selection
•
0 likes•3 views
Selection sort is an in-place sorting algorithm that works by repeatedly finding the minimum element from unsorted part and putting it at the beginning. It maintains two subarrays, sorted subarray which is built up from left to right at the front of the array, and unsorted subarray to the right. In each iteration, the minimum element (considering ascending order) from the unsorted subarray is picked and moved to the next slot in the sorted subarray. This continues until the whole array is sorted. The time complexity of selection sort is O(n^2) as it requires n iterations and in each iteration it has to scan remaining unsorted elements to find the minimum.
Report
Share
Report
Share
Download to read offline
Recommended
insertion sort-new.pdf
Insertion sort is an O(n^2) sorting algorithm that builds up a sorted array one element at a time. It works by taking the first unsorted element and inserting it into the correct place in the already sorted portion of the array. This requires linearly searching the sorted portion and shifting elements over to make space. Each pass increases the length of the sorted portion by one. While O(n^2), insertion sort performs better than other quadratic sorts like selection sort on arrays that are already partially sorted.
5 DimensionalityReduction.pdf
This document provides an overview of dimensionality reduction techniques, specifically principal component analysis (PCA). It begins with acknowledging dimensionality reduction aims to choose a lower-dimensional set of features to improve classification accuracy. Feature extraction and feature selection are introduced as two common dimensionality reduction methods. PCA is then explained in detail, including how it seeks a new set of basis vectors that maximizes retained variance from the original data. Key mathematical steps of PCA are outlined, such as computing the covariance matrix and its eigenvectors/eigenvalues to determine the principal components.
Lecture 7_introduction_algorithms.pptx
This document introduces algorithms and some common algorithmic techniques. It begins by defining an algorithm as a step-by-step procedure for solving a problem. It then discusses what makes a good algorithm and provides examples of common algorithm design strategies like brute force, greedy, divide-and-conquer, and dynamic programming. The document proceeds to provide examples of algorithms for searching, sorting, finding largest values in matrices, and calculating powers and factorials recursively. It aims to explain fundamental algorithmic concepts and techniques.
lecture 10
The document discusses various sorting algorithms and their time complexities:
1. Comparison sorts like merge sort and quicksort have a best case time complexity of O(n log n).
2. Counting sort runs in O(n+k) time where k is the range of input values, and is not a comparison sort.
3. Radix sort treats input as d-digit numbers in some base k and uses counting sort to sort on each digit, achieving O(dn+dk) time which is O(n) when d and k are constants.
4. A randomized selection algorithm finds the ith order statistic in expected O(n) time using randomized partition.
Module 2_ Divide and Conquer Approach.pptx
The document describes the divide and conquer approach and analyzes the complexity of several divide and conquer algorithms, including binary search, merge sort, quicksort, and finding minimum and maximum values. It explains the general divide and conquer method involves three steps: 1) divide the problem into subproblems, 2) solve the subproblems, and 3) combine the solutions to solve the original problem. It then provides detailed explanations and complexity analyses of specific divide and conquer algorithms.
Quick Sort
QUICK SORT
MECHANISM
METHODOLOGY
PSEUDO CODE
ALGORITHM
ANALYSIS
DESIGN
TIME COMPLEXITY
SPACE COMPLEXITY
SOURCE CODE
APPLICATIONS
RECENT TRENDS OF QUICK SORT
RANDOMIZED QUICK SORT
NOTATIONS
sorting-160810203705.pptx
The document discusses sorting algorithms and their time complexities. It describes several common sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quick sort. Bubble sort, selection sort and insertion sort have a time complexity of O(n^2) in the worst case. Merge sort and quick sort are more efficient with divide-and-conquer approaches, giving them worst-case time complexities of O(n log n). The document provides pseudocode and explanations of how each algorithm works.
(Data Structure) Chapter11 searching & sorting
The document summarizes sorting and searching algorithms. It describes linear and binary search algorithms and analyzes their performance. It also describes selection sort, bubble sort, and heapsort algorithms. Selection sort has O(n^2) performance while bubble sort and heapsort have comparable performance to selection sort. Heapsort improves on these and has O(nlogn) performance by using a heap data structure implemented as an array.
Recommended
insertion sort-new.pdf
Insertion sort is an O(n^2) sorting algorithm that builds up a sorted array one element at a time. It works by taking the first unsorted element and inserting it into the correct place in the already sorted portion of the array. This requires linearly searching the sorted portion and shifting elements over to make space. Each pass increases the length of the sorted portion by one. While O(n^2), insertion sort performs better than other quadratic sorts like selection sort on arrays that are already partially sorted.
