This presentation was made for CSE student to understand easily quick sort algorithm to implement quick algorithm. So if u want to learn quick sort than watch it.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
This document discusses row-major and column-major ordering for representing 2D arrays in memory. In row-major order, elements are arranged sequentially row by row, with the first row occupying the first memory locations. Column-major order arranges elements column by column. Formulas are provided to calculate the memory location of a given element based on its indices, the array base address, element size, number of rows, and whether the array uses row-major or column-major ordering. An example calculates the location of element A[3,2] for both a row-major and column-major 3x4 integer array.
Merge sort is a sorting algorithm that works by dividing an array into two halves, recursively sorting the halves, and then merging the sorted halves into a single sorted array. The document provides details on how merge sort works, including pseudocode for the main, merge sort, and merging functions. It analyzes the time complexity of merge sort as O(n log n), making it more efficient than other basic sorts with O(n^2) time complexity like bubble, selection, and insertion sort.
The document discusses several sorting algorithms including selection sort, insertion sort, bubble sort, merge sort, and quick sort. It provides details on how each algorithm works including pseudocode implementations and analyses of their time complexities. Selection sort, insertion sort and bubble sort have a worst-case time complexity of O(n^2) while merge sort divides the list into halves and merges in O(n log n) time, making it more efficient for large lists.
Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the sub-partitions. It first chooses a pivot element and partitions the array by placing all elements less than the pivot before it and all elements greater than it after it. It then recursively quicksorts the two partitions. This continues until the individual partitions only contain single elements at which point they are sorted. Quicksort has average case performance of O(n log n) time making it very efficient for large data sets.
The document discusses quicksort, an efficient sorting algorithm. It begins with a review of insertion sort and merge sort. It then presents the quicksort algorithm, including choosing a pivot, partitioning the array around the pivot, and recursively sorting subarrays. Details are provided on the partition process with examples. Analysis shows quicksort has worst-case time complexity of O(n^2) but expected complexity of O(nlogn) with only O(1) extra memory. Strict proofs are outlined for the worst and expected cases. The document concludes with notes on implementing quicksort in Java for practice.
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
The document discusses insertion sort, a simple sorting algorithm that builds a sorted output list from an input one element at a time. It is less efficient on large lists than more advanced algorithms. Insertion sort iterates through the input, at each step removing an element and inserting it into the correct position in the sorted output list. The best case for insertion sort is an already sorted array, while the worst is a reverse sorted array.
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
PPT On Sorting And Searching Concepts In Data Structure | In Programming Lang...Umesh Kumar
The document discusses various sorting and searching algorithms:
- Bubble sort, selection sort, merge sort, quicksort are sorting algorithms that arrange data in a particular order like numerical or alphabetical.
- Linear search and binary search are searching algorithms where linear search sequentially checks each item while binary search divides the data set in half with each comparison.
- Examples are provided to illustrate how each algorithm works step-by-step on sample data sets.
Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the subarrays. It first selects a pivot element and partitions the array by moving all elements less than the pivot before it and greater elements after it. The subarrays are then recursively sorted through this process. When implemented efficiently with an in-place partition, quicksort is one of the fastest sorting algorithms in practice, with average case performance of O(n log n) time but worst case of O(n^2) time.
Selection sort is a sorting algorithm that finds the smallest element in an unsorted list and swaps it with the first element, then finds the next smallest element and swaps it with the second element, continuing in this way until the list is fully sorted. It works by iterating through the list, finding the minimum element, and swapping it into its correct place at each step.
Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.
Binary search provides an efficient O(log n) solution for searching a sorted list. It works by repeatedly dividing the search space in half and focusing on only one subdivision, based on comparing the search key to the middle element. This recursively narrows down possible locations until the key is found or the entire list has been searched. Binary search mimics traversing a binary search tree built from the sorted list, with divide-and-conquer reducing the search space at each step.
Quick Sort is a recursive divide and conquer sorting algorithm that works by partitioning a list around a pivot value and recursively sorting the sublists. It has average case performance of O(n log n) time. The algorithm involves picking a pivot element, partitioning the list based on element values relative to the pivot, and recursively sorting the sublists until the entire list is sorted. An example using Hoare's partition scheme is provided to demonstrate the partitioning and swapping steps.
