#implement circular queue

[codingprolab]

COP 5615 Lab 1: Process Coordination using Sempahores & Mutexes

I. Objectives • Learn about a classical synchronization problem known in concurrent processing. Specifically, you will learn about the consumer-producer problem. • You will implement solutions to this problem under Xinu semaphores, along with mutexes of your own design. II. The Producer-Consumer Problem Two processes are sharing a circular buffer (queue), one produces at the tail of the circular…

[finarena]

DSA Channel: The Ultimate Destination for Learning Data Structures and Algorithms from Basics to Advanced

DSA mastery stands vital for successful software development and competitive programming in the current digital world that operates at high speeds. People at every skill level from beginner to advanced developer will find their educational destination at the DSA Channel.

Why is DSA Important?

Software development relies on data structures together with algorithms as its essential core components. Code optimization emerges from data structures and algorithms which produces better performance and leads to successful solutions of complex problems. Strategic knowledge of DSA serves essential needs for handling job interviews and coding competitions while enhancing logical thinking abilities. Proper guidance makes basic concepts of DSA both rewarding and enjoyable to study.

What Makes DSA Channel Unique?

The DSA Channel exists to simplify both data structures along algorithms and make them accessible to all users. Here’s why it stands out:

The channel provides step-by-step learning progress which conservatively begins by teaching arrays and linked lists and continues to dynamic programming and graph theory.

Each theoretical concept gets backed through coding examples practically to facilitate easier understanding and application in real-life situations.

Major companies like Google, Microsoft, and Amazon utilize DSA knowledge as part of their job recruiter process. Through their DSA Channel service candidates can perform mock interview preparation along with receiving technical interview problem-solving advice and interview cracking techniques.

Updates Occur Regularly Because the DSA Channel Matches the Ongoing Transformation in the Technology Industry. The content uses current algorithm field trends and new elements for constant updates.

 DSAC channels will be covering the below key topics

DSA Channel makes certain you have clear ideas that are necessary for everything from the basics of data structures to the most sophisticated methods and use cases. Highlights :

1. Introduction Basic Data Structures

Fundamentals First, You Always Need To Start With the Basics. Some of the DSA Channel topics are:

Memories storing and manipulating elements of Arrays

Linked Lists — learn linked lists: Singly Linked lists Dually linked lists and Circular linked list

Implementing Stacks and Queues — linear data structure with these implementations.

Hash Table: Understanding Hashing and its impact in the retrieval of Data.

2. Advanced Data Structures

If you want to get Intense: the DSA channel has profound lessons:

Graph bases Types- Type of Graph Traversals: BFS, DFS

Heaps — Come to know about Min Heap and Max Heap

Index Tries – How to store and retrieve a string faster than the fastest possible.

3. Algorithms

This is especially true for efficient problem-solving. The DSA Channel discusses in-depth:

Searching Algorithms Binary Search and Linear Search etc.

Dynamic Programming: Optimization of subproblems

Recursion and Backtracking: How to solve a problem by recursion.

Graph Algorithms — Dijkstra, Bellman-Ford and Floyd-Warshall etc

4. Applications of DSA in Real life

So one of the unique things (About the DSA channel) is these real-world applications of his DSA Channel.

Instead of just teaching Theory the channel gives a hands-on to see how it's used in world DSA applications.

Learning about Database Management Systems — Indexing, Query Optimization, Storage Techniques

Operating Systems – study algorithms scheduling, memory management,t, and file systems.

Machine Learning and AI — Learning the usage of algorithms in training models, and optimizing computations.

Finance and Banking — data structures that help us in identifying risk scheme things, fraud detection, transaction processing, etc.

This hands-on approach to working out will ensure that learners not only know how to use these concepts in real-life examples. 

How Arena Fincorp Benefits from DSA?

Arena Fincorp, a leading financial services provider, understands the importance of efficiency and optimization in the fintech sector. The financial solutions offered through Arena Fincorp operate under the same principles as data structures and algorithms which enhance coding operations. Arena Fincorp guarantees perfect financial transactions and data protection through its implementation of sophisticated algorithms. The foundational principles of DSA enable developers to build strong financial technological solutions for contemporary financial complications.

How to Get Started with DSA Channel?

New users of the DSA Channel should follow these instructions to maximize their experience:

The educational process should start with fundamental videos explaining arrays together with linked lists and stacks to establish a basic knowledge base.

The practice of DSA needs regular exercise and time to build comprehension. Devote specific time each day to find solutions for problems.

The platforms LeetCode, CodeChef, and HackerRank provide various DSA problems for daily problem-solving which boosts your skills.

Join community discussions where you can help learners by sharing solutions as well as working with fellow participants.

Students should do Mock Interviews through the DSA Channel to enhance their self-confidence and gain experience in actual interview situations.

The process of learning becomes more successful when people study together in a community. Through the DSA Channel students find an energetic learning community to share knowledge about doubts and project work and they exchange insight among themselves.

Conclusion

Using either data structures or algorithms in tech requires mastery so they have become mandatory in this sector. The DSA Channel delivers the best learning gateway that suits students as well as professionals and competitive programmers. Through their well-organized educational approach, practical experience and active learner network the DSA Channel builds a deep understanding of DSA with effective problem-solving abilities.

The value of data structures and algorithms and their optimized algorithms and efficient coding practices allows companies such as Arena Fincorp to succeed in their industries. New learners should begin their educational journey right now with the DSA Channel to master data structures and algorithms expertise.

[sunbeaminfo]

Enhance Your Coding Skills with Data Structures and Algorithms Classes at Sunbeam Institute, Pune

Elevate your programming expertise by enrolling in the Data Structures and Algorithms course at Sunbeam Institute, Pune. This comprehensive program is designed for students, freshers, and working professionals aiming to deepen their understanding of essential data structures and algorithms using Java.

Course Highlights:

Algorithm Analysis: Learn to evaluate time and space complexity for efficient coding.

Linked Lists: Master various types, including singly, doubly, and circular linked lists.

Stacks and Queues: Understand their implementation using arrays and linked lists, and apply them in expression evaluation and parenthesis balancing.

Sorting and Searching: Gain proficiency in algorithms like Quick Sort, Merge Sort, Heap Sort, Linear Search, Binary Search, and Hashing.

Trees and Graphs: Explore tree traversals, Binary Search Trees (BST), and graph algorithms such as Prim’s MST, Kruskal’s MST, Dijkstra's, and A* search.

Course Details:

Duration: 60 hours

Schedule: Weekdays (Monday to Saturday), 5:00 PM to 8:00 PM

Upcoming Batch: January 27, 2025, to February 18, 2025

Fees: ₹7,500 (including 18% GST)

Prerequisites:

Basic knowledge of Java programming, including classes, objects, generics, and Java collections (e.g., ArrayList).

Why Choose Sunbeam Institute?

Sunbeam Institute is renowned for its effective IT training programs in Pune, offering a blend of theoretical knowledge and practical application to ensure a thorough understanding of complex concepts.

