Group Observation Report

Group Observation Report
1. As the instructor, I will be able to view personal contributions (e.g., content, frequency, duration)
to group activities (i.e., discussions in the reading forum, planning in the working forum, and
editing/finalizing group assignments on the Group Wiki). What will you do to increase your task
visibility? If you havenï¿½t already done so, you and your group members may want to
assign/volunteer for specific KIN 247 group roles (e.g., group leader(s), homework editor(s),
homework submitter(s), etc.). Consider what it is that your group needs to accomplish and how you
might personally contribute to your groupï¿½s productivity.
In order to increase my task visibility by ensuring that I am contributing to the group project in the
best way possible, whether that be through giving feedback to other members when needed,
working hard on the assigned role I was given, or by making sure everything is being completed to
the best of my groups ability. Additionally, to increase my task visibility I will make sure that I help
to facilitate group roles, as that has been found to be the best end result for everyone involved. I
personally feel that I can contribute to the group productivity by assigning individuals roles, and
help others out in my group who may need help. I think as a group we need to accomplish making
sure that everyone is on the same page, and that everyone knows what they should be doing. When
people are feeling confused they tend to not know what to do and sometimes do not actually do
anything, so I want to ensure that I am a group member that is always willing to help others out
when they have questions or do not understand something. I also feel that I could personally
contribute by helping set up a work schedule that ... Show more content on Helpwriting.net ...
Piezon and Donaldson (2005) provide a list of recommendations to help reduce social loafing in
online group settings. Discuss how you might implement some of these strategies to ensure your
commitment to your groupï¿½s overall
... Get more on HelpWriting.net ...

Graph Theory Concepts and Strategies with Ticket to Ride...
Ticket to Ride is a board game created by Alan R. Moon that has been growing in popularity since
its first release in 2004 by Days of Wonder. The game components include a map with cities and
defined train routes, sets of 45 colored, plastic train car tokens for up to five players, destination
tickets, and colored train cards. The premise of the game involves collecting enough of the colored
train cards to claim or build train routes to connect various major cities in the United States and
southern Canada to earn points as well as completing routes designated on the destination tickets.
The game itself is not only a fun way to spend hours playing but it is also a good tool to showcase
various concepts in graph theory and combinatorics. ... Show more content on Helpwriting.net ...
There are also multi–colored locomotive that are used like wild cards. The colored train cards are
shown in Figure 2. The second part of the set up to deal out five destination tickets (three, if not
playing with the "Mega Game" expansion) a minimum of three which must be retained. Any
destination tickets not kept by a player are returned to draw pile for later in the game. Note that
some versions of the game remove any discarded tickets from play for the rest of the game. An
example of a destination ticket is shown in Figure 3.
On a player's turn, he/she chooses one of three options: Build a train connecting two adjacent cities
using sets of colored train cards to earn points immediately, draw up to two additional colored train
cards to be used later to build a train, or draw three additional destination tickets keeping a
minimum of one to earn points at the end of the game. Play continues in this fashion for each player
until one player has two or fewer train tokens left and then everyone still has one more turn.
The points earned from building a train connecting two adjacent cities are based on the length of the
route with the longer routes earning more points per train token. Routes of length one and two earn
only one point per token while a route of length three earns a total of four

Structural Vulnerabilities And Link Privacy
Structural vulnerabilities and Link Privacy in Social Networks
Introduction/Background:
In social networks, a link represents a relationship between two nodes in the network. These links
can represent email conversations, web surfing, co–purchases of two or more products (e.g.
Amazon), friendships (e.g. Facebook), followers (e.g. Twitter), etc. Often times these relationships
are sensitive and/or confidential in nature [ying–wu] and the users are operating under the
assumption that their private relationships will not be disclosed.
In recent years the amount of data accumulated from social networks has become very large, and
there is a lot of valuable information to gain from analyzing and applying data mining to social
network data. ... Show more content on Helpwriting.net ...
Results:
Neighborhood Randomization Using Sub–Graph Perturbation
In order for people to mine valuable data from social network graphs they must first be given
information about the network. Even without explicit information about the nodes, an attacker may
use structural information about the nodes and graph itself (e.g. node degree) to identify who the
individuals are that the nodes represent. Simple graph–wise randomization addresses this problem
by deleting k randomly chosen edges and replacing them with k randomly chosen edges, however a
problem arises since data–miners depend on these structural attributes to properly analyze the social
network. Fard and Wang [fard–wang] propose a structure–aware algorithm for the randomization of
social network edges as well as a formal definition of "link privacy" with respect to a probabilistic
threshold. Their motivation is to help conceal sensitive links by using randomization techniques,
without disturbing the actual structure of the graph, which is achieved through local neighborhood
perturbation. This is needed so that graphs can be analyzed without the link structure being left
entirely vulnerable to attackers. The goal of their algorithm is to make it so that an adversary cannot
know if a link in the original graph exists from having a link in the new graph.
Problem definition: "Given a