Booth's multiplication algorithm multiplies two signed binary numbers in two's complement notation. It was invented by Andrew Donald Booth in 1950. The algorithm inspects two bits of the multiplier at a time, and either adds, subtracts, or leaves unchanged the partial product depending on whether the bits are 10, 01, or the same. It shifts the partial product and multiplier arithmeticly to the right after each step to inspect the next bits.
Quick sort Algorithm Discussion And AnalysisSNJ Chaudhary
Quicksort is a divide-and-conquer algorithm that works by partitioning an array around a pivot element and recursively sorting the subarrays. In the average case, it has an efficiency of Θ(n log n) time as the partitioning typically divides the array into balanced subproblems. However, in the worst case of an already sorted array, it can be Θ(n^2) time due to highly unbalanced partitioning. Randomizing the choice of pivot helps avoid worst-case scenarios and achieve average-case efficiency in practice, making quicksort very efficient and commonly used.
This document discusses different sorting algorithms including bubble sort, insertion sort, and selection sort. It provides details on each algorithm, including time complexity, code examples, and graphical examples. Bubble sort is an O(n2) algorithm that works by repeatedly comparing and swapping adjacent elements. Insertion sort also has O(n2) time complexity but is more efficient than bubble sort for small or partially sorted lists. Selection sort finds the minimum value and swaps it into place at each step.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
The document provides an overview of the quick sort algorithm through diagrams and explanations. It begins by introducing quick sort and stating that it is one of the fastest sorting algorithms because it runs in O(n log n) time and uses less memory than other algorithms like merge sort. It then provides step-by-step examples to demonstrate how quick sort works by picking a pivot element, partitioning the array around the pivot, and recursively sorting the subarrays. The summary concludes by restating that quick sort is an efficient sorting algorithm due to its speed and memory usage.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
The document discusses various sorting algorithms that use the divide-and-conquer approach, including quicksort, mergesort, and heapsort. It provides examples of how each algorithm works by recursively dividing problems into subproblems until a base case is reached. Code implementations and pseudocode are presented for key steps like partitioning arrays in quicksort, merging sorted subarrays in mergesort, and adding and removing elements from a heap data structure in heapsort. The algorithms are compared in terms of their time and space complexity and best uses.
The document discusses three address code, which is an intermediate code used by optimizing compilers. Three address code breaks expressions down into separate instructions that use at most three operands. Each instruction performs an assignment or binary operation on the operands. The code is implemented using quadruple, triple, or indirect triple representations. Quadruple representation stores each instruction in four fields for the operator, two operands, and result. Triple avoids temporaries by making two instructions. Indirect triple uses pointers to freely reorder subexpressions.
The document discusses Turing machines. It begins by introducing Alan Turing as the father of the Turing machine model. A Turing machine is a general model of a CPU that can manipulate data through a finite set of states and symbols. It consists of a tape divided into cells that can be read from and written to by a tape head. The tape head moves left and right across the cells. The document then provides examples of constructing Turing machines to accept specific languages, such as the language "aba" and checking for palindromes of even length strings. Transition tables are used to represent the state transitions of the Turing machines.
This document discusses asymptotic notations that are used to characterize an algorithm's efficiency. It introduces Big-Oh, Big-Omega, and Theta notations. Big-Oh gives an upper bound on running time. Big-Omega gives a lower bound. Theta gives both upper and lower bounds, representing an algorithm's exact running time. Examples are provided for each notation. Time complexity is also introduced, which describes how the time to solve a problem changes with problem size. Worst-case time analysis provides an upper bound on runtime.
The document discusses divide and conquer algorithms. It describes divide and conquer as a design strategy that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. It provides examples of divide and conquer algorithms like merge sort, quicksort, and binary search. Merge sort works by recursively sorting halves of an array until it is fully sorted. Quicksort selects a pivot element and partitions the array into subarrays of smaller and larger elements, recursively sorting the subarrays. Binary search recursively searches half-intervals of a sorted array to find a target value.
Quick sort is a sorting algorithm that uses divide-and-conquer to partition an array into two sub-arrays based on a pivot element, and then recursively sorts the sub-arrays. It works by picking a pivot element, partitioning the array into elements less than or equal to the pivot and elements greater than the pivot, and then applying quicksort recursively on the two sub-arrays. Quick sort has an average time complexity of O(nlogn) and is often faster in practice than other popular sorting algorithms like merge sort and heap sort.
Quick sort uses a divide-and-conquer approach to sort a list. It works by selecting a pivot element, partitioning the list into elements less than, equal to, and greater than the pivot, and then recursively sorting the sub-lists. The example shows quick sort being applied to a list of numbers, with the pivot being swapped with other elements to partition the list into sorted sub-lists on each iteration.