Enroll Now: Secure your spot in this sought-after course to advance your programming skills and enhance your career prospects. For registration and more information, visit:

[fortunatelycoldengineer]

Test Your Knowledge: Quiz Challenge!!! 📝🧠

In a circular queue implementation using array of size 5, the array index starts with 0 where front and rear values are 3 and 4 respectively. Determine the array index at which the insertion of the next element will take place.🤔

For more interesting quizzes, check the link below! 📚

https://bit.ly/3WDJhF0

For the explanation of the right answer, you can check Q.No. 46 of the above link. 📖

[nocodehackathon]

Essential Algorithms and Data Structures for Competitive Programming

Competitive programming is a thrilling and intellectually stimulating field that challenges participants to solve complex problems efficiently and effectively. At its core, competitive programming revolves around algorithms and data structures—tools that help you tackle problems with precision and speed. If you're preparing for a competitive programming contest or just want to enhance your problem-solving skills, understanding essential algorithms and data structures is crucial. In this blog, we’ll walk through some of the most important ones you should be familiar with.

1. Arrays and Strings

Arrays are fundamental data structures that store elements in a contiguous block of memory. They allow for efficient access to elements via indexing and are often the first data structure you encounter in competitive programming.

Operations: Basic operations include traversal, insertion, deletion, and searching. Understanding how to manipulate arrays efficiently can help solve a wide range of problems.

Strings are arrays of characters and are often used to solve problems involving text processing. Basic string operations like concatenation, substring search, and pattern matching are essential.

2. Linked Lists

A linked list is a data structure where elements (nodes) are stored in separate memory locations and linked together using pointers. There are several types of linked lists:

Singly Linked List: Each node points to the next node.

Doubly Linked List: Each node points to both the next and previous nodes.

Circular Linked List: The last node points back to the first node.

Linked lists are useful when you need to frequently insert or delete elements as they allow for efficient manipulation of the data.

3. Stacks and Queues

Stacks and queues are abstract data types that operate on a last-in-first-out (LIFO) and first-in-first-out (FIFO) principle, respectively.

Stacks: Useful for problems involving backtracking or nested structures (e.g., parsing expressions).

Queues: Useful for problems involving scheduling or buffering (e.g., breadth-first search).

Both can be implemented using arrays or linked lists and are foundational for many algorithms.

4. Hashing

Hashing involves using a hash function to convert keys into indices in a hash table. This allows for efficient data retrieval and insertion.

Hash Tables: Hash tables provide average-case constant time complexity for search, insert, and delete operations.

Collisions: Handling collisions (when two keys hash to the same index) using techniques like chaining or open addressing is crucial for effective hashing.

5. Trees

Trees are hierarchical data structures with a root node and child nodes. They are used to represent hierarchical relationships and are key to many algorithms.

Binary Trees: Each node has at most two children. They are used in various applications such as binary search trees (BSTs), where the left child is less than the parent, and the right child is greater.

Binary Search Trees (BSTs): Useful for dynamic sets where elements need to be ordered. Operations like insertion, deletion, and search have an average-case time complexity of O(log n).

Balanced Trees: Trees like AVL trees and Red-Black trees maintain balance to ensure O(log n) time complexity for operations.

6. Heaps

A heap is a specialized tree-based data structure that satisfies the heap property:

Max-Heap: The value of each node is greater than or equal to the values of its children.

Min-Heap: The value of each node is less than or equal to the values of its children.

Heaps are used in algorithms like heap sort and are also crucial for implementing priority queues.

7. Graphs

Graphs represent relationships between entities using nodes (vertices) and edges. They are essential for solving problems involving networks, paths, and connectivity.

Graph Traversal: Algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS) are used to explore nodes and edges in graphs.

Shortest Path: Algorithms such as Dijkstra’s and Floyd-Warshall help find the shortest path between nodes.

Minimum Spanning Tree: Algorithms like Kruskal’s and Prim’s are used to find the minimum spanning tree in a graph.

8. Dynamic Programming

Dynamic Programming (DP) is a method for solving problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant computations.

Memoization: Storing results of subproblems to avoid recomputation.

Tabulation: Building a table of results iteratively, bottom-up.

DP is especially useful for optimization problems, such as finding the shortest path, longest common subsequence, or knapsack problem.

9. Greedy Algorithms

Greedy Algorithms make a series of choices, each of which looks best at the moment, with the hope that these local choices will lead to a global optimum.

Applications: Commonly used for problems like activity selection, Huffman coding, and coin change.

10. Graph Algorithms

Understanding graph algorithms is crucial for competitive programming:

Shortest Path Algorithms: Dijkstra’s Algorithm, Bellman-Ford Algorithm.

Minimum Spanning Tree Algorithms: Kruskal’s Algorithm, Prim’s Algorithm.

Network Flow Algorithms: Ford-Fulkerson Algorithm, Edmonds-Karp Algorithm.

Preparing for Competitive Programming: Summer Internship Program

If you're eager to dive deeper into these algorithms and data structures, participating in a summer internship program focused on Data Structures and Algorithms (DSA) can be incredibly beneficial. At our Summer Internship Program, we provide hands-on experience and mentorship to help you master these crucial skills. This program is designed for aspiring programmers who want to enhance their competitive programming abilities and prepare for real-world challenges.

What to Expect:

Hands-On Projects: Work on real-world problems and implement algorithms and data structures.

Mentorship: Receive guidance from experienced professionals in the field.

Workshops and Seminars: Participate in workshops that cover advanced topics and techniques.

Networking Opportunities: Connect with peers and industry experts to expand your professional network.

By participating in our DSA Internship, you’ll gain practical experience and insights that will significantly boost your competitive programming skills and prepare you for success in contests and future career opportunities.

In conclusion, mastering essential algorithms and data structures is key to excelling in competitive programming. By understanding and practicing these concepts, you can tackle complex problems with confidence and efficiency. Whether you’re just starting out or looking to sharpen your skills, focusing on these fundamentals will set you on the path to success.

Ready to take your skills to the next level? Join our Summer Internship Program and dive into the world of algorithms and data structures with expert guidance and hands-on experience. Your journey to becoming a competitive programming expert starts here!

[myprogrammingsolver]

Lab 3: Queue Implementations

Goals: * Implement a Queue class using a link-based data structure: queue_linked.py * Implement a Queue class using a circular array: queue_array.py * In the first implementation, you will use a linked structure similar to the linked structure used in implementing the Stack ADT (i.e. create a Node class). In this case, there must be a way to add items to the back of the list and remove items from…

[tutort-academy]

How to Ace Your DSA Interview, Even If You're a Newbie

Are you aiming to crack DSA interviews and land your dream job as a software engineer or developer? Look no further! This comprehensive guide will provide you with all the necessary tips and insights to ace your DSA interviews. We'll explore the important DSA topics to study, share valuable preparation tips, and even introduce you to Tutort Academy DSA courses to help you get started on your journey. So let's dive in!

Why is DSA Important?

Before we delve into the specifics of DSA interviews, let's first understand why data structures and algorithms are crucial for software development. DSA plays a vital role in optimizing software components, enabling efficient data storage and processing.

From logging into your Facebook account to finding the shortest route on Google Maps, DSA is at work in various applications we use every day. Mastering DSA allows you to solve complex problems, optimize code performance, and design efficient software systems.