5 DimensionalityReduction.pdf
This document provides an overview of dimensionality reduction techniques, specifically principal component analysis (PCA). It begins with acknowledging dimensionality reduction aims to choose a lower-dimensional set of features to improve classification accuracy. Feature extraction and feature selection are introduced as two common dimensionality reduction methods. PCA is then explained in detail, including how it seeks a new set of basis vectors that maximizes retained variance from the original data. Key mathematical steps of PCA are outlined, such as computing the covariance matrix and its eigenvectors/eigenvalues to determine the principal components.
Lecture 7_introduction_algorithms.pptx
This document introduces algorithms and some common algorithmic techniques. It begins by defining an algorithm as a step-by-step procedure for solving a problem. It then discusses what makes a good algorithm and provides examples of common algorithm design strategies like brute force, greedy, divide-and-conquer, and dynamic programming. The document proceeds to provide examples of algorithms for searching, sorting, finding largest values in matrices, and calculating powers and factorials recursively. It aims to explain fundamental algorithmic concepts and techniques.
lecture 10
The document discusses various sorting algorithms and their time complexities:
1. Comparison sorts like merge sort and quicksort have a best case time complexity of O(n log n).
2. Counting sort runs in O(n+k) time where k is the range of input values, and is not a comparison sort.
3. Radix sort treats input as d-digit numbers in some base k and uses counting sort to sort on each digit, achieving O(dn+dk) time which is O(n) when d and k are constants.
4. A randomized selection algorithm finds the ith order statistic in expected O(n) time using randomized partition.
Module 2_ Divide and Conquer Approach.pptx
The document describes the divide and conquer approach and analyzes the complexity of several divide and conquer algorithms, including binary search, merge sort, quicksort, and finding minimum and maximum values. It explains the general divide and conquer method involves three steps: 1) divide the problem into subproblems, 2) solve the subproblems, and 3) combine the solutions to solve the original problem. It then provides detailed explanations and complexity analyses of specific divide and conquer algorithms.
Quick Sort
QUICK SORT
MECHANISM
METHODOLOGY
PSEUDO CODE
ALGORITHM
ANALYSIS
DESIGN
TIME COMPLEXITY
SPACE COMPLEXITY
SOURCE CODE
APPLICATIONS
RECENT TRENDS OF QUICK SORT
RANDOMIZED QUICK SORT
NOTATIONS
sorting-160810203705.pptx
The document discusses sorting algorithms and their time complexities. It describes several common sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quick sort. Bubble sort, selection sort and insertion sort have a time complexity of O(n^2) in the worst case. Merge sort and quick sort are more efficient with divide-and-conquer approaches, giving them worst-case time complexities of O(n log n). The document provides pseudocode and explanations of how each algorithm works.
(Data Structure) Chapter11 searching & sorting
The document summarizes sorting and searching algorithms. It describes linear and binary search algorithms and analyzes their performance. It also describes selection sort, bubble sort, and heapsort algorithms. Selection sort has O(n^2) performance while bubble sort and heapsort have comparable performance to selection sort. Heapsort improves on these and has O(nlogn) performance by using a heap data structure implemented as an array.
Unit 7 sorting
The document discusses various sorting algorithms including insertion sort, selection sort, bubble sort, merge sort, and quick sort. It provides detailed explanations of how each algorithm works through examples using arrays or lists of numbers. The key steps of each algorithm are outlined in pseudocode to demonstrate how they sort a set of data in either ascending or descending order.
module2_dIVIDEncONQUER_2022.pdf
The document describes various divide and conquer algorithms including binary search, merge sort, quicksort, and finding maximum and minimum elements. It begins by explaining the general divide and conquer approach of dividing a problem into smaller subproblems, solving the subproblems independently, and combining the solutions. Several examples are then provided with pseudocode and analysis of their divide and conquer implementations. Key algorithms covered in the document include binary search (log n time), merge sort (n log n time), and quicksort (n log n time on average).
Unit 2 in daa
1) The document describes the divide-and-conquer algorithm design paradigm. It can be applied to problems where the input can be divided into smaller subproblems, the subproblems can be solved independently, and the solutions combined to solve the original problem.
2) Binary search is provided as an example divide-and-conquer algorithm. It works by recursively dividing the search space in half and only searching the subspace containing the target value.