MTT Project 1 Graphs With Application
MTT Project 1 (Graphs with application) Name: Mohamed Alzbeidi ID: 1054072 Section: 1
Function: Quadratic Function Instructor: Jaya Kumar A. Introduction: Quadratic is the function that
is used for a squared degree. In this function its graph is called a parabola. The graph of all quadratic
function is called a parabola its shape is basically a U shape it might be transformed or reflected or
inversed witch might change the shape in some cases The simplest quadratic function is: y(x)=X^2
Furthermore, the general form to the quadratic function is y=〖ax〗^2+bx+c=0 In any case where a
quadratic function cannot be solved the quadratic formula is used x=(–b±√(b^2–4ac))/2a The
Quadratic Inverse: y(x)=√x ... Show more content on Helpwriting.net ...
The equation of the reflected graph is y=〖– (x)〗^2 Example: y(x)=〖–(2x–3)〗^2+1 C.
Combination of all transformations with its inverse: Basic Function: y=–1/2 (2x–4)^2+2 Inverse
Function: y(x)=2±√(2–x)/√2 Full Graph including basic function its inverse and y = x D. Real life
situation Quadratic Function A ball shot can be made using the equation y=–0.〖0281x〗^2+2x+10
, Where x is distance traveled (in feet) and y is the height (also in feet). How long was the throw?
First we apply the quadratic formula x=(–2±√(2^2 )–4(–0.281×10))/(2(–0.0281)) x=75.9 ft x=–4.7
y=10 E. Summary Although quadratic functions do not seem difficult but it is related to most fields
in science and examples can be made by any shape or act that might represent the form of a
parabola. Including, there are more complex polynomials than the quadratic but it seems that this
function is the best introduction to functions and there graphs. Recourses https://www.desmos.com/

Questions On Arab Open University Of Computing Studies
Arab Open University Faculty of computing studies Discrete Mathematics. M131. 2016_2017.
TMA Name: Mariam Ahmed Mostafa Abdelmnem Mohamed. ID: 1551310073 Group : 4 I hereby
declare that this submitted TMA work is a result of my own efforts and I have not plagiarized any
other person 's work. I have provided all references of information that I have used and quoted in
my TMA work. Name of Student: Mariam Ahmed Mostafa Abdelmnem Mohamed. Signature:
Mariam Ahmed Date: 6–12–2016 Question(1): ? F F F F T T T F F T T F a)by using p , q , ¬ and ^ .
P q ¬p ¬p ^ q F F T F F T T T T F F F T T F F b)by using p , q , ¬ and V . P Q ¬q P V ¬q ¬(p V ¬q)
F F T T F F T F F T T F T T F T T F T F c) by using p, q , ¬ and →. P Q ¬p ¬q ¬p → ¬q ¬(¬p →
¬q) F F T T T F F T T F F T T F F T T F T T F F T F Question(2): . False. Counter example : –1 (X
+1)(X–2)=0 (0 +1)(0–2)>=0 –2≱0 . True. P(0) (X +1)(X–2)< 0 True P(3) (X +1)(X–2)< 0 True. P(3)
(X +1)(X–2)< 10}. .|■(X)|=7 Reason: X={–3,–2,–1,0,1,2,3} {x | xis natural number and 9x2 1 =
0}. .|■(X)| = 0 Reason: 9x2–1=0 9x2=1 x2=1/9 x=±1/3 x = Ø c)P(A), where A is the power set of
{a, b, c}. .|■(X)| = 8 Reason:|■(p(x))| = 2n = 23 = 8 d)A×B, where A={a, b, c} and B={1, 2, 3, 4, 5}.
.|■(A*B)| = 15 .Reason:|■(A*B)|=|■(A)|*|■(B)| =3*5 = 15 e)ɸ ×B, where B={2, 4, 6,

Annotated Bibliography On Import Java
/*package adsa;*/
/** * * @author GOPIKRISHN */ import java.util.HashSet; import java.util.Iterator; import
java.util.Random; import java.util.Set; import java.util.InputMismatchException;
public class AdjListGraph
{
private int distances[]; private int nodes; public static final int MAX_VALUE = 999; private
Set<Integer> visited; private Set<Integer> unvisited; private int adjacencyMatrix[][]; public
AdjListGraph(int nodes) //Constructor { this.nodes = nodes; distances = new int[nodes + 1]; visited
= new HashSet<Integer>(); unvisited = new HashSet<Integer>(); adjacencyMatrix = new int[nodes
+ 1][nodes + 1]; } public void Dijkstra(int AdjacencyMatrix[][], int source) { int evaluationNode;
for (int i = 1; i <= nodes; i++) for (int j = 1; j <= nodes; j++) adjacencyMatrix[i][j] =
AdjacencyMatrix[i][j]; for (int i = 1; i <= nodes; i++) { distances[i] = Integer.MAX_VALUE; }
unvisited.add(source); distances[source] = 0; while (!unvisited.isEmpty()) { evaluationNode =
getNodeWithMinimumDistanceFromUnvisited(); unvisited.remove(evaluationNode);
visited.add(evaluationNode); evaluateNeighbours(evaluationNode); } } private int
getNodeWithMinimumDistanceFromUnvisited() { int min ; int