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
PPT On Sorting And Searching Concepts In Data Structure | In Programming Lang...Umesh Kumar
The document discusses various sorting and searching algorithms:
- Bubble sort, selection sort, merge sort, quicksort are sorting algorithms that arrange data in a particular order like numerical or alphabetical.
- Linear search and binary search are searching algorithms where linear search sequentially checks each item while binary search divides the data set in half with each comparison.
- Examples are provided to illustrate how each algorithm works step-by-step on sample data sets.
Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the subarrays. It first selects a pivot element and partitions the array by moving all elements less than the pivot before it and greater elements after it. The subarrays are then recursively sorted through this process. When implemented efficiently with an in-place partition, quicksort is one of the fastest sorting algorithms in practice, with average case performance of O(n log n) time but worst case of O(n^2) time.
Selection sort is a sorting algorithm that finds the smallest element in an unsorted list and swaps it with the first element, then finds the next smallest element and swaps it with the second element, continuing in this way until the list is fully sorted. It works by iterating through the list, finding the minimum element, and swapping it into its correct place at each step.
Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.
Binary search provides an efficient O(log n) solution for searching a sorted list. It works by repeatedly dividing the search space in half and focusing on only one subdivision, based on comparing the search key to the middle element. This recursively narrows down possible locations until the key is found or the entire list has been searched. Binary search mimics traversing a binary search tree built from the sorted list, with divide-and-conquer reducing the search space at each step.
Quick Sort is a recursive divide and conquer sorting algorithm that works by partitioning a list around a pivot value and recursively sorting the sublists. It has average case performance of O(n log n) time. The algorithm involves picking a pivot element, partitioning the list based on element values relative to the pivot, and recursively sorting the sublists until the entire list is sorted. An example using Hoare's partition scheme is provided to demonstrate the partitioning and swapping steps.
Booth's multiplication algorithm multiplies two signed binary numbers in two's complement notation. It was invented by Andrew Donald Booth in 1950. The algorithm inspects two bits of the multiplier at a time, and either adds, subtracts, or leaves unchanged the partial product depending on whether the bits are 10, 01, or the same. It shifts the partial product and multiplier arithmeticly to the right after each step to inspect the next bits.
Quick sort Algorithm Discussion And AnalysisSNJ Chaudhary
Quicksort is a divide-and-conquer algorithm that works by partitioning an array around a pivot element and recursively sorting the subarrays. In the average case, it has an efficiency of Θ(n log n) time as the partitioning typically divides the array into balanced subproblems. However, in the worst case of an already sorted array, it can be Θ(n^2) time due to highly unbalanced partitioning. Randomizing the choice of pivot helps avoid worst-case scenarios and achieve average-case efficiency in practice, making quicksort very efficient and commonly used.
This document discusses different sorting algorithms including bubble sort, insertion sort, and selection sort. It provides details on each algorithm, including time complexity, code examples, and graphical examples. Bubble sort is an O(n2) algorithm that works by repeatedly comparing and swapping adjacent elements. Insertion sort also has O(n2) time complexity but is more efficient than bubble sort for small or partially sorted lists. Selection sort finds the minimum value and swaps it into place at each step.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
The document provides an overview of the quick sort algorithm through diagrams and explanations. It begins by introducing quick sort and stating that it is one of the fastest sorting algorithms because it runs in O(n log n) time and uses less memory than other algorithms like merge sort. It then provides step-by-step examples to demonstrate how quick sort works by picking a pivot element, partitioning the array around the pivot, and recursively sorting the subarrays. The summary concludes by restating that quick sort is an efficient sorting algorithm due to its speed and memory usage.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
The document discusses various sorting algorithms that use the divide-and-conquer approach, including quicksort, mergesort, and heapsort. It provides examples of how each algorithm works by recursively dividing problems into subproblems until a base case is reached. Code implementations and pseudocode are presented for key steps like partitioning arrays in quicksort, merging sorted subarrays in mergesort, and adding and removing elements from a heap data structure in heapsort. The algorithms are compared in terms of their time and space complexity and best uses.