3) Finding the maximum and minimum elements in an array is also solved using divide-and-conquer. The array is divided into two halves, the max/min found for each subarray, and the overall max/min determined by comparing the subsolutions.
algorithm Unit 2
1) The document describes the divide-and-conquer algorithm design paradigm. It splits problems into smaller subproblems, solves the subproblems recursively, and then combines the solutions to solve the original problem.
2) Binary search is provided as an example algorithm that uses divide-and-conquer. It divides the search space in half at each step to quickly determine if an element is present.
3) Finding the maximum and minimum elements in an array is another problem solved using divide-and-conquer. It recursively finds the max and min of halves of the array and combines the results.
Daa final
Merge sort is a sorting algorithm that uses the divide and conquer technique. It divides the unsorted list into halves, recursively sorts the sub-lists, and then merges the sorted sub-lists into a single sorted list. The key steps are dividing, conquering by recursively sorting the sub-problems, and merging the sorted sub-lists. Merge sort has a time complexity of O(n log n) in all cases (best, average, worst) and requires additional storage space.
Algorithms with-java-advanced-1.0
The document discusses algorithms and their use for solving problems expressed as a sequence of steps. It provides examples of common algorithms like sorting and searching arrays, and analyzing their time and space complexity. Specific sorting algorithms like bubble sort, insertion sort, and quick sort are explained with pseudocode examples. Permutations, combinations and variations as examples of combinatorial algorithms are also covered briefly.
Chapter 11 - Sorting and Searching
The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
chapter1.pdf ......................................
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure for solving a problem and analyzes the time complexity of various algorithms. Key points made include:
1) Algorithms are analyzed based on how many times their basic operation is performed as a function of input size n.
2) Common time complexities include O(n) for sequential search, O(n^3) for matrix multiplication, and O(log n) for binary search.
3) Recursive algorithms like fibonacci are inefficient and iterative versions improve performance by storing previously computed values.
Module 2 Design Analysis and Algorithms
The document discusses several algorithm design strategies including brute force, divide and conquer, and decrease and conquer. It provides examples of each strategy, including string matching and linear search as brute force algorithms. Merge sort and Strassen's matrix multiplication are presented as divide and conquer algorithms, with analysis of their time complexities. Binary search is analyzed as a decrease and conquer algorithm with logarithmic time complexity.
220exercises2
1. The document discusses sorting and searching algorithms and exercises related to analyzing their time complexities. It provides solutions for exercises involving sorting lists by different schemes, optimizing sorting algorithms by splitting arrays, and selecting subsets from sorted and unsorted arrays.
2. Key points made include that sorting then selecting items is faster than repeated searches for large lists, and that splitting an array into square root of n subsets each sorted and merged provides the optimal time complexity of O(n√n) for sorting algorithms like insertion sort.
3. Questions ask to determine complexities of sorting algorithms like insertion sort, merge sort, and selection from arrays, and to recommend optimal algorithms like quicksort or selection for given array sizes and problem constraints
Implementing Minimum Error Rate Classifier
In this experiment, I tried to implement Minimum
error rate classifier using the posterior probabilities which
uses Normal distribution to calculate likelihood probabilities to
classify given sample points
Medians and Order Statistics
This document discusses order statistics and medians. It defines the i-th order statistic as the i-th smallest element of a sorted array. The median is defined as the halfway point of a data set. For an odd number of elements, the median is the (n+1)/2 order statistic, and for an even number it is defined as the average of the n/2 and n/2+1 order statistics. Methods for finding the minimum and maximum in expected linear time using pairs are presented. Selection algorithms for finding the k-th order statistic in expected and worst-case linear time using techniques like median of medians are also summarized.
07 Analysis of Algorithms: Order Statistics
One of the most important tasks when analyzing a collection of numbers is to find the ith smallest element. Examples of these elements are
• The minimum
• The maximum
• The Median for n odd,
Then, it is necessary to find fast solutions to find this statistics.
lecture 11
This document discusses binary search trees (BSTs) and their use for dynamic sets and sorting. It covers BST operations like search, insert, find minimum/maximum, and delete. It explains that BST sorting runs in O(n log n) time like quicksort. Maintaining a height of O(log n) can lead to efficient implementations of priority queues and other dynamic set applications using BSTs.
Perform brute force
The document discusses brute force algorithms and exhaustive search techniques. It provides examples of problems that can be solved using these approaches, such as computing powers and factorials, sorting, string matching, polynomial evaluation, the traveling salesman problem, knapsack problem, and the assignment problem. For each problem, it describes generating all possible solutions and evaluating them to find the best one. Most brute force algorithms have exponential time complexity, evaluating all possible combinations or permutations of the input.