Important DSA Topics to Study

To excel in DSA interviews, it's essential to have a strong foundation in key topics. Here are some important DSA topics you should study:

1. Arrays and Strings

Arrays and strings are fundamental data structures in programming. Understanding array manipulation, string operations, and common algorithms like sorting and searching is crucial for solving coding problems.

2. Linked Lists

Linked lists are linear data structures that consist of nodes linked together. It's important to understand concepts like singly linked lists, doubly linked lists, and circular linked lists, as well as operations like insertion, deletion, and traversal.

3. Stacks and Queues

Stacks and queues are abstract data types that follow specific orderings. Mastering concepts like LIFO (Last In, First Out) for stacks and FIFO (First In, First Out) for queues is essential. Additionally, learn about their applications in real-life scenarios.

4. Trees and Binary Trees

Trees are hierarchical data structures with nodes connected by edges. Understanding binary trees, binary search trees, and traversal algorithms like preorder, inorder, and postorder is crucial. Additionally, explore advanced concepts like AVL trees and red-black trees.

5. Graphs

Graphs are non-linear data structures consisting of nodes (vertices) and edges. Familiarize yourself with graph representations, traversal algorithms like BFS (Breadth-First Search) and DFS (Depth-First Search), and graph algorithms such as Dijkstra's algorithm and Kruskal's algorithm.

6. Sorting and Searching Algorithms

Understanding various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quicksort is essential. Additionally, familiarize yourself with searching algorithms like linear search, binary search, and hash-based searching.

7. Dynamic Programming

Dynamic programming involves breaking down a complex problem into smaller overlapping subproblems and solving them individually. Mastering this technique allows you to solve optimization problems efficiently.

These are just a few of the important DSA topics to study. It's crucial to have a solid understanding of these concepts and their applications to perform well in DSA interviews.

Tips to Follow While Preparing for DSA Interviews

Preparing for DSA interviews can be challenging, but with the right approach, you can maximize your chances of success. Here are some tips to keep in mind:

1. Understand the Fundamentals

Before diving into complex algorithms, ensure you have a strong grasp of the fundamentals. Familiarize yourself with basic data structures, common algorithms, and time and space complexities.

2. Practice Regularly

Consistent practice is key to mastering DSA. Solve a wide range of coding problems, participate in coding challenges, and implement algorithms from scratch. Leverage online coding platforms like LeetCode, HackerRank to practice and improve your problem-solving skills.

3. Analyze and Optimize

After solving a problem, analyze your solution and look for areas of improvement. Optimize your code for better time and space complexities. This demonstrates your ability to write efficient and scalable code.

4. Collaborate and Learn from Others

Engage with the coding community, join study groups, and participate in online forums. Collaborating with others allows you to learn different approaches, gain insights, and improve your problem-solving skills.

5. Mock Interviews and Feedback

Simulate real interview scenarios by participating in mock interviews. Seek feedback from experienced professionals or mentors who can provide valuable insights into your strengths and areas for improvement.

Following these tips will help you build a solid foundation in DSA and boost your confidence for interviews.

Conclusion

Mastering DSA is crucial for acing coding interviews and securing your dream job as a software engineer or developer. By studying important DSA topics, following effective preparation tips, and leveraging Tutort Academy's DSA courses, you'll be well-equipped to tackle DSA interviews with confidence. Remember to practice regularly, seek feedback, and stay curious.

Good luck on your DSA journey!

[c-official]

How can i say no to a :3 request. Basically a Fibonacci heap is a super Priority queue. It supports the following operations

Insert: Insert a number in O(1) time.

GetMin: Get the lowest number in he heap in O(1) time.

DeleteMin: Remove the lowest number in the heap in amortized O(log(n)) time. Here n refers to the amount of values in the heap.

DecreaseKey: Decrease one of the values in the heap in amortized O(1) time.

Merge: Merge two Fibonacci heaps in O(1) time.

Now for the implementation. The heap is stored as a collection of rooted trees such that child notes hold larger values than their parent, and the heap also keeps track of which root has the lowest value.

Insert: To insert a value simply add a new tree of one element and update the pointer to the lowest root if necessary. This is clearly constant time.

GetMin: We already have a pointer to the root with the lowest value so just return that value. This is clearly also constant time.

Merge: Simply merge the collections of roots from the two heaps and set the pointer to the lowest root to the lowest of the two lowest roots. This can be done in constant time if the collections of roots are stored as circular doubly linked lists.

DeleteMin: First remove the lowest root and add its children as new roots. Now this is where things get interesting. Establish a hashmap. Now scan through the roots and put them in the hashmap by their degree(aka the amount of children they have). If their is already a node with the same degree merge that node with the current one by adding the one with the highest value as a child of the other. By the end of this process the highest degree of root node should be higher or equal than the amount of roots. We will write this important fact as #roots <= #maxDegree. While this scan is done find the new lowest value root. This clearly runs in O(#roots) time. This is not O(log n) as i promised but we will return to this.

DecreaseKey: Decrease the value of the desired key. If the value is still larger than the parents value all is well. Otherwise remove the node from the parent and add is as a new root. Mark the parent. If the parent is already marked do the same procedure of removing the parent, adding it as a new root at mark its parent. Do not mark the parent if it is a root and remove the mark when making a node a new root. This way a root will never be marked.

The whole marked thing sounds complicated but the only thing it does is keeping the restriction that no non root node can have removed more that one child. This is again not constant time and can run in O(#maxDepth) if the nodes parent is marked, the parent's parent is marked and so on up to a root.

This will be a good place to explain what amortized running time is. The point is that we will not consider the worst case, but the worst case average of a series of instructions. So lets keep track of some potential work. Now when you make a DecreaseKey add a token that represents some constant time work you could do now to a pile. As we mark at most one node per operation we know that #potentialWorkDone >= #markedNodes. Now that we know this, each time we move a marked node to be a new root instead of counting the time that took discard one token of potential work. We still unmark the node so the previous inequality still holds. This we will always have a token of potential work when moving a marked node to be a new node. So on each operation we did some constant time work and some constant time potential work. This is amortized O(1).

Now it is just the DeleteMin runtime that needs to be explained and why fibonacci numbers are relevant.

Instaed of looking at #roots. Lets write #roots = #coreRoots+#extraRoots where a core root is a root left after a DeleteMin operation. Thus it is always true that #coreRoots<=#maxDegree. Now we can do the same trick. Each time we add a new root from Insert or DecreaseKey add a token of potential work and each time we handle an extra root in the DeleteMin operation remove a token of potential work. Thus after each DeleteMin operation no tokens of potential work and no extra roots are left. Now DeleteMin runs in amortized O(#coreRoots)=O(#maxDegree) time.

So why is #maxDegre=O(log(n)). The main claim is that given a tree with a root of degree d then the amount of nodes in that tree is at least F_d the d'th Fibonacci number.

To prove this the Fibonacci numbers are screaming to do it inductively. So for a degree 0 tree the minimum amount of nodes is clearly the one node. A degree 1 tree has per definition a child and thus has at least 2 nodes. Now assume that the minimum tree with a root of degree m has at least F_m nodes for all m less than some n. Now look at a tree where the root has degree n. The newest node got added to the tree when it had at least n-1 nodes and because we only merge trees of the same root degree the newest root must have degree at least n-2 because it could have lost a child. Thus the newest child's subtree has at leas size F_(n-2) and before the newest child was added the tree must have had at least F_(n-1) nodes. Thus the tree now has at least size F_(n-2)+F_(n-1)=F_n proving the claim.