The document discusses three address code, which is an intermediate code used by optimizing compilers. Three address code breaks expressions down into separate instructions that use at most three operands. Each instruction performs an assignment or binary operation on the operands. The code is implemented using quadruple, triple, or indirect triple representations. Quadruple representation stores each instruction in four fields for the operator, two operands, and result. Triple avoids temporaries by making two instructions. Indirect triple uses pointers to freely reorder subexpressions.
The document discusses Turing machines. It begins by introducing Alan Turing as the father of the Turing machine model. A Turing machine is a general model of a CPU that can manipulate data through a finite set of states and symbols. It consists of a tape divided into cells that can be read from and written to by a tape head. The tape head moves left and right across the cells. The document then provides examples of constructing Turing machines to accept specific languages, such as the language "aba" and checking for palindromes of even length strings. Transition tables are used to represent the state transitions of the Turing machines.
This document discusses asymptotic notations that are used to characterize an algorithm's efficiency. It introduces Big-Oh, Big-Omega, and Theta notations. Big-Oh gives an upper bound on running time. Big-Omega gives a lower bound. Theta gives both upper and lower bounds, representing an algorithm's exact running time. Examples are provided for each notation. Time complexity is also introduced, which describes how the time to solve a problem changes with problem size. Worst-case time analysis provides an upper bound on runtime.
The document discusses divide and conquer algorithms. It describes divide and conquer as a design strategy that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. It provides examples of divide and conquer algorithms like merge sort, quicksort, and binary search. Merge sort works by recursively sorting halves of an array until it is fully sorted. Quicksort selects a pivot element and partitions the array into subarrays of smaller and larger elements, recursively sorting the subarrays. Binary search recursively searches half-intervals of a sorted array to find a target value.
Quick sort is a sorting algorithm that uses divide-and-conquer to partition an array into two sub-arrays based on a pivot element, and then recursively sorts the sub-arrays. It works by picking a pivot element, partitioning the array into elements less than or equal to the pivot and elements greater than the pivot, and then applying quicksort recursively on the two sub-arrays. Quick sort has an average time complexity of O(nlogn) and is often faster in practice than other popular sorting algorithms like merge sort and heap sort.
Quick sort uses a divide-and-conquer approach to sort a list. It works by selecting a pivot element, partitioning the list into elements less than, equal to, and greater than the pivot, and then recursively sorting the sub-lists. The example shows quick sort being applied to a list of numbers, with the pivot being swapped with other elements to partition the list into sorted sub-lists on each iteration.
Cyber-physical system with machine learning (Poster)wassim bouazza
1. The document proposes a heterarchical approach using intelligent cyber-physical products to schedule jobs in a partially flexible manufacturing system.
2. Under the proposed approach, each cyber-physical product uses distributed decision-making and learning to determine the optimal sequence of required services from flexible resources to complete its production.
3. Experimental results on three case studies show the proposed approach using cyber-physical products provides better performance than other traditional scheduling methods in terms of key metrics like average waiting time and completion time.
This document discusses various sorting algorithms and their time complexities. It covers common sorting algorithms like bubble sort, selection sort, insertion sort, which have O(N^2) time complexity and are slow for large data sets. More efficient algorithms like merge sort, quicksort, heapsort with O(N log N) time complexity are also discussed. Implementation details and examples are provided for selection sort, insertion sort, merge sort and quicksort algorithms.
1. The document presents a Lean Six Sigma project to reduce liquid particle counts (LPC) in plastic injection molded ramp components produced by ultrasonic washing and drying processes.
2. Baseline data shows the current processes have high variability and are not capable of meeting a new, stricter LPC specification required by customers.
3. The project aims to improve the washing processes for two representative ramp products to achieve a process mean LPC lower than 70% of the new specification by analyzing sources of variation and implementing process improvements.
Control Strategy of Triple Effect Evaporators based on Solar Desalination of...IRJET Journal
This document discusses control strategies for triple effect evaporators used in solar desalination of Red Sea water. It presents the transfer functions identified for the evaporator system and analyzes stability using Routh-Hurwitz, root locus, and Bode methods. PID controllers were designed and tuned for each evaporator loop using Ziegler-Nichols methods. Simulation results showed PID control provided the best performance with minimum overshoot and shortest setting times for all loops.
Pressure drop model presentation april 19thYen Nguyen
This document describes an experiment to model pressure drop through a filter plate arrangement. The experiment involves measuring pressure drop and flow rate for different numbers of filter plates and orientations. Equations are provided to model pressure drop in laminar, turbulent and combined flow. Pressure drop is plotted against flow rate for different numbers of plates, and residuals are analyzed. Uncertainty analysis is also presented. The conclusion asks if there are any questions and notes the units of pressure drop and flow rate.