Explanation Of A Computer System
#include
#include //in this version, you only need left //and right rotation, not 4 cases
#include
#include using namespace std;
struct Node
{
int data; struct Node* left; struct Node* right; int height;
};
//a function to calculate height of the tree int height(struct Node* root)
{
if(root == NULL) { return 0; //if there is no node, return 0 } return root–>height; //else, repeat the
function
}
//a helper function to create a new node faster
Node* newNode(int data)
{
Node* node = new Node(); node–>data = data; node–>left = NULL; node–>right = NULL; node–
>height = 1; // new node is added at leaf return (node); //return the pointer to the newly created node
}
//rotations
Node* rightRotate(Node* input)
{
Node* x ... Show more content on Helpwriting.net ...
If this node is unbalanced, there are 4 cases //Left Left case //notice balance will change depends on
how you //calculate your balance factor if(balance > 1 && data < node–>left–>data) { return
rightRotate(node); }
//Right Right case if(balance < –1 && data > node–>right–>data) { return leftRotate(node); }
//Left Right case if(balance > 1 && data > node–>left–>data) { node–>left = leftRotate(node–>left);
return rightRotate(node); }
//Right Left case if(balance < –1 && data < node–>right–>data) { //swapping using rightRotate,

since it is a pointer node–>right = rightRotate(node–>right); return leftRotate(node); }
//return the (unchanged) node pointer return node; }
Node* FindMinNode(Node* root) //find the minimum value node in the tree
{
Node* current = root; //keep traversing to the leftest leaf since it WILL be in the left while(current–
>left != NULL) { current = current–>left; } return current;
}
//recursion are like moving from stations to
//stations
Node* deleteNode(Node* root, int data)
{
//1. Perform standard BST delete if(root == NULL) { return root; } //if the key to be deleted is
smaller than //root's key, then go left, recursively if( data < root–>data) { root–>left =
deleteNode(root–>left, data); }
//if the key to be deleted is bigger than //root's key, then go right, recursively else if(data > root–
>data) { root–>right =

mth221 r2 network flows case study Essay
23
Network Flows
Author: versity. Arthur M. Hobbs, Department of Mathematics, Texas A&M Uni–
Prerequisites: The prerequisites for this chapter are graphs and trees. See
Sections 9.1 and 10.1 of Discrete Mathematics and Its Applications.
Introduction
In this chapter we solve three very different problems.
Example 1
Joe the plumber has made an interesting offer. He says he has lots of short pieces of varying gauges
of copper pipe; they are nearly worthless to him, but for only 1/5 of the usual cost of installing a
plumbing connection under your house, he will use a bunch of T– and Y–joints he picked up at a
distress sale and these small pipes to build the network shown in Figure 1. He claims that it will
deliver three gallons per minute ... Show more content on Helpwriting.net ...
Example 4
Find a flow in the graph of Figure 3.
Solution:
The path p = s, b, a, t extends from s to t, and seen as a sequence of pipes, the largest amount of flow
that could travel along it is the minimum of the capacities of the pipes comprising it. This minimum
is 2, which is c(s, b)
Chapter 23 Network Flows
Figure 3.
411
A small capacitated s,t–graph.
and also c(b, a). Thus we put number pairs on each of the edges, the second entry being 2 for each

edge in the path and 0 for the other two edges. The result is shown in Figure 4.
Figure 4.
Graph of Figure 3 with flow along path s,b,a,t.
There are two ways we can view a flow, and Example 4 illustrates them both. One view is to trace
out the path from the source to the sink of one or more units of flow. In the example, path p is such a
path. The other view is to measure the total flow in each edge of the graph. This view is shown in
the example by our placing the amount of flow along each edge. Since there is actually only one
flow, namely the orderly procession of fluid from the source to the sink through the network, these
two views must be equivalent.
When solving the problem of finding maximum flows through the graph, the second view is
preferable for two reasons. If we are searching a very large network by hand, it may well be
impossible for us to find a best set