But because the Fibonacci numbers grow exponentially we get that #maxDegre=O(log(n)). Pure magic!

Side note, this can not get any quicker. If it could we could sort a list by adding all of our values to our data structure and then running GetMin and DeleteMin until it is empty which would result in less that O(n log n) operations if DeleteMin was quicker than O(log n) and the other operations where constant time.

A better explanation with animations can be found here. And also general shout out to that video for introducing me to the subject.

I just discovered fibonachi heaps! That is some top tier stuff and has left me craving my next big hit. So please, i am in dire need off beutifull algorithms and datastructures.

[courseforfree]

Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Java Coding)

What you’ll learn

Java Data Structures and Algorithms Masterclass

Learn, implement, and use different Data Structures

Learn, implement and use different Algorithms

Become a better developer by mastering computer science fundamentals

Learn everything you need to ace difficult coding interviews

Cracking the Coding Interview with 100+ questions with explanations

Time and Space Complexity of Data Structures and Algorithms

Recursion

Big O

Dynamic Programming

Divide and Conquer Algorithms

Graph Algorithms

Greedy Algorithms

Requirements

Basic Java Programming skills

Description

Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.

At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. You will see 100+ Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft and how-to face Interviews with comprehensive visual explanatory video materials which will bring you closer to landing the tech job of your dreams!

Learning Java is one of the fastest ways to improve your career prospects as it is one of the most in-demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high-level programming languages.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.

After finishing this course, you will be able to:

Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting, and dynamic programming to solve programming challenges.

Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications

Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets

Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Why this course is so special and different from any other resource available online?

This course will take you from the very beginning to very complex and advanced topics in understanding Data Structures and Algorithms!

You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.

You will also see Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft.

I cover everything you need to know about the technical interview process!

So whether you are interested in learning the top programming language in the world in-depth and interested in learning the fundamental Algorithms, Data Structures, and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you!

And this is what you get by signing up today:

Lifetime access to 44+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want

Friendly and fast support in the course Q&A whenever you have questions or get stuck

FULL money-back guarantee for 30 days!

This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms, increase your earning potential, or just want a job with more freedom, this is the right course for you!

The topics that are covered in this course.

Section 1 – Introduction

What are Data Structures?

What is an algorithm?

Why are Data Structures And Algorithms important?

Types of Data Structures

Types of Algorithms

Section 2 – Recursion

What is Recursion?

Why do we need recursion?

How does Recursion work?

Recursive vs Iterative Solutions

When to use/avoid Recursion?

How to write Recursion in 3 steps?

How to find Fibonacci numbers using Recursion?

Section 3 – Cracking Recursion Interview Questions

Question 1 – Sum of Digits

Question 2 – Power

Question 3 – Greatest Common Divisor

Question 4 – Decimal To Binary

Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)

power

factorial

products array

recursiveRange

fib

reverse

palindrome

some recursive

flatten

capitalize first

nestedEvenSum

capitalize words

stringifyNumbers

collects things

Section 5 – Big O Notation

Analogy and Time Complexity

Big O, Big Theta, and Big Omega

Time complexity examples

Space Complexity

Drop the Constants and the nondominant terms

Add vs Multiply

How to measure the codes using Big O?

How to find time complexity for Recursive calls?

How to measure Recursive Algorithms that make multiple calls?

Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Product and Sum

Print Pairs

Print Unordered Pairs

Print Unordered Pairs 2 Arrays

Print Unordered Pairs 2 Arrays 100000 Units

Reverse

O(N)  Equivalents

Factorial Complexity

Fibonacci Complexity

Powers of 2

Section 7 – Arrays

What is an Array?

Types of Array

Arrays in Memory

Create an Array

Insertion Operation

Traversal Operation

Accessing an element of Array

Searching for an element in Array

Deleting an element from Array

Time and Space complexity of One Dimensional Array

One Dimensional Array Practice

Create Two Dimensional Array

Insertion – Two Dimensional Array

Accessing an element of Two Dimensional Array

Traversal – Two Dimensional Array

Searching for an element in Two Dimensional Array

Deletion – Two Dimensional Array

Time and Space complexity of Two Dimensional Array

When to use/avoid array

Section 8 – Cracking Array Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Question 1 – Missing Number

Question 2 – Pairs

Question 3 – Finding a number in an Array

Question 4 – Max product of two int

Question 5 – Is Unique

Question 6 – Permutation

Question 7 – Rotate Matrix

Section 9 – CHALLENGING Array Problems (Exercises)

Middle Function

2D Lists

Best Score

Missing Number

Duplicate Number

Pairs

Section 10 – Linked List

What is a Linked List?

Linked List vs Arrays

Types of Linked List

Linked List in the Memory

Creation of Singly Linked List

Insertion in Singly Linked List in Memory

Insertion in Singly Linked List Algorithm

Insertion Method in Singly Linked List

Traversal of Singly Linked List

Search for a value in Single Linked List

Deletion of a node from Singly Linked List

Deletion Method in Singly Linked List

Deletion of entire Singly Linked List

Time and Space Complexity of Singly Linked List

Section 11 – Circular Singly Linked List

Creation of Circular Singly Linked List

Insertion in Circular Singly Linked List

Insertion Algorithm in Circular Singly Linked List

Insertion method in Circular Singly Linked List

Traversal of Circular Singly Linked List

Searching a node in Circular Singly Linked List

Deletion of a node from Circular Singly Linked List

Deletion Algorithm in Circular Singly Linked List

A method in Circular Singly Linked List

Deletion of entire Circular Singly Linked List

Time and Space Complexity of Circular Singly Linked List

Section 12 – Doubly Linked List

Creation of Doubly Linked List

Insertion in Doubly Linked List

Insertion Algorithm in Doubly Linked List

Insertion Method in Doubly Linked List

Traversal of Doubly Linked List

Reverse Traversal of Doubly Linked List

Searching for a node in Doubly Linked List

Deletion of a node in Doubly Linked List

Deletion Algorithm in Doubly Linked List

Deletion Method in Doubly Linked List

Deletion of entire Doubly Linked List

Time and Space Complexity of Doubly Linked List

Section 13 – Circular Doubly Linked List

Creation of Circular Doubly Linked List

Insertion in Circular Doubly Linked List

Insertion Algorithm in Circular Doubly Linked List

Insertion Method in Circular Doubly Linked List

Traversal of Circular Doubly Linked List

Reverse Traversal of Circular Doubly Linked List

Search for a node in Circular Doubly Linked List

Delete a node from Circular Doubly Linked List

Deletion Algorithm in Circular Doubly Linked List

Deletion Method in Circular Doubly Linked List

Entire Circular Doubly Linked List

Time and Space Complexity of Circular Doubly Linked List

Time Complexity of Linked List vs Arrays

Section 14 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple, and Microsoft)

Linked List Class

Question 1 – Remove Dups

Question 2 – Return Kth to Last

Question 3 – Partition

Question 4 – Sum Linked Lists

Question 5 – Intersection

Section 15 – Stack

What is a Stack?