The document describes sequential pattern mining and the Apriori algorithm for finding frequent sequential patterns. It discusses (1) defining the problem of finding all subsequences that occur at or above a minimum support threshold, (2) the Apriori algorithm which works in phases to generate candidate sequences, determine support counts, and find frequent sequences, and (3) techniques like AprioriSome and DynamicSome that aim to optimize the algorithm by avoiding counting non-maximal sequences. The goal is to efficiently mine databases to discover sequential patterns showing commonly occurring ordered events.
Quicksort is described including its Java source code, time complexity analysis, and optimizations. It works by partitioning an array around pivots and recursively sorting the subarrays. The basic algorithm runs in O(n log n) time on average but can degrade to O(n^2). Optimizations like insertion sort for small subproblems and dual pivots are tested, with dual pivots performing best in testing.
The project is based on detecting frequent patterns on a web-server by using the server log. It generates multi-level statistics based on Apriori algorithm with each level determined by Pattern length.
Currently this is a dynamic programming form of Apriori where we have implemented the rule that to be a frequent pattern at a level k (i.e patter of size k), one of the elements from its powerset of size (k-1) must be frequent. This helps in improving the efficiency to less than quadratic time on average.
The document discusses two sorting algorithms: Quicksort and Mergesort. Quicksort works by picking a pivot element and partitioning the array around that pivot, recursively sorting the subarrays. It has average time complexity of O(n log n) but worst case of O(n^2). Mergesort works by dividing the array into halves, recursively sorting the halves, and then merging the sorted halves together. It has time complexity of O(n log n) in all cases. The document also includes Java code for implementing MergeSort and discusses how it works.
The document describes the implementation of various digital logic circuits like D latch, D flip flop, JK flip flop, multiplexers, decoders, counters etc. using VHDL. It includes the VHDL code, test benches and synthesis reports for each circuit. The aim is to design the circuits in behavioral and structural modeling and verify their functionality.
This document summarizes the radix sort algorithm in 3 steps:
1) It sorts the array by the rightmost digit (units place) then by the next digit to the left (tens place) and finally by the leftmost digit (hundreds place).
2) It provides an example of sorting the array [123, 167, 788, 567, 345, 234, 456, 862] in this way over 3 iterations.
3) After 3 iterations/sorts, the array is fully sorted from lowest to highest number.
This document provides an overview of a project management course to be taught from March to July 2024 by Professor Aminullah Assagaf. The course will cover topics such as project planning, scheduling, control methods, critical path method (CPM), probabilistic activity times, and project crashing and time-cost tradeoffs. It lists learning objectives, lecture outlines, and resources including a YouTube channel for the course.
This document discusses critical path method (CPM) and program evaluation review technique (PERT) project management techniques. It explains that CPM uses a single activity time estimate with activities as nodes, while PERT uses multiple estimates with activities as lines. Both are improvements over Gantt charts and show precedence relationships visually. The document then provides details on how to construct the network diagrams and determine the critical path for each technique.
- This transcript is for Stephen J. Legleiter, who earned an Associate in Arts degree in Liberal Studies from Barton County Community College in 2009.
- Mr. Legleiter transferred 51 credit hours from Harold Washington College and Ace Guide Service School.
- At Barton County Community College, Mr. Legleiter completed 56 credit hours with a GPA of 2.93, earning several honors including Dean's List and Good Standing.
This document contains an agenda and notes from a technical discussion. It includes topics like Kubernetes, etcd, the operator framework, Kafka installation on OpenShift, Zookeeper, and configuration management. Various technical concepts and components are defined briefly.
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
This document summarizes Nicki Ripperda's internship portfolio from their Manufacturing Engineering internship in 2015. It includes summaries of process mapping, time studies, standard work development, work instructions, control plans, capability studies and other projects completed during the internship. Diagrams and tables are included showing process flows, machine setup details, time study results, and capability analysis findings and recommendations.
In this slide I explained about merge sort algorithm. By reading attentively and watching my slide topic you will easily understand merge sort algorithm.
The slide was made about Basic Database Management system.
If you are a new student for Database Management then you should read this slide. by this slide you can easily gather a quick knowledge about database Management system. so now open this slide and read it.
