reduce, filtering even numbers from a list, mapping string transformations, and converting a list of strings to uppercase.
The document discusses stacks and their properties. It defines a stack as a linear data structure where items are inserted and deleted from one end, following the LIFO (last in, first out) principle. Stacks are commonly used in computer systems for tasks like compiler operations and process scheduling. The key stack operations are push to insert an item and pop to remove the top item.
This document provides an introduction to the Haskell programming language. It discusses Haskell's features such as being purely functional, lazily evaluated, statically typed, and supporting currying. It also covers Haskell concepts like functions as first-class citizens, pattern matching, monads, and how Haskell avoids side effects through referential transparency and purity. Examples are given for many of these features to illustrate how they work.
This document provides an introduction to the Haskell programming language. It discusses Haskell's features such as being purely functional, lazily evaluated, statically typed, and supporting currying. It also covers Haskell concepts like functions as first-class citizens, pattern matching, monads, and how Haskell avoids side effects through referential transparency and purity. Examples are given for many of these features to illustrate how they work.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated to any data type. Two functions min() are shown, one for ints and one for doubles, demonstrating the need for templates.
- A template solution defines a single min() function that accepts any type through a template parameter <Type>. This provides a more flexible solution than separate overloaded functions.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated for any data type. Two min() functions showed how templates provide a cleaner solution than separate functions.
- A stack is an abstract data type that follows LIFO (last-in, first-out) behavior. Elements are added and removed only from one end, called the top. Common stack operations are push, pop, empty, and top.
This document discusses monads and continuations in functional programming. It provides examples of using monads like Option and List to handle failure in sequences of operations. It also discusses delimited continuations as a low-level control flow primitive that can implement exceptions, concurrency, and suspensions. The document proposes using monads to pass implicit state through programs by wrapping computations in a state transformer (ST) monad.
This document provides an overview of functional programming concepts in Ruby. It discusses the history and advantages of functional programming, comparing the functional and object-oriented paradigms. It introduces functional programming concepts like pure functions, recursion, immutable data, and higher-order functions. It demonstrates how these concepts can be applied in Ruby using functions, closures, currying, composition, and the Enumerable module. Overall, the document serves as a high-level introduction to thinking functionally in Ruby.
This document summarizes a nonstandard library called psp-std that provides consistency, correctness, and convenience without dependencies. It includes:
- A consciously constructed namespace that reexports common Scala types with consistent names.
- Support for empty values through type classes like Empty, providing default empty values when collections are converted.
- Additional collection methods like splitAfter, grep, splitAround for filtering, splitting, and matching patterns in collections.
- Predicate algebra for filtering collections based on boolean combinations of predicates.
- Support for pairness awareness when working with paired data like zipped collections.
- Fair trade collections that allow type-safe mapping and other operations across different collection types.
A2 Computing Reverse Polish Notation Part 2pstevens1963
The document provides an overview of a computing lesson on Reverse Polish Notation (RPN) and use of the stack data structure. It includes the following key points:
1) Objectives are to review RPN concepts, apply RPN to the stack data structure, and review past paper questions.
2) Examples are provided to demonstrate converting infix expressions to postfix RPN and using a stack to evaluate RPN expressions step-by-step.
3) A worksheet exercise is assigned for students to complete, showing their working out.
4) The lesson aims to help students understand where RPN is applied, the differences between infix, prefix and postfix notation, and how RPN relates to the
A queue is a first-in, first-out (FIFO) data structure where elements are inserted at the rear and deleted from the front. There are two common implementations - a linear array implementation where the front and rear indices are incremented as elements are added/removed, and a circular array implementation where the indices wrap around to avoid unused space. Queues have applications in printing, scheduling, and call centers where requests are handled in the order received.
This document discusses several graph theory concepts including:
- A connected undirected graph contains a simple path between any two vertices.
- Cut vertices and edges divide a graph into more components upon removal.
- Euler circuits and paths visit every edge exactly once in a connected multigraph.
- Planar graphs can be drawn in a plane without edge crossings.
- Graph coloring assigns colors to vertices such that no adjacent vertices share a color.
Discrete mathematics counting and logic relationSelf-Employed
In the mathematics world logic is the main fundamental key wi=ord to deal with the problems .If any problems are tried to solve by using certain logics and information provided by the statement then it became quite easier to get the required result effectively and efficiently without any lag of time.
Algorithm and C code related to data structureSelf-Employed
Everything lies inside an algorithm in the world of coding and algorithm formation which is the basis of data structure and manipulation of the algorithm in computer science and information technology which is ultimately used to find a particular problems solution
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
This document discusses inverses of matrices. It defines an invertible matrix as a square matrix A that has an inverse matrix B such that AB and BA are the identity matrix. It also defines singular and non-singular matrices. Theorems are provided to determine if a 2x2 matrix is invertible based on its determinant, and to solve systems of equations using the inverse matrix. Elementary matrices from row operations on the identity matrix are introduced. An algorithm for finding the inverse of an invertible matrix using row operations on the augmented matrix [A|I] is also given.