K- Nearest Neighbor Approach
The document discusses K-Nearest Neighbor (KNN) algorithm as a supervised classification approach. KNN is a non-parametric technique that classifies a query instance based on its similarity to training examples. It finds the K closest neighbors of the query instance and predicts the class based on a majority vote of its neighbors' classes. The value of K is an important hyperparameter that affects classification accuracy. Larger K values generally improve accuracy by smoothing the decision boundary.
Sorting algorithms
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.
anova Statistical analysis
1) The document discusses various statistical analysis techniques including data tabulation, graphical representation, standard deviation, ANOVA, and linear regression analysis.
2) Data tabulation involves arranging data in logical tables to reduce space, explanations, and help with comparisons. Graphical representation involves using charts like linear, bar, and pie charts to visually depict relationships.
3) Standard deviation and standard error are discussed as measures of dispersion in a data series and sampling distribution respectively. ANOVA and linear regression are analyzed as methods to understand relationships between variables and predict outcomes.
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
More Related Content
Similar to selection
Unit 7 sorting
The document discusses various sorting algorithms including insertion sort, selection sort, bubble sort, merge sort, and quick sort. It provides detailed explanations of how each algorithm works through examples using arrays or lists of numbers. The key steps of each algorithm are outlined in pseudocode to demonstrate how they sort a set of data in either ascending or descending order.
module2_dIVIDEncONQUER_2022.pdf
The document describes various divide and conquer algorithms including binary search, merge sort, quicksort, and finding maximum and minimum elements. It begins by explaining the general divide and conquer approach of dividing a problem into smaller subproblems, solving the subproblems independently, and combining the solutions. Several examples are then provided with pseudocode and analysis of their divide and conquer implementations. Key algorithms covered in the document include binary search (log n time), merge sort (n log n time), and quicksort (n log n time on average).
Unit 2 in daa
1) The document describes the divide-and-conquer algorithm design paradigm. It can be applied to problems where the input can be divided into smaller subproblems, the subproblems can be solved independently, and the solutions combined to solve the original problem.
2) Binary search is provided as an example divide-and-conquer algorithm. It works by recursively dividing the search space in half and only searching the subspace containing the target value.
3) Finding the maximum and minimum elements in an array is also solved using divide-and-conquer. The array is divided into two halves, the max/min found for each subarray, and the overall max/min determined by comparing the subsolutions.
algorithm Unit 2
1) The document describes the divide-and-conquer algorithm design paradigm. It splits problems into smaller subproblems, solves the subproblems recursively, and then combines the solutions to solve the original problem.
2) Binary search is provided as an example algorithm that uses divide-and-conquer. It divides the search space in half at each step to quickly determine if an element is present.
3) Finding the maximum and minimum elements in an array is another problem solved using divide-and-conquer. It recursively finds the max and min of halves of the array and combines the results.
Daa final
Merge sort is a sorting algorithm that uses the divide and conquer technique. It divides the unsorted list into halves, recursively sorts the sub-lists, and then merges the sorted sub-lists into a single sorted list. The key steps are dividing, conquering by recursively sorting the sub-problems, and merging the sorted sub-lists. Merge sort has a time complexity of O(n log n) in all cases (best, average, worst) and requires additional storage space.
Algorithms with-java-advanced-1.0
The document discusses algorithms and their use for solving problems expressed as a sequence of steps. It provides examples of common algorithms like sorting and searching arrays, and analyzing their time and space complexity. Specific sorting algorithms like bubble sort, insertion sort, and quick sort are explained with pseudocode examples. Permutations, combinations and variations as examples of combinatorial algorithms are also covered briefly.
Chapter 11 - Sorting and Searching
The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
chapter1.pdf ......................................
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure for solving a problem and analyzes the time complexity of various algorithms. Key points made include:
1) Algorithms are analyzed based on how many times their basic operation is performed as a function of input size n.
2) Common time complexities include O(n) for sequential search, O(n^3) for matrix multiplication, and O(log n) for binary search.
3) Recursive algorithms like fibonacci are inefficient and iterative versions improve performance by storing previously computed values.
Module 2 Design Analysis and Algorithms
The document discusses several algorithm design strategies including brute force, divide and conquer, and decrease and conquer. It provides examples of each strategy, including string matching and linear search as brute force algorithms. Merge sort and Strassen's matrix multiplication are presented as divide and conquer algorithms, with analysis of their time complexities. Binary search is analyzed as a decrease and conquer algorithm with logarithmic time complexity.