What and Why of Stack?

Stack Operations

Stack using Array vs Linked List

Stack Operations using Array (Create, isEmpty, isFull)

Stack Operations using Array (Push, Pop, Peek, Delete)

Time and Space Complexity of Stack using Array

Stack Operations using Linked List

Stack methods – Push, Pop, Peek, Delete, and isEmpty using Linked List

Time and Space Complexity of Stack using Linked List

When to Use/Avoid Stack

Stack Quiz

Section 16 – Queue

What is a Queue?

Linear Queue Operations using Array

Create, isFull, isEmpty, and enQueue methods using Linear Queue Array

Dequeue, Peek and Delete Methods using Linear Queue Array

Time and Space Complexity of Linear Queue using Array

Why Circular Queue?

Circular Queue Operations using Array

Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array

Dequeue, Peek and Delete Methods in Circular Queue using Array

Time and Space Complexity of Circular Queue using Array

Queue Operations using Linked List

Create, Enqueue and isEmpty Methods in Queue using Linked List

Dequeue, Peek and Delete Methods in Queue using Linked List

Time and Space Complexity of Queue using Linked List

Array vs Linked List Implementation

When to Use/Avoid Queue?

Section 17 – Cracking Stack and Queue Interview Questions (Amazon, Facebook, Apple, Microsoft)

Question 1 – Three in One

Question 2 – Stack Minimum

Question 3 – Stack of Plates

Question 4 – Queue via Stacks

Question 5 – Animal Shelter

Section 18 – Tree / Binary Tree

What is a Tree?

Why Tree?

Tree Terminology

How to create a basic tree in Java?

Binary Tree

Types of Binary Tree

Binary Tree Representation

Create Binary Tree (Linked List)

PreOrder Traversal Binary Tree (Linked List)

InOrder Traversal Binary Tree (Linked List)

PostOrder Traversal Binary Tree (Linked List)

LevelOrder Traversal Binary Tree (Linked List)

Searching for a node in Binary Tree (Linked List)

Inserting a node in Binary Tree (Linked List)

Delete a node from Binary Tree (Linked List)

Delete entire Binary Tree (Linked List)

Create Binary Tree (Array)

Insert a value Binary Tree (Array)

Search for a node in Binary Tree (Array)

PreOrder Traversal Binary Tree (Array)

InOrder Traversal Binary Tree (Array)

PostOrder Traversal Binary Tree (Array)

Level Order Traversal Binary Tree (Array)

Delete a node from Binary Tree (Array)

Entire Binary Tree (Array)

Linked List vs Python List Binary Tree

Section 19 – Binary Search Tree

What is a Binary Search Tree? Why do we need it?

Create a Binary Search Tree

Insert a node to BST

Traverse BST

Search in BST

Delete a node from BST

Delete entire BST

Time and Space complexity of BST

Section 20 – AVL Tree

What is an AVL Tree?

Why AVL Tree?

Common Operations on AVL Trees

Insert a node in AVL (Left Left Condition)

Insert a node in AVL (Left-Right Condition)

Insert a node in AVL (Right Right Condition)

Insert a node in AVL (Right Left Condition)

Insert a node in AVL (all together)

Insert a node in AVL (method)

Delete a node from AVL (LL, LR, RR, RL)

Delete a node from AVL (all together)

Delete a node from AVL (method)

Delete entire AVL

Time and Space complexity of AVL Tree

Section 21 – Binary Heap

What is Binary Heap? Why do we need it?

Common operations (Creation, Peek, sizeofheap) on Binary Heap

Insert a node in Binary Heap

Extract a node from Binary Heap

Delete entire Binary Heap

Time and space complexity of Binary Heap

Section 22 – Trie

What is a Trie? Why do we need it?

Common Operations on Trie (Creation)

Insert a string in Trie

Search for a string in Trie

Delete a string from Trie

Practical use of Trie

Section 23 – Hashing

What is Hashing? Why do we need it?

Hashing Terminology

Hash Functions

Types of Collision Resolution Techniques

Hash Table is Full

Pros and Cons of Resolution Techniques

Practical Use of Hashing

Hashing vs Other Data structures

Section 24 – Sort Algorithms

What is Sorting?

Types of Sorting

Sorting Terminologies

Bubble Sort

Selection Sort

Insertion Sort

Bucket Sort

Merge Sort

Quick Sort

Heap Sort

Comparison of Sorting Algorithms

Section 25 – Searching Algorithms

Introduction to Searching Algorithms

Linear Search

Linear Search in Python

Binary Search

Binary Search in Python

Time Complexity of Binary Search

Section 26 – Graph Algorithms

What is a Graph? Why Graph?

Graph Terminology

Types of Graph

Graph Representation

The graph in Java using Adjacency Matrix

The graph in Java using Adjacency List

Section 27 – Graph Traversal

Breadth-First Search Algorithm (BFS)

Breadth-First Search Algorithm (BFS) in Java – Adjacency Matrix

Breadth-First Search Algorithm (BFS) in Java – Adjacency List

Time Complexity of Breadth-First Search (BFS) Algorithm

Depth First Search (DFS) Algorithm

Depth First Search (DFS) Algorithm in Java – Adjacency List

Depth First Search (DFS) Algorithm in Java – Adjacency Matrix

Time Complexity of Depth First Search (DFS) Algorithm

BFS Traversal vs DFS Traversal

Section 28 – Topological Sort

What is Topological Sort?

Topological Sort Algorithm

Topological Sort using Adjacency List

Topological Sort using Adjacency Matrix

Time and Space Complexity of Topological Sort

Section 29 – Single Source Shortest Path Problem

what is Single Source Shortest Path Problem?

Breadth-First Search (BFS) for Single Source Shortest Path Problem (SSSPP)

BFS for SSSPP in Java using Adjacency List

BFS for SSSPP in Java using Adjacency Matrix

Time and Space Complexity of BFS for SSSPP

Why does BFS not work with Weighted Graph?

Why does DFS not work for SSSP?

Section 30 – Dijkstra’s Algorithm

Dijkstra’s Algorithm for SSSPP

Dijkstra’s Algorithm in Java – 1

Dijkstra’s Algorithm in Java – 2

Dijkstra’s Algorithm with Negative Cycle

Section 31 – Bellman-Ford Algorithm

Bellman-Ford Algorithm

Bellman-Ford Algorithm with negative cycle

Why does Bellman-Ford run V-1 times?

Bellman-Ford in Python

BFS vs Dijkstra vs Bellman Ford

Section 32 – All Pairs Shortest Path Problem

All pairs shortest path problem

Dry run for All pair shortest path

Section 33 – Floyd Warshall

Floyd Warshall Algorithm

Why Floyd Warshall?