A Game Of Thrones Based Off The Very Popular George R.r
The HBO series A Game of Thrones based off the very popular George R.R. Martins fantasy book
series entitled, A Song of Ice and Fire. The HBO series follows nine noble families and their fight
for control of Westeros, the land in which they all call home. The series has political and sexual
intrigue along with networks within networks that are pervasive throughout the series. In season one
of A Game of Thrones, King Robert, of Westeros, asks his old friend, Lord Stark, to serve as Hand
of the King, or the second in command. Secretly warned that the previous "Hand" and dear friend to
both Lord Stark and the King Robert was assassinated, Lord Stark accepts the offer only to
investigate the former Hand 's assassination further. Meanwhile the Queen 's family, the Lannister's,
the wealthiest house in the realm may be hatching a plot to take power of the Seven Kingdoms.
Across the sea, the last two members of the previously overthrown family, the Targaryens, are also
scheming to regain the throne. The eldest of the two remaining Targaryen's, Viserys is attempting to
arrange a wedding between his sister Daenerys to a Dothraki horse lord (leader of an estimated
40,000 fighters) in an attempt to build an army through marriage. Unbeknownst to the rest of the
kingdom, a heavier threat heads south from the northern outreaches of the kingdom to destroy the
realm, the only thing that stands in the way are a band of misfit criminals sentenced to "the wall" (a
mile high, thousands of miles

Online Forums And Platforms Of Social Media
2. SOCIAL MEDIAANALYTICS
The several online forums and platforms that allow a person to synthesize, update, delete and
exchange data is Social media [10]. Social media can be categorized [30, 31] as:
Social networks: The explosion of startups is causing new social networks to pop up.
Blogs: The best way to put an end to that silly belief is to read a large number of blogs.
Microblogs: Studies by Treude et al., Storey, and Yuan et al. have shown that a wealth of interesting
information is stored in these microblogs.
Social news: Sift through journals so that others don 't have to.
Social bookmarking: Popular way to return to your site regularly to see if there something new and
interesting.
Media sharing: Where content hunters ... Show more content on Helpwriting.net ...
Social media analytics has seen a widespread application in marketing of late. This is due to the
growing adoption of social media by people [32]. Forrester Research [5], projects social media to be
one of the fastest growing marketing channels in the US between 2010 and 2015 [33]. User–
generated content and interactions between the network entities are the two main sources of
information in social media. Social media analytics can be categorized into two groups based on
this:
Content–based analytics: This type of analytics deals with large amounts of unstructured and noisy
data (Text, audio, video and images) created and exchanged by users on social media platforms, as
discussed earlier, can be applied to derive insight from such data. Data processing challenges can be
solved by adopting big data technologies.
Structure–based analytics (Social network analytics): This type of analytics deals with gaining
intelligence from the participants' relationships and creating structural attributes of a social network.
The structure of a social network is created with the help of nodes and edges, as a network graph,
where each participant is represented by a node and each edge represents the relationship between
two participants. We discuss two kinds of graphs, social graphs and activity graphs [34].
In social graphs, an edge between a pair of nodes only indicates the existence of a relationship
between two corresponding participants. Social graphs can be analyzed to

Gossip-Based Algorithms
1. INTRODUCTION
Gossip–based algorithm plays a major part for distributing simple and efficient information in large
networks. One of the examples of gossip–based algorithm is rumor –spreading model. It is also
called as rumor mongering. It is introduced by Daley & Kendall (D K model) in the context of
duplicated databases. The rumor spreading algorithm is an example of epidemic process. It is
mainly used to examine in the view of mathematics. The algorithm follows synchronous rounds.
The main aim of rumor spreading is to spread a rumor to all nodes in a social network in small no of
rounds. At the beginning of the round, the information is sent to initial node known as start node.
Then the information is sent to all nodes. The node having information will not accept to receive the
information again. While executing the algorithm the graph and degree of nodes must be constant.
In case of dynamic networks, an evolving graph is introduced to study the behavior of graph and
nodes.
Fig. 1 Graph connected with rumors
1.1. Problem statement:
To begin with the rumor spreading algorithm mainly concentrates the broadcasting of message that
is the information should reach all nodes of a graph. Secondly it concerns about the completion time
i.e., within how many rounds the information is reached to all nodes. From the above research the
problem can be stated as :each node transfers the rumor what has but in cases the node might not be
knowing what information that the neighbour

Grade Hierarchy Analysis Call Graph
According to Figure 2.8, node 7, node 8 and node 9 do not have any other predecessors except node
5 & node 6 and by removing non–instantiated methods they become head nodes. So, I should
remove these heads from graph and this new graph can be considered as RTA result for the given
CHA Call Graph (Figure 2.9).
Figure 2.9: The result of removing head nodes from graph
To clarify this approach, I will use the computed Class Hierarchy Analysis Call Graph from the first
example (Figure 2.6) and convert it to RTA. Since set of instantiated classes contains Class B &
Class C, according to the algorithm, I have to remove node A.m( ).
Moreover, if I check Call Graph again, I will find that node Interface.( ) has a reflexive edge and it's
indegree=1 . Therefore, this node should be deleted as well.
Figure 2.10 illustrates a conversion from CHA to RTA:
CHA retrieved call graph
Removing non–instantiated node Removing non–connected node
Figure 2.10: CHA to RTA conversion
2.3.3 Class Type Analysis (CTA)
CTA's main idea is narrowing down the set of reachable methods of a call site b.n( ) inside method
A.m( ) by keeping track of "available target types" within class A. Since CTA algorithm is
refinement of CHA and RTA, I can reuse CHA or RTA Call Graph result in CTA and decrease the
set of reachable methods of a call b.n( ) to make it more precise.
CTA algorithm implementation has three phases:
a) Class Graph Generation
b) Data flow
c) Call Graph Generation
a)