The 8086 microprocessor is a 16-bit processor that can address up to 1MB of memory using a segmented architecture. It is divided into a Bus Interface Unit that handles fetching instructions and transferring data, and an Execution Unit that decodes and executes instructions. The BIU uses segment registers to map the physical memory addresses and fetches multiple instructions using pipelining to overlap execution. The EU contains registers like AX, BX, CX for data and flags for status.
GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar
This presentation covers the fundamentals of Git and version control in a practical, beginner-friendly way. Learn key commands, the Git data model, commit workflows, and how to collaborate effectively using Git — all explained with visuals, examples, and relatable humor.
INTRO TO STATISTICS
INTRO TO SPSS INTERFACE
CLEANING MULTIPLE CHOICE RESPONSE DATA WITH EXCEL
ANALYZING MULTIPLE CHOICE RESPONSE DATA
INTERPRETATION
Q & A SESSION
PRACTICAL HANDS-ON ACTIVITY
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
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.
Social Problem-Unemployment .pptx notes for Physiotherapy StudentsDrNidhiAgarwal
Unemployment is a major social problem, by which not only rural population have suffered but also urban population are suffered while they are literate having good qualification.The evil consequences like poverty, frustration, revolution
result in crimes and social disorganization. Therefore, it is
necessary that all efforts be made to have maximum.
employment facilities. The Government of India has already
announced that the question of payment of unemployment
allowance cannot be considered in India
Exploring Substances:
Acidic, Basic, and
Neutral
Welcome to the fascinating world of acids and bases! Join siblings Ashwin and
Keerthi as they explore the colorful world of substances at their school's
National Science Day fair. Their adventure begins with a mysterious white paper
that reveals hidden messages when sprayed with a special liquid.
In this presentation, we'll discover how different substances can be classified as
acidic, basic, or neutral. We'll explore natural indicators like litmus, red rose
extract, and turmeric that help us identify these substances through color
changes. We'll also learn about neutralization reactions and their applications in
our daily lives.
by sandeep swamy
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.
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.
The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
This chapter provides an in-depth overview of the viscosity of macromolecules, an essential concept in biophysics and medical sciences, especially in understanding fluid behavior like blood flow in the human body.
Key concepts covered include:
✅ Definition and Types of Viscosity: Dynamic vs. Kinematic viscosity, cohesion, and adhesion.
⚙️ Methods of Measuring Viscosity:
Rotary Viscometer
Vibrational Viscometer
Falling Object Method
Capillary Viscometer
🌡️ Factors Affecting Viscosity: Temperature, composition, flow rate.
🩺 Clinical Relevance: Impact of blood viscosity in cardiovascular health.
🌊 Fluid Dynamics: Laminar vs. turbulent flow, Reynolds number.
🔬 Extension Techniques:
Chromatography (adsorption, partition, TLC, etc.)
Electrophoresis (protein/DNA separation)
Sedimentation and Centrifugation methods.
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.
Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
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
for BSC Nursing 1st semester
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K12 Tableau Tuesday - Algebra Equity and Access in Atlanta Public Schoolsdogden2
Algebra 1 is often described as a “gateway” class, a pivotal moment that can shape the rest of a student’s K–12 education. Early access is key: successfully completing Algebra 1 in middle school allows students to complete advanced math and science coursework in high school, which research shows lead to higher wages and lower rates of unemployment in adulthood.
Learn how The Atlanta Public Schools is using their data to create a more equitable enrollment in middle school Algebra classes.
2. Infix, Postfix and Prefix
• 2+3 <operand><operator><operand>
• A-b
• (P*2)
Operands are basic objects on which operation is performed
INFIX
<operand><operator><operand>
• (2+3) * 4 (2+3) * 4
• (P+Q)*(R+S) (P+Q) * (R+S)
Common expressions
3. Infix
• What about 4+6*2 and what about 4+6+2
• To clear ambiguity we remember school Mathematics
• Order of precedence
1. Parentheses (Brackets)
2. Order (Exponents)
3. Division
4. Multiplication
5. Addition
6. Subtraction
• So 4+6*2 | 2*6/2 -3 +7 | {(2*6)/2}-(3+7)
=4+12 | = 2*3-3+7 | = ???
=16 | = 6-3+7 | = ??
| = 3+7=10 | = ?
4. Prefix (polish notation)
• In prefix notation the operator proceeds the two operands. i.e.
the operator is written before the operands.
<operator><operand><operand>
infix prefix
2+3 +23
p-q -pq
a+b*c +a*bc
5. Postfix (Reverse Polish Notation)
• In postfix notation the operators are written after the operands
so it is called the postfix notation (post means after).