Floyd Warshall with negative cycle,

Floyd Warshall in Java,

BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,

Section 34 – Minimum Spanning Tree

Minimum Spanning Tree,

Disjoint Set,

Disjoint Set in Java,

Section 35 – Kruskal’s and Prim’s Algorithms

Kruskal Algorithm,

Kruskal Algorithm in Python,

Prim’s Algorithm,

Prim’s Algorithm in Python,

Prim’s vs Kruskal

Section 36 – Cracking Graph and Tree Interview Questions (Amazon, Facebook, Apple, Microsoft)

Section 37 – Greedy Algorithms

What is a Greedy Algorithm?

Well known Greedy Algorithms

Activity Selection Problem

Activity Selection Problem in Python

Coin Change Problem

Coin Change Problem in Python

Fractional Knapsack Problem

Fractional Knapsack Problem in Python

Section 38 – Divide and Conquer Algorithms

What is a Divide and Conquer Algorithm?

Common Divide and Conquer algorithms

How to solve the Fibonacci series using the Divide and Conquer approach?

Number Factor

Number Factor in Java

House Robber

House Robber Problem in Java

Convert one string to another

Convert One String to another in Java

Zero One Knapsack problem

Zero One Knapsack problem in Java

Longest Common Sequence Problem

Longest Common Subsequence in Java

Longest Palindromic Subsequence Problem

Longest Palindromic Subsequence in Java

Minimum cost to reach the Last cell problem

Minimum Cost to reach the Last Cell in 2D array using Java

Number of Ways to reach the Last Cell with given Cost

Number of Ways to reach the Last Cell with given Cost in Java

Section 39 – Dynamic Programming

What is Dynamic Programming? (Overlapping property)

Where does the name of DC come from?

Top-Down with Memoization

Bottom-Up with Tabulation

Top-Down vs Bottom Up

Is Merge Sort Dynamic Programming?

Number Factor Problem using Dynamic Programming

Number Factor: Top-Down and Bottom-Up

House Robber Problem using Dynamic Programming

House Robber: Top-Down and Bottom-Up

Convert one string to another using Dynamic Programming

Convert String using Bottom Up

Zero One Knapsack using Dynamic Programming

Zero One Knapsack – Top Down

Zero One Knapsack – Bottom Up

Section 40 – CHALLENGING Dynamic Programming Problems

Longest repeated Subsequence Length problem

Longest Common Subsequence Length problem

Longest Common Subsequence  problem

Diff Utility

Shortest Common Subsequence  problem

Length of Longest Palindromic Subsequence

Subset Sum Problem

Egg Dropping Puzzle

Maximum Length Chain of Pairs

Section 41 – A Recipe for Problem Solving

Introduction

Step 1 – Understand the problem

Step 2 – Examples

Step 3 – Break it Down

Step 4 – Solve or Simplify

Step 5 – Look Back and Refactor

Section 41 – Wild West

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Algorithms

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[programmingsolver]

Lab 3: Queue Implementations

Goals: * Implement a Queue class using a link-based data structure: queue_linked.py * Implement a Queue class using a circular array: queue_array.py   * In the first implementation, you will use a linked structure similar to the linked structure used in implementing the Stack ADT (i.e. create a Node class).  In this case, there must be a way to add items to the back of the list and remove items…

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Test Your Knowledge: Quiz Challenge!!! 📝🧠

Which one of the following is the overflow condition if a circular queue is implemented using array having size MAX?🤔

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[myprogrammingsolver]

Homework 5 Stacks and Queues

Overview 1. Delete, re-adjust, and detect loop in a linked list. A queue is a container of objects (a linear collection) that are inserted and removed according to the first-in first-out (FIFO) principle. Both of them can be implemented using Arrays or LinkedLists. In this assignment, stacks are implemented using Linked Lists and Queues are modified to a circular queues implemented using…

[architectnews]

2020 Expo Dubai German Pavilion

2020 Expo Dubai German Pavilion, Event + Exhibition Building Architect, UAE Design Project Images

2020 Expo Dubai German Pavilion Design

ADUNIC: Event + Exhibition Architecture, UAE design by LAVA Architects

post updated 27 September 2021

The German Pavilion @ Expo 2020 Dubai opens on 1 October for six months.

Photo taken in September 2021:

image courtesy of architects practice

2020 Expo Dubai German Pavilion Opening

German Pavilion design embodies message of sustainability

LAVA’s design of the German Pavilion at Expo 2020 Dubai is an ensemble of suspended cubes, a forest of steel poles, covered by a floating roof, opening on 1 October. And behind these visually exciting design elements everything from intelligent use of local climatic conditions to materials reuse to connectedness is sustainable.

“The key question was how to design a temporary exhibition and event space for up to three million visitors in a desert environment that was sustainable. LAVA’s solution linked the Expo theme of connectedness, with our approach of ‘more with less’, with humans interacting with nature and technology at its heart,” said Tobias Wallisser, director of LAVA.

Photos taken in August 2021:

1. BUILDING AS EXHIBIT: Social Sustainability

“The building design is part of the exhibition, a tool to connect people.”

“We wanted to address the Expo motto ‘Connecting Minds, Creating the Future’ and so the team chose to represent Germany as a ‘campus’, an open place for exchange of knowledge, ideas and innovations. Rather than placing the buildings horizontally across a site, three suspended cubes are assembled vertically. This loose, porous stacking of volumes, an ensemble rather than a single form, suggests interconnectedness,” said Christian Tschersich, project director, LAVA.

The positioning of the cantilevered cubes generates a spacious central atrium for gathering and events. At the heart of the visitor experience this covered vertical space visually connects all functional areas with one another, helps with way-finding, creates diverse visual relationships and access points, and assists with management of large visitor numbers.

The visitor route brings people continuously onto terraces (on top of the cubed spaces). They can see their past and future trajectory, engage with other people, and enjoy vistas out to the Expo site. The exhibition spaces inside the cubes (Energy Lab, Future City Lab, and Biodiversity Lab) feature individual immersive experiences, whilst terrace exhibitions invite group interaction.

Rather than a traditional exhibition hall the campus metaphor sees the whole building as an exhibit, not merely a canvas to display, but a tool for connecting people and content, and a place to experience German innovations.

2. BUILDING AS EXHIBIT: STRUCTURAL SUSTAINABILITY

The actual structure is the message.

“We incorporated the principle of sustainability right from the start by using the minimum amount of material to create maximum volume. LAVA’s ‘more with less’,” said Tschersich.

Three cubes were stacked on top of a plinth with other functions (restaurant, pre-show, office, back-ofhouse) formed as an abstract landscape. This created a large volume at the centre, and a roof creates shade and comfort – a technical cloud. A sandwich of three parts: landscape – stacked cubes – roof. People between nature and technology.

The clever positioning of the stacked cubes is driven by local climate and features passive energy-saving features that reduce the impact of direct sunlight, generate natural shade, decrease the heat load and optimise the indoor climate. This intelligent creation of shade by the building elements also makes “hybrid” air conditioning possible. It also referenced the design of the local courtyard house, with closed exterior facades and rooms oriented towards an inner airspace that open up to each other.

A hybrid facade minimises the sense of building bulk and creates an iconic framing of the space. At the upper level a dynamic arrangement of 900 vertical steel poles, a forest of trees swaying in the wind, creates movement. With gradually changing angles they frame the central atrium space and modulate light.