Learning Tree Executive Summary
Nowadays, in the education industry has been a highly competitive industry and many new
competitors are entering the market. Learning Tree International, Inc. was originated in 1974 and
headquartered in Reston, Virginia, it is considered one of the well–known companies in the
education industry. According to Yahoo Finance, Learning Tree Inc. has 393 full–time employees.
Learning Tree International, Inc. (LTRE), operates in the education and training services industry
(SIC code: 8200). The services that the company provides are training and education for commercial
and government information technology and management professionals. Also, it known for its
spread worldwide and that they offer their services online through what they call "Learning ... Show
more content on Helpwriting.net ...
The return on equity ratio for the company is –66.01%, K12's is 3.7%, and the industry is 21.35%,
This is another indication that the company is not operating well and that the shareholders are
currently not earning from their investments in the company. On the other hand, the competitor –
K12– is also operating poorly with comparison to the industry's average percentage, but it is
performing better than Learning Tree International, Inc. Also, using another profitability ratio,
which is return on assets. The company's ratio is –13.3, K12's ratio is 2.74, and the industry's ratio is
11.85; consequently, the company has a negative percentage while the percentages for the industry
and K12 are positive. So the company is not employing its total asset to generate profit as the same
as K12. In short, when comparing the profitability ratios of the company with industry and K12, it
shows that the company is in unstable condition with its investors. Moreover, the earning per share
for Learning Tree International, Inc. over the last three years are: $–0.90 on 9/12, $–0.66 on 9/13,
and $–0.50 on 9/14. Even though the company still has a negative EPS, but it has been increasing
from year to year. In addition, the price/sale ratio is a ratio that measure the stock price with the
annual sales and could be a good comparison between companies. The company's P/S ratio is 0.27,
K12 is 0.77, and the industry is 1.3, so we can tell that the company is clearly below its competitor
and its

A Note On Detection Algorithm
2.1 PAGE CHANGE DETECTION ALGORITHM
2.1.1 Introduction: About 60% of the content on the web is dynamic. It is quiet possible that after
downloading a particular web page, the local copy of the page residing in the repository of the web
pages becomes obsolete compared to the copy on the web. Therefore a need arises to update the
database of web pages. Once a decision has been taken to update the pages, it should be ensured that
minimal resources are used in the process. Updating only those elements of the database, which
have actually undergone a change, can do this. Importance of web pages to be downloaded has been
discussed in the above section. It also checks whether the page is already there in the database or not
and lowers its priority value if it is referred rather frequently. In this section, we discuss some
algorithms to derive certain parameters, which can help in deriving the fact whether the page has
changed, or not. These parameters will be calculated at the time of page parsing. When the client
again counters the same URL, it just calculates the code by parsing the page without downloading
the page and compares it to the current parameters. If changes in parameters are detected, it is
concluded that the page has changed and needs to be downloaded again. Otherwise the URL is
discarded immediately without further processing. The following changes are of importance when
considering changes in a web page:
Change in page structure.
Change in text contents.

Hafordan Function Essay
3.27. Cut vertex: Let G= (V, E) be a connected graph. A vertex V ϵ G is called a cut vertex of graph
G, if "G – V" results in a disconnected graph G.
3.28. Cut edge: Let G= (V, E) be a connected graph, an edge E ϵ G is called a cut edge of graph G, if
"G–E" result in a disconnected graph G.
3.29. Euler graph: A connected graph G=(V, E) is said to be Euler graph (traversable), if there exists
a path which includes, (which contains each edge of the graph G exactly once) and each vertex at
least once (if we can draw the graph on a plain paper without repeating any edge or letting the pen).
Such a path is called Euler path. ... Show more content on Helpwriting.net ...
A Hamiltonian path presents the efficiency of including every vertex in the route.
4.2. Traffic Signal Lights:
To study the traffic control problem at an arbitrary point of intersection, it has to be modeled
mathematically by using a simple graph for the traffic accumulation data problem. The set of edges
of the rudimentary graph will represent the communication link between the set of nodes at an
intersection. In the graph stand for the traffic control problem, the traffic streams which may move
at the same time at an intersection without any difference will be joined by an edge and the streams
which cannot move together will not be connected by an edge.
The functioning of traffic lights i.e. turning Green/Red/Yellow lights and timing between them. Here
vertex coloring technique is utilised to solve contravenes of time and space by identifying the
chromatic number for the number of cycles needed.
4.3. Social Networks:
We connect with friends via social media or a video gets viral, here user is a Vertex and other
connected users produce an edge, therefore videos get viral when reached to certain connections. In
sociology, economics, political science, medicine, social biology, psychology, anthropology, history,
and related fields, one often wants to study a society by examining the structure of connections
within the society. This could befriend networks in a high school or Facebook, support networks in a
village or political/business