<operand><operand><operator>
infix prefix postfix
2+3 +23 23+
p-q -pq pq-
a+b*c +a*bc abc*+
Human-readable Good for machines
6. Conversion of Infix to Postfix Expression
• While evaluating an infix expression, there is an evaluation order according
to which the operations are executed
• Brackets or Parentheses
• Exponentiation
• Multiplication or Division
• Addition or Subtraction
• The operators with same priority are evaluated from left to right.
• Eg:
• A+B+C means (A+B)+C
7. Evaluation of Postfix Expressions
• Suppose an expression
a*b+c*d-e
• We can write this as:
{(a*b)+(c*d)}-e
• As we want to change it into postfix expression
{(ab*)+(cd*)}-e
{(ab*)(cd*)+}-e
{(ab*)(cd*)+} e-
• Final form
ab*cd*+e-
8. Evaluation of Postfix Expressions
• Suppose we have a postfix expression
ab*cd*+e-
• And we want to evaluate it so lets say
a=2, b=3,c=5,d=4,e=9
• We can solve this as,
2 3 * 5 4 * + 9 – <operand><operand><operator>
6 5 4 * + 9 –
6 20 + 9 –
26 9 –
17
9. Evaluation of Postfix Expressions
• From above we get,
2 3 * 5 4 * + 9 –
Stack
EvaluatePostfix(exp)
{
create a stack S
for i=0 to length (exp) -1
{
if (exp[i] is operand)
PUSH(exp[i])
elseif (exp[i] is operator)
op2 <- pop()
op1 <- pop()
res– Perform(exp[i],op1,op2)
PUSH(res)
}
return top of STACK
}
2
3
10. Evaluation of Prefix expressions
• An expression: 2*3 +5*4-9
• Can be written as:
{(2*3)+(5*4)}-9
{(*2 3)+(*5 4)}-9
{+(*2 3) (*5 4)}-9
-{+(*2 3) (*5 4)}9
• We can get Rid of Paranthesis
-+*2 3 *5 4 9
11. Evaluation of Prefix expressions
• We have to scan it from right
-+*2 3 *5 4 9
Stack
9
4
5
Stack
9
20
6
Stack
9
26
Stack
17
13. Examples:
1. A * B + C becomes A B * C +
NOTE:
• When the '+' is read, it has lower precedence than the '*', so the '*' must be
printed first.
current symbol Opstack Poststack
A A
* * A
B * AB
+ + AB* {pop and print the '*' before pushing the '+'}
C + AB*C
AB*C+
14. Examples:
2. A * (B + C) becomes A B C + *
NOTE:
1.Since expressions in parentheses must be done first, everything on the stack is saved and the
left parenthesis is pushed to provide a marker.
2.When the next operator is read, the stack is treated as though it were empty and the new
operator (here the '+' sign) is pushed on.
3.Then when the right parenthesis is read, the stack is popped until the corresponding left
parenthesis is found.
4.Since postfix expressions have no parentheses, the parentheses are not printed.
current symbol Opstack Poststack
A A
* * A
( *( A
B *( AB
+ *(+ AB
C *(+ ABC
) * ABC+
ABC+*
16. Recursion
• Recursion is a process by which a function calls itself
repeatedly, until some specified condition has been satisfied
• The process is used for repetitive computations in which each
action is stated in terms of a previous result.
• To solve a problem recursively, two conditions must be
satisfied.
• First, the problem must be written in a recursive form
• Second the problem statement must include a stopping condition
17. Factorial of an integer number using recursive
function
void main(){
int n;
long int facto;
scanf(“%d”,&n);
facto=factorial(n);
printf(“%d!=%ld”,n,facto);
}
long int factorial(int n){
if(n==0){
return 1;
}else{
return n*factorial(n-1);
}
Factorial(5)=
5*Factorial(4)=
5*(4*Factorial(3))=
5*(4*(3*Factorial(2)))=
5*(4*(3*(2*Factorial(1))))=
5*(4*(3*(2*(1*Factorial(0)))))=
5*(4*(3*(2*(1*1))))= 5*(4*(3*(2*1)))=
5*(4*(3*2))= 5*(4*6)= 5*24=
120
18. Recursion Pros and Cons
• Pros
• The code may be much easier to write.
• To solve some problems which are naturally recursive such as tower of
Hanoi.
• Cons
• Recursive functions are generally slower than non-recursive functions.
• May require a lot of memory to hold intermediate results on the system
stack.
• It is difficult to think recursively so one must be very careful when
writing recursive functions.
20. Tower of Hanoi Problem
• The mission is to move all the disks to some another tower
without violating the sequence of arrangement.
• The below mentioned are few rules which are to be followed for
tower of hanoi −
• Only one disk can be moved among the towers at any given time.
• Only the "top" disk can be removed.
• No large disk can sit over a small disk.