An opaque, trapezoidal single-layer ETFE membrane can be opened and closed, responsive to different climate conditions during the six-month Expo period, such as sandstorms and cooler days, and minimises the need for air conditioning. The pavilion’s outer shell also includes 1.5 metres wide glass elements that can be rotated and opened, allowing the building to breathe.

The visually striking technical cloud roof creates shade and comfort. It allows daylight into the interior through multiple small openings, similar to sunlight penetrating a forest canopy, creating an ever-changing visitor experience. Mirror surfaces reflect direct sunlight against the roof skin, a dynamic interplay of light. At night, a field of LED lights integrated in the ceiling make the building radiate from within.

Resource consumption and the circular economy were also major design drivers and are reflected in numerous passive and active sustainability features – from Design for Disassembly (DfD) to “Mine the Scrap”, “grey energy”, sustainable and reusable building materials. The building will be repurposed after the Expo is finished, with standardised building elements such as steel poles dismantled and reconfigured into different geometries.

LAVA’s bottom-up approach focussed on visitor comfort, technology in the service of humans. “At LAVA we’re always looking at the interaction between people and the physical environment they inhabit. Sustainability requires that environments are adaptable and changeable,” added Wallisser.

The transition from hot exterior to inside was carefully considered. To reduce temperature shock and save on energy costs the architects designed a transitional space where visitors entering the building, with lengthy queues, will be cooled by a gentle water mist emanating from steel poles allowing them to gradually acclimatise. The central atrium is cooled by cold air expelled from the air conditioned exhibition spaces, thereby reducing energy usage and improving visitor comfort.

“An efficiently stacked volume of space, responding to the local environment with an intelligent climate management system. This project shows how buildings can be optimised, made intelligent, be reconfigured, can adapt to changing users, environments, temperatures, acoustics, and light,” said Alexander Rieck, director, LAVA.

Chris Bosse LAVA Director added: “Functional requirements, visitor experience, climate and environmental concerns are all resolved thorough this clever multi-performative design.”

Added Wallisser: “Architecture isn’t purely a façade. Of course we wanted the building to be Instagrammable! But also innovative, thought-provoking, with an effective experiential quality. The hardware of the building creates a journey for visitors from around the world.”

The concept continues innovation in pavilion design, from London’s Crystal Palace to Germany’s history – Mies van der Rohe’s German Pavilion at Expo ’29 in Barcelona, Frei Otto at Montreal’s Expo ’67.

The German Pavilion houses a three-level restaurant, VIP spaces for business meetings, an auditorium for events and performances, plus work spaces for 50 staff. It is located in the sustainability section, close to Al Wasl Plaza, which forms the heart of the Expo site. Expo 2020 Dubai 1 October 2021 to 31 March 2022 is divided into three districts: sustainability, mobility and opportunity, with exhibitions from 190 countries.

The Federal Ministry for Economic Affairs and Energy commissioned Koelnmesse GmbH to organise and run the German Pavilion. The “German Pavilion Expo 2020 Dubai Consortium”, comprising facts and fiction GmbH and NUSSLI Adunic AG is in charge of concept design, planning and realisation. Facts and fiction is responsible for content, exhibition and media design, and the pavilion was built by NUSSLI Adunic, with architecture and spatial design by LAVA.

2020 Expo Dubai German Pavilion UAE – Building Information

NAME OF PROJECT German Pavilion Expo 2020 MINISTRY Federal Ministry for Economic Affairs and Energy (BMWi) MANAGEMENT Koelnmesse GmbH LOCATION Dubai, UAE CLIENT CONSORTIUM facts and fiction with NUSSLI ADUNIC STATUS Built 2021 SIZE 4,600m2; building height 27 m PRACTICE CREDITS LAVA: Tobias Wallisser, Alexander Rieck, Chris Bosse with Christian Tschersich Project Team: Maria Pachi, Ahmed Rihan, Niklas Knap, Daniele Colombati, Wassef Dabboussi Competition team: Maria Pachi, Christina Ciardullo, Courtney Jones, Jed Finanne, Benjamin Riess, Joanna Rzewuska PARTNERS: Concept design, planning and implementation – facts and fiction/NUSSLI Structural engineers – schlaich bergermann partner Climate – Transsolar Reuse – Certain Measures MEP – energytec Fire – Steinlehner Light – Kardorff Ingenieure DRAWINGS © facts and fiction | NUSSLI Group| LAVA IMAGE CREDITS: © German Pavilion Expo 2020 Dubai / Björn Lauen; NUSSLI Group

Previously on e-architect:

9 Nov 2018

2020 Expo Dubai German Pavilion Building

Design: LAVA Architects

2020 Expo Dubai German Pavilion Building Design by LAVA

A vertical campus of nature and technology

LAVA’s design for the German Pavilion Expo 2020 Dubai is a vertical campus of nature and technology, taking cues from the local architecture and Germany’s history of outstanding lightweight pavilion design. Demonstrating the Expo theme, everything from intelligent use of local climatic conditions to materials reuse to construction is sustainable.

On behalf of the German Federal Ministry of Economy and Energy, Koelnmesse GmbH is responsible for the organisation and operation of the Pavilion, with Consortium German Pavilion EXPO 2020 Dubai facts and fiction GmbH and ADUNIC AG (concept design, planning and implementation) and LAVA (architecture/spatial concepts).

CONCEPT The airy construction is an efficiently stacked volume of space. A visually striking freeform roof encloses a spacious volume and an intelligent ensemble of interlinked floating cubes housing exhibition and event spaces. The contrast of enclosed air space and immersive exhibition experiences generates an exciting exchange between interior and exterior spaces. The open structure is formed from abstract elements and surprising materials, a composition of repetition and differentiation.

The concept continues German innovation in Expo pavilion design by using new ways of forming space – from Mies van der Rohe’s Barcelona 1929 to Frei Otto’s Montreal 1967 to Fritz Bornemann’s spherical concert hall Osaka 1970.

It also responds to the local climate and references the local courtyard house with closed exterior facade and rooms oriented towards an inner airspace.

ROOF An opaque trapezoidal single-layer ETFE membrane creates a large volume of space with a minimal enveloping surface and highly efficient material. The metallic skin lets light rays into the interior through many small openings, similar to sunlight penetrating forest foliage, creating a continuously changing visitor experience.

Mirror surfaces reflect direct sunlight against the roof skin, a dynamic interplay of light. At night, a field of LED lights integrated in the ceiling make the building radiate from within. Supported by vertical steel cables, the cloudscape roof keeps out the heat and controls light and temperature in the atrium. It reduces production energy, optimises resources by weight savings and creates the technical conditions for a pleasant visitor experience.

ATRIUM A central atrium, a green, open space, connects all visitor areas and allows many surprising perspectives. The composition of exhibition spaces, event area and restaurants are interwoven through these manifold visual relationships. The pavilion tour brings visitors continuously onto the terraces of the open atrium, providing an overview of their location within the pavilion, the variety of topics and social interaction with other visitors. Native German plants hang from the terraces and roof creating comfort in the atrium and special light.