Reflection Paper For Science
The students were working on science fair projects for science, and many of the students needed to
create a graph to add to their science fair backboard. This allowed me to teach a lesson that would
become multidiscipline lesson combining math skills and scientific data analysis. The purpose of the
graph was to display data collected during the student during their experiments. Initially, the
students responded well to the lesson and were focused. I began the lesson by asking the students
about the movie, Jurassic Park. The student become engaged immediately and I had to take a minute
to refocus the class before continuing on the with the lesson. The movie was popular among the
students and familiar. The dinosaurs in the movie were computer generated models, CG. I pointed
out that the creation of CG characters was a marriage of math and science. Computer science creates
the model; however, the animals need to appear the correct size which requires math. Scientist are
not old men in lab coats toiling over a microscope, scientists are inventing new technologies,
creating digital monsters, and changing the world. A few students struggled to refocus and were
distracted, but were easily returned to task with personal attention. I was pleasantly surprised when
a student I had previous struggled with focused on the task and engaged in this lesson. She
completed the sample graph, and then asked for assistance completing her project graph.
Unfortunately, I faced challenges with

The And Of The Forest
In investigation 1, the data points are very clustered from the height of 150cm to the height of
300cm. This makes sense as saplings are defined as young trees that are taller than 1.35m above the
forest floor. So between 150cm and 300cm is most likely average height which would be the
majority of the population. The overall data set of investigation 1 is alot more spread out than the
compact data set of investigation 2. On the other hand, in investigation 2 all the data points are
overlapping and in line with the regression line, up to a height of 600cm where there is few data
points, indicating that the majority of saplings taken in this sample are young and planted for
regeneration of the forest purposes.
In both cases there is a positive relationship between the two sets of variables. In investigation one,
as H of a sapling increases, the DBH tends to increase. This would make sense because as the
sapling grows up, it subsequently grows in diameter because it manufactures new cells around its
circumference which increase the diameter at breast height in order to maintain and support the
weight of the growing height. On the graph there appears to be some unusal feature's where the
height of the Sapling is approx 450cm however the DBH of just below 10cm is alot lower than the
other saplings of the sample population that have a height of 450cm. It is possible that some sapling
trees, like this one, are alot thinner and taller. This could be caused by a mutation. However

Connectivity And Network Security : Connectivity
Connectivity and Network Security
1. Introduction In this paper, we will look into three topics: connectivity, Menger 's theorem, and
network flows to further understand the application of connectivity such as network systems. In
graph theory, connectivity is an important topic and can be applied to many different areas. By
considering the connectivity of the graph(network system map), we will be able to see clearly the
problems of the graph(the system), such as low–connectivity that may lead to the vulnerability of an
attack. Once we know the properties of the graph(the system), we can determine or change how the
graph is or should be.
2. Connectivity
A graph G is connected if for all pairs u, v ∈ G, there is a path in G from u to v. Note that it suffices
for there to be a walk from u to v. [Graph Theory, p. 9] A walk in G is a sequence of vertices
v0,v1,v2,...,vk, and a sequence of edges (vi, vi+1) ∈ E(G). A walk is a path if all vi are distinct.
[graph_theory_notes, p. 8] Figure 1 [Graph Theory, p. 9]
A (connected) component of G is a connected subgraph that is maximal by inclusion. We say G is
connected if and only if it has one connected component. [Graph Theory, p. 9]
Figure 2 [Graph Theory, p. 9]
2.1 Vertex connectivity
A vertex cut in a connected graph G = (V,E) is a set S ⊆ V such that GS:= G[V S] has more than one
connected component. A cut vertex is a vertex v such that {v} is a cut. [Graph Theory, p. 17]
Notation GS= G[V S] means that, given a subset

Multi-objective Reconfiguration of Electrical Distribution...
EDSs are mainly designed meshed but operated radially for some technical and financial concerns.
Distribution networks can be represented with a graph in ordered pairs consisting of a set of
vertices, i.e. buses and a set of edges, i.e. branches; in terms of mathematics this equivalents to a
sparse matrix which its non–zero elements signifies the existence of an edge in the system. On this
basis a typical distribution network is radial if it forms a tree where each load bus is exactly supplied
from one source node, i.e. substation bus [11]. This suggests MOEDNRC problem as identifying the
set of non–dominated trees of the given graph. In this section we've devised a heuristic technique
based on this idea as well as the rules defined in [30] to retain the connectivity and radial properties
of individuals during the optimization process. It's worth mentioning that these properties are
broadly disturbed by EAs due to the stochastic nature of these algorithms unless a heuristic plan is
devised to preserve the mentioned properties. As a result generation of infeasible agents in sheer
numbers by EAs is quite a normal observation. The proposed technique is able to prevail over this
shortcoming and would increase the performance of EAs as well. Before proceeding with the
designed technique, some terminologies are first introduced to set the stage for the plan.
Loop vectors (LVs)
The term LV is used to identify branches contributing to forming loops in EDSs when all