220exercises2
1. The document discusses sorting and searching algorithms and exercises related to analyzing their time complexities. It provides solutions for exercises involving sorting lists by different schemes, optimizing sorting algorithms by splitting arrays, and selecting subsets from sorted and unsorted arrays.
2. Key points made include that sorting then selecting items is faster than repeated searches for large lists, and that splitting an array into square root of n subsets each sorted and merged provides the optimal time complexity of O(n√n) for sorting algorithms like insertion sort.
3. Questions ask to determine complexities of sorting algorithms like insertion sort, merge sort, and selection from arrays, and to recommend optimal algorithms like quicksort or selection for given array sizes and problem constraints
Implementing Minimum Error Rate Classifier
In this experiment, I tried to implement Minimum
error rate classifier using the posterior probabilities which
uses Normal distribution to calculate likelihood probabilities to
classify given sample points
Medians and Order Statistics
This document discusses order statistics and medians. It defines the i-th order statistic as the i-th smallest element of a sorted array. The median is defined as the halfway point of a data set. For an odd number of elements, the median is the (n+1)/2 order statistic, and for an even number it is defined as the average of the n/2 and n/2+1 order statistics. Methods for finding the minimum and maximum in expected linear time using pairs are presented. Selection algorithms for finding the k-th order statistic in expected and worst-case linear time using techniques like median of medians are also summarized.
07 Analysis of Algorithms: Order Statistics
One of the most important tasks when analyzing a collection of numbers is to find the ith smallest element. Examples of these elements are
• The minimum
• The maximum
• The Median for n odd,
Then, it is necessary to find fast solutions to find this statistics.
lecture 11
This document discusses binary search trees (BSTs) and their use for dynamic sets and sorting. It covers BST operations like search, insert, find minimum/maximum, and delete. It explains that BST sorting runs in O(n log n) time like quicksort. Maintaining a height of O(log n) can lead to efficient implementations of priority queues and other dynamic set applications using BSTs.
Perform brute force
The document discusses brute force algorithms and exhaustive search techniques. It provides examples of problems that can be solved using these approaches, such as computing powers and factorials, sorting, string matching, polynomial evaluation, the traveling salesman problem, knapsack problem, and the assignment problem. For each problem, it describes generating all possible solutions and evaluating them to find the best one. Most brute force algorithms have exponential time complexity, evaluating all possible combinations or permutations of the input.
K- Nearest Neighbor Approach
The document discusses K-Nearest Neighbor (KNN) algorithm as a supervised classification approach. KNN is a non-parametric technique that classifies a query instance based on its similarity to training examples. It finds the K closest neighbors of the query instance and predicts the class based on a majority vote of its neighbors' classes. The value of K is an important hyperparameter that affects classification accuracy. Larger K values generally improve accuracy by smoothing the decision boundary.
Sorting algorithms
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.
anova Statistical analysis
1) The document discusses various statistical analysis techniques including data tabulation, graphical representation, standard deviation, ANOVA, and linear regression analysis.
2) Data tabulation involves arranging data in logical tables to reduce space, explanations, and help with comparisons. Graphical representation involves using charts like linear, bar, and pie charts to visually depict relationships.
3) Standard deviation and standard error are discussed as measures of dispersion in a data series and sampling distribution respectively. ANOVA and linear regression are analyzed as methods to understand relationships between variables and predict outcomes.
Similar to selection (20)
chapter1.pdf ......................................
chapter1.pdf ......................................
Searching and sorting Techniques in Data structures
Searching and sorting Techniques in Data structures
Machine Learning Notes for beginners ,Step by step
Machine Learning Notes for beginners ,Step by step
Recently uploaded
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Summary Of transcription and Translation.pdf
Hello Everyone Here We are Sharing You with The process of protien Synthesis in very short points you will be Able to understand It Very well
8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)
Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.