LAYOUT The vertical campus courses between a landscaped layer on the lower two levels and the cloud roof. The rear east is a vertical backbone with technical facilities and service functions, and the front western side houses exhibition and restaurant spaces layered horizontally. The sequence of stacked seemingly floating building elements is a journey through the campus learning experience – from enrolment to learning curriculum to graduation. The ‘laboratories’, purposedesigned for exhibition, performance and dining, are grouped around the atrium.

SUSTAINABILITY The design continues LAVA’s philosophy of sustainability at multiple levels, a strong visual symbol and example for visitors, by:

1. Material-optimised construction based on nature’s geometries. 2. Passive energy saving measures applied at the very outset in the design – for example the stacked building elements not only trap vertical airspace, but also minimise direct sunlight creating a sheltered atrium with minimal solar input and optimised climate. 3. Intelligent arrangement creates different spatial situations, not through complex technology, but rather through digitised production processes. 4. Minimisation of grey energy and operational energy. 5. Graduated climatic zones of individual areas allow reduced energy use, whilst supporting the diversified room experience for visitors. 6. Reuse of building parts and materials – using algorithms to reduce waste. 7. Barrier-free access.

Tobias Wallisser, LAVA director, said: “It exemplifies LAVA’s work with natural geometries to generate efficient and beautiful structures and space through intelligent skins using integrative technology and cutting edge materials. And how buildings perform at many sustainable levels – environmental, structural and social.”

Alexander Rieck, LAVA director said: “The project continues LAVA’s expertise working in desert environments (Masdar Plaza UAE and more recently the university masterplan and headquarters for KACST in Saudi Arabia.) The integration of local climatic conditions, innovative use of recyclable materials and ground-breaking climate control technologies demonstrate how one can build and operate in harmony with the location.”

LAVA Director Chris Bosse said: “The challenge is: how does a country physically represent itself in an exhibition like the Expo? World exhibitions give a glimpse of the future from the perspective of their time. Our pavilion design shows Germany’s contribution to that view of the future. The sequence of different spatial areas encourages visitors to have fun whilst learning.”

“The building itself is part of the exhibition, a tool for connecting people and content, both a model of sustainability and a place to discover German innovations and solutions on this important topic,” added Christian Tschersich, Project Manager at LAVA.

The Expo 2020 theme is “Connecting Minds, Creating the Future�� and it runs from 20 October 2020 to 10 April 2021.

IMAGE CREDITS: (c) facts and fiction | adunic | LAVA

LAVA

2020 Expo Dubai German Pavilion Building images / information received 081118

20 Aug 2018

2020 Expo Dubai German Pavilion

Design: LAVA Architects

2020 Expo Dubai German Pavilion

Cologne, August 2018 – EXPO 2020: Cologne creativity and Swiss construction expertise for Dubai

facts and fiction and ADUNIC win German Pavilion bid

In a key milestone on the road to EXPO 2020 Dubai, Germany has decided who is to design and build the German Pavilion. The bid was won by a consortium comprising Cologne-based agency facts and fiction and Swiss construction company ADUNIC, with architecture to be designed by Berlin firm LAVA.

While facts and fiction will be responsible for content, exhibition and media design, building the pavilion will be ADUNIC’s job. facts and fiction specialises in spatial communication and ADUNIC are specialists in the construction of temporary buildings. As well as matching both companies’ profiles, the brief reflects the essence of Expos, which are all about giving those who visit during the short six months of the event an Expo experience that stays with them forever. “We’re delighted to have won the German Pavilion contest,” says Dietmar Jähn, a managing director at facts and fiction, commenting on the selection committee’s decision. “It’s a dream come true and a great honour for us to have our concept chosen to represent Germany at the EXPO in Dubai.”

An EU-wide notice of tender was published back in September 2017, inviting teams to apply to design the concept and to plan and build the interior and exterior of the German Pavilion for EXPO 2020 Dubai. The brief also included the technical management of the pavilion during the World Expo from 20 October 2020 to 10 April 2021 and the dismantling of the pavilion after the event.

Initially, applicants were asked to draw up a high-level concept. This was followed by a second phase, in which a 17-member selection committee (made up of representatives of various federal ministries, trade/industry associations and experts on Expo and regional matters) chose five interdisciplinary teams to put through to the next phase – the design of a detailed concept for the German Pavilion.

“The awarding of the contract to facts and fiction and ADUNIC marks the end of a ten-month process. We are happy that things can now progress and building work can start on our plot in Dubai next year,” says Dietmar Schmitz from Germany’s Federal Ministry for Economic Affairs and Energy. As Commissioner General of the German Pavilion, Mr Schmitz is responsible for the country’s presence in Dubai. He is head of the “Policy on fairs and exhibitions, expo participations” division at the Ministry and has been involved in World Exhibitions across the globe over the course of many years.

The two partners in the German Pavilion consortium are no newcomers to Expo either. facts and fiction designed the Monaco and Kazakhstan pavilions in Milan in 2015 as well as the German presence at EXPO 2012 in the Korean city of Yeosu. ADUNIC built a number of the pavilions in Milan in 2015.

New to Expo is LAVA, an international team of architects with offices in Berlin, Stuttgart and Sydney and a wealth of experience, having worked on a wide variety of major projects around the world. In the Middle East, for instance, LAVA helped plan the Masdar eco city in Abu Dhabi and also designed the architecture for the King Abdulaziz City for Science and Technology in Riyadh, Saudi Arabia.

facts and fiction and ADUNIC were selected on the basis of the tender criteria. The main requirement was to take the EXPO theme of “Connecting Minds, Creating the Future” and the sustainability subtheme chosen by Germany and translate them into a pavilion concept that will grasp visitors’ interest and hold it from start to finish while also creating a seamless marriage between the pavilion’s architecture and its content.

But all has not yet been revealed. The concept will not be presented in Germany until a press conference at Koelnmesse in Cologne on 4 September 2018, to be followed by a second press conference at the Steigenberger Hotel Business Bay in Dubai on 19 September. Koelnmesse is the company that will be organising and running the German Pavilion at the 2020 World Expo in Dubai on behalf of the Federal Ministry for Economic Affairs and Energy.

For more information, visit www.expo2020germany.de and on the YouTube channel

LAVA

2020 Expo Dubai German Pavilion images / information received 200818

Location: Jebel Ali, Dubai, UAE

Dubai 2020 Expo Pavilions

Dubai 2020 Expo Pavilions Design: Santiago Calatrava, Foster + Partners, BIG and Grimshaw Architects Dubai 2020 Expo Pavilions

2020 Expo Dubai Luxembourgish Pavilion Design: METAFORM Architects image courtesy of architects 2020 Expo Dubai Luxembourg Pavilion Building

Sustainability Pavilion for Expo 2020 Dubai Design: Grimshaw Architects image courtesy of architects Sustainability Pavilion for Expo 2020 Dubai

Dubai World Expo 2010 Masterplan Design: HOK / Populous 2020 Expo Dubai Pavilions by HOK / Populous

Dubai World Expo

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Burj 2020 Dubai Tower in UAE Architects: RNL Design photo courtesy of architects BURJ 2020 Dubai Building

Hyperloop Pods and Portals Design: BIG-Bjarke Ingels Group image from architects Hyperloop Pods and Portals in Dubai

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Dubai World Expo 2020

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Website: 2020 Expo Dubai German Pavilion

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