Presentation Of Data Security Arrangement
Presentation of data security Data security arrangement is a situated of strategies issued by a
business to ensure that all data learning clients inside the space of the association or its systems
affirm with standards and methodology unified to the wellbeing of the data put away digitally
anytime in the system or inside the association 's furthest reaches of power. Fundamental
CHARACTERISTICS OF INFORMATION Accessibility Availability grants clients UN
organization should access information to attempt to in this manner while not obstruction or
impediment, and to get it inside the required arrangement. Availability of learning is open to any
client. Requires the check of the client altogether with endorsed access to the information. The
information, then, is asserted to be out there to an authorized client once and wherever obliged and
inside the right configuration. Case: – Research libraries that need ID before passageway.
Bookkeepers shield the substance of the library, in place that its out there exclusively to affirmed
supporters. The expert ought to see and settle for a supporter 's verification of ID before that
benefactor has free and straightforward access to the substance out there inside the book room.
Precision Information is right when its free from slip–ups or slips and It has the value that the tip
client anticipates. Information contains a value totally unique in relation to the client 's desires on
account of the deliberate or

Team Member Duties : Neeraj Kumar ( Team Leader )
Team Member Duties
Neeraj Kumar (Team Leader)
 Selecting and understanding the concept based on the past research experience on sensor database
networks and distributed programming.
 Strategic plan design on weekly basis helped in completion of project.
 Implementation of leader election algorithm.
 Dividing and assigning the task based on the team members interest area and capabilities.
 Managing the whole project with full cooperation with all team members with good team
communication.
Satish Ekambaram
 Drafting the whole paper with APA format.
 Deep research on the value and need of the leader election algorithm in mobile ad hoc network.
 Finding out the real applications implementing the leader election algorithm.
Srikanth Bommana
 Good research on the history related topic with the whole project.
 Research on what the mobile ad hoc network and its need and evolution.
 Finding out the very brief and good conclusion of the whole project. Abstract
Technology advancement is growing very rapidly one example we can see surrounding us is
wireless networks and its related very complex applications such as sensor database network,
robotics military and so on. At the starting point following paper represents about the mobile ad hoc
network and related basic history. After that research paper explores the problems related with the
current technology and major drawbacks. Later paper shows the need of the leader election
algorithm and its implementation. At the end it

Csc200 Week 3
Taylor Shuler CSCI 36200 HW1 Report with Run Time Analysis Selection Sort: Pseudocode: n =
A.length for j = 1 to n – 1 c1: n smallest = j c2: n–1 for i = j + 1 c3: ∑_(j=1)^(n–1)▒〖(j+1)〗 if
A[i] < A[smallest] c4: ∑_(j=1)^(n–1)▒j smallest = i c5: ∑_(j=1)^(n–1)▒〖jt_(i,j) 〗 exchange A[j]
with A[smallest] c6: n–1 Best Case: Already Sorted; tij = 0 Worst Case: Sorted Backwards; tij = 1
T(n)=c_1 n+ c_2 (n–1)+ c_3 (∑_(j=1)^(n–1)▒(j+1) )+ c_4 (∑_(j=1)^(n–1)▒j)+ c_5 (∑_(j=1)^(n–
1)▒〖jt_(i,j) 〗)+ c_6 (n–1) ... Show more content on Helpwriting.net ...
In insertion sort, the best case is not quadratic because it only has to verify each entry once since it
would have nothing to insert. The algorithm would just read through the array once and see that it is
already sorted. Selection sort must check each entry twice to verify that the array is correctly sorted
in the best

Calculus Program Entry Essay
There is a metaphor about math education which posits that doing math is like painting, and yet
most students focus on whitewashing fences rather than examining pieces by the great masters of
art. Up until taking calculus in high school, my thoughts about math rarely strayed beyond fence
painting. However, I was lucky enough to get a passionate high–school calculus teacher who made
it a goal to introduce us to Van Gogh, Salvador Dali, and the like. From such introductions I began
to build my own sense of mathematical curiosity and build internal motivation to explore deeper
problems than fence painting. As a result, I have come to a place in life where I believe that making
a career of math research will be the true path to my own eudaimonia. Through my conversations
with the aforementioned calculus teacher I was shown work in real analysis and probability. I
understood none of it at the time, but was able to take away that the work I was doing in my
calculus and statistics classes were merely the tips of icebergs that I am glad to be descending upon
now. I was also introduced to more exotic fields like knot theory and chaos theory. Chaos theory
was the field that interested me the most, and so we would often talk about this during class
downtime. What got me seriously interested was the notion of non integer dimensions. ... Show
more content on Helpwriting.net ...
At the end of this I will take stock of my current situation and if all is well I plan to proceed to
pursue a PhD and continue onward to becoming a research mathematician. Moreover, I have no
qualms with other associated duties. That is, I have had positive experiences as a grader for
university math classes and I also believe that I would in turn enjoy teaching. This is because of the
commonly held belief that the best way to learn is to teach and that I believe teaching to be one of
the most noble