Farming systems analysis: what have we learnt?.pptx
Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024
Alternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024. (June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.Pests of Storage_Identification_Dr.UPR.pdf
InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses
Applied Science: Thermodynamics, Laws & Methodology.pdf
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
Class 10 human eye notes physics
Handwritten best quality
Recently uploaded (20)
GBSN - Biochemistry (Unit 6) Chemistry of Proteins
GBSN - Biochemistry (Unit 6) Chemistry of Proteins
The debris of the ‘last major merger’ is dynamically young
The debris of the ‘last major merger’ is dynamically young
8.Isolation of pure cultures and preservation of cultures.pdf
8.Isolation of pure cultures and preservation of cultures.pdf
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
Tissue fluids_etiology_volume regulation_pressure.pptx
Tissue fluids_etiology_volume regulation_pressure.pptx
Farming systems analysis: what have we learnt?.pptx
Farming systems analysis: what have we learnt?.pptx
Alternate Wetting and Drying - Climate Smart Agriculture
Alternate Wetting and Drying - Climate Smart Agriculture
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
Applied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdf
Immersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths Forward
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
selection
- 1. DESIGN AND ANALYSIS OF ALGORITHMS Selection Sort MADHAVAN MUKUND, CHENNAI MATHEMATICAL INSTITUTE http://www.cmi.ac.in/~madhavan NPTEL MOOC,JAN-FEB 2015 Week 2, Module 3
- 2. Sorting Searching for a value Unsorted array — linear scan, O(n) Sorted array — binary search, O(log n) Other advantages of sorting Finding median value: midpoint of sorted list Checking for duplicates Building a frequency table of values
- 3. How to sort? You are a Teaching Assistant for a course The instructor gives you a stack of exam answer papers with marks, ordered randomly Your task is to arrange them in descending order
- 4. Strategy 1 Scan the entire stack and find the paper with minimum marks Move this paper to a new stack Repeat with remaining papers Each time, add next minimum mark paper on top of new stack Eventually, new stack is sorted in descending order
- 5. Strategy 1 … 74 32 89 55 21 64
- 6. Strategy 1 … 21 74 32 89 55 21 64
- 7. Strategy 1 … 21 32 74 32 89 55 21 64
- 8. Strategy 1 … 21 32 55 74 32 89 55 21 64
- 9. Strategy 1 … 21 32 55 64 74 32 89 55 21 64
- 10. Strategy 1 … 21 32 55 64 74 74 32 89 55 21 64
- 11. Strategy 1 … 21 32 55 64 74 89 74 32 89 55 21 64
- 12. Strategy 1 … Selection Sort Select the next element in sorted order Move it into its correct place in the final sorted list
- 13. Selection Sort Avoid using a second list Swap minimum element with value in first position Swap second minimum element to second position …
- 14. Selection Sort 74 32 89 55 21 64
- 15. Selection Sort 74 32 89 55 21 64
- 16. Selection Sort 21 32 89 55 74 64
- 17. Selection Sort 21 32 89 55 74 64
- 18. Selection Sort 21 32 89 55 74 64
- 19. Selection Sort 21 32 89 55 74 64
- 20. Selection Sort 21 32 55 89 74 64
- 21. Selection Sort 21 32 55 89 74 64
- 22. Selection Sort 21 32 55 64 74 89
- 23. Selection Sort 21 32 55 64 74 89
- 24. Selection Sort 21 32 55 64 74 89
- 25. Selection Sort 21 32 55 64 74 89
- 26. Selection Sort SelectionSort(A,n) // Sort A of size n for (startpos = 0; startpos < n; startpos++) // Scan segments A[0]..A[n-1], A[1]..A[n-1], … // Locate position of minimum element in current segment minpos = startpos; for (i = minpos+1; i < n; i++) if (A[i] < A[minpos]) minpos = i; // Move minimum element to start of current segment swap(A,startpos,minpos)
- 27. Analysis of Selection Sort Finding minimum in unsorted segment of length k requires one scan, k steps In each iteration, segment to be scanned reduces by 1 t(n) = n + (n-1) + (n-2) + … + 1 = n(n+1)/2 = O(n2)
- 28. Recursive formulation To sort A[i .. n-1] Find minimum value in segment and move to A[i] Apply Selection Sort to A[i+1..n-1] Base case Do nothing if i = n-1
- 29. Selection Sort, recursive SelectionSort(A,start,n) // Sort A from start to n-1 if (start >= n-1) return; // Locate minimum element and move to start of segment minpos = start; for (i = start+1; i < n; i++) if (A[i] < A[minpos]) minpos = i; swap(A,start,minpos) // Recursively sort the rest SelectionSort(A,start+1,n)
- 30. Alternative calculation t(n), time to run selection sort on length n n steps to find minimum and move to position 0 t(n-1) time to run selection sort on A[1] to A[n-1] Recurrence t(n) = n + t(n-1) t(1) = 1 t(n) = n + t(n-1) = n + ((n-1) + t(n-2)) = … = n + (n-1) + (n-2) + … + 1 = n(n+1)/2 = O(n2)