This document discusses database languages used in database management systems (DBMS). It describes three types of database languages: data definition language (DDL) used to define and modify the database schema; data manipulation language (DML) used to insert, update, delete and retrieve data; and data control language (DCL) used to control access privileges. Examples are provided for common statements in each language type like CREATE, ALTER, DROP for DDL and INSERT, UPDATE, DELETE, SELECT for DML. Case sensitivity and data types are also briefly covered.
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran
The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
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]
"Basics of Heterocyclic Compounds and Their Naming Rules"rupalinirmalbpharm
This video is about heterocyclic compounds, which are chemical compounds with rings that include atoms like nitrogen, oxygen, or sulfur along with carbon. It covers:
Introduction – What heterocyclic compounds are.
Prefix for heteroatom – How to name the different non-carbon atoms in the ring.
Suffix for heterocyclic compounds – How to finish the name depending on the ring size and type.
Nomenclature rules – Simple rules for naming these compounds the right way.
Common rings – Examples of popular heterocyclic compounds used in real life.
How to Add Customer Note in Odoo 18 POS - Odoo SlidesCeline George
In this slide, we’ll discuss on how to add customer note in Odoo 18 POS module. Customer Notes in Odoo 18 POS allow you to add specific instructions or information related to individual order lines or the entire order.
*Metamorphosis* is a biological process where an animal undergoes a dramatic transformation from a juvenile or larval stage to a adult stage, often involving significant changes in form and structure. This process is commonly seen in insects, amphibians, and some other animals.
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 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 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.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
What makes space feel generous, and how architecture address this generosity in terms of atmosphere, metrics, and the implications of its scale? This edition of #Untagged explores these and other questions in its presentation of the 2024 edition of the Master in Collective Housing. The Master of Architecture in Collective Housing, MCH, is a postgraduate full-time international professional program of advanced architecture design in collective housing presented by Universidad Politécnica of Madrid (UPM) and Swiss Federal Institute of Technology (ETH).
Yearbook MCH 2024. Master in Advanced Studies in Collective Housing UPM - ETH
Understanding P–N Junction Semiconductors: A Beginner’s GuideGS Virdi
Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers:
What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.
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.
APM event hosted by the Midlands Network on 30 April 2025.
Speaker: Sacha Hind, Senior Programme Manager, Network Rail
With fierce competition in today’s job market, candidates need a lot more than a good CV and interview skills to stand out from the crowd.
Based on her own experience of progressing to a senior project role and leading a team of 35 project professionals, Sacha shared not just how to land that dream role, but how to be successful in it and most importantly, how to enjoy it!
Sacha included her top tips for aspiring leaders – the things you really need to know but people rarely tell you!
We also celebrated our Midlands Regional Network Awards 2025, and presenting the award for Midlands Student of the Year 2025.
This session provided the opportunity for personal reflection on areas attendees are currently focussing on in order to be successful versus what really makes a difference.
Sacha answered some common questions about what it takes to thrive at a senior level in a fast-paced project environment: Do I need a degree? How do I balance work with family and life outside of work? How do I get leadership experience before I become a line manager?
The session was full of practical takeaways and the audience also had the opportunity to get their questions answered on the evening with a live Q&A session.
Attendees hopefully came away feeling more confident, motivated and empowered to progress their careers
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 Purchase Alternatives in Odoo 18Celine George
Managing purchase alternatives is crucial for ensuring a smooth and cost-effective procurement process. Odoo 18 provides robust tools to handle alternative vendors and products, enabling businesses to maintain flexibility and mitigate supply chain disruptions.
World war-1(Causes & impacts at a glance) PPT by Simanchala Sarab(BABed,sem-4...larencebapu132
This is short and accurate description of World war-1 (1914-18)
It can give you the perfect factual conceptual clarity on the great war
Regards Simanchala Sarab
Student of BABed(ITEP, Secondary stage)in History at Guru Nanak Dev University Amritsar Punjab 🙏🙏
Learn about the APGAR SCORE , a simple yet effective method to evaluate a newborn's physical condition immediately after birth ....this presentation covers .....
what is apgar score ?
Components of apgar score.
Scoring system
Indications of apgar score........
5. Procedure:
It is one of the fastest sorting techniques available.
Like binary search, this method uses a recursive , devide and conquer
strategy
The basic idea is to separate a list of items into two parts,
Surrounding a distinguished item calls pivot
At the end of the process, one part will contain items
Smaller than the pivot and the other part will contain items
Larger than the pivot