Encryption in Today's Information Systems
In today's world of instant connectivity and information at users' fingertips, it's vital that sensitive
information is safeguarded against those who seek to do personal harm and profit from gaining
access to the data. The key behind keeping information safe is the method in which it's protected
and encrypted. In order to appreciate how information is secured, users must understand the
encryption concepts behind it. To do this, one must comprehend the current encryption standards,
the trends and developments in encryption technology, the importance of securing data, the
government's regulations pertaining to encryption, the companies involved in research and
implementation, the implications of leaked or stolen data, and a brief look into ... Show more
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When a fellow general received the message, he would wrap the paper around his corresponding
scytale to decipher the message (Tyson 2014). Since the advent of computers though, encryption has
become increasing important and relies almost solely on cryptographic means to secure information.
When speaking about encryption today, it refers more to the process rather than the mathematical
formulas used to scramble data. The basic idea behind encrypting a computer message is such that it
is scrambled with a sequence of random bits, known as a key, and only parties with the
corresponding key can transpose it back into a comprehensible format. These keys are created via a
cipher, otherwise known as an algorithm. When a user sends a message, known as the plaintext,
across a network, the computer applies an algorithm to the information to encode it, resulting in a
ciphertext (Encryption Basics 2014). This method can be best summarized visually: Plaintext
message + encryption algorithm + secret key = Ciphertext Ciphertext + corresponding key +
decryption algorithm = Plaintext message Generally speaking, modern encryption techniques fall
into one of two categories – symmetric (homogeneous) and asymmetric (heterogeneous). Symmetric
encryption is a system of communication whereby both parties share the same key to encode and
decode a message. The Spartan generals used this method with their scytales.

Analysis Of Local Search Algorithm For STP
From the tree SP we presented in the algorithm that we have obtained via Local Search Algorithm
for STP, we have generated the matrix of cost. This is done by assigning a cost to all the edges of
tree SP and by assigning a cost on "n" no. of nodes to all the other edges in graph. This assignment
of cost helps in recognizing the cost of the longest possible path between a pair of nodes in any
spanning tree is n−1 (i.e. it passes n−1 edges) while the cost of the shortest path between any pair of
nodes without using of SPT edges is at least "n" (i.e. passes one edge). Consequently, the 802.1d
protocol will produce the intended spanning tree "SP".
3.5 DATA GENERATION
In this section we progress by generating network topologies and traffic ... Show more content on
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root = 1; in_tree = {root}; considered = ∅; while #in_tree< n do select (u ∈in_tree) and (u !∈
considered); selectnum_branch∈ [min..max] ; foreach i ∈ [1..num_branch] do if #in_tree< n then
select (v ∈ [1..n]) and (u /∈in_tree); creatEdge(u, v); in_tree = in_tree + {v} end end considered =
considered + u; end To the obtained spanning tree from above algorithm we add two types of edges
so that we can get a bi–connected graph. The bi–connected graph has a significance that if any of
the edge becomes down then also the network will be connected via another edge. This gives us
assurance of always up time for a network. This means in case of link failure alternate link will
always be present to ensure the network connectivity.
In this type1 edge connect a leaf with the higher level node while the type 2 edge connect a non–
leaf node (not the root) with the no–leaf node or lower level node of different branch. For each tree
new "n–1" edges are added while the generation of bi–connected graph.
To pretend a network in which a switch has many ports, we define a ratio "r". This means each node
in the tree is connected to at least "r" edges. In each test graph, from the generated bi–connected
graph, we create three more trees with ratio r15 = n/15, r10 = n/10 and r5 = n/5 (where n = no. of
nodes).
3.5.4 The FAT Tree:
Figure shown below depicts the Fat Tree – another topology for DCNs proposed in [35] It is called
Fat Tree because it is not a

Use Of A Theory And Pagerank Algorithm
Mathematics in Football
The purpose of this report is to research the use of mathematics in football. In particular, we have
researched the use of network theory and PageRank algorithm. The report aims to consider which
strategies are optimal and discuss whether teams ought to adopt a more mathematical approach to
on–field play.
Introduction
Network theory is becoming more and more used within football to help teams analyse and change
their style of play. It consists of a collection of nodes, with edges joining them that are weighted
depending on how frequent a path is used. In football, the nodes represent the players of a team, the
edges will be the passes between them and the weight will be the amount of passes between them.
We will ... Show more content on Helpwriting.net ...
The PageRank algorithm gives more weight to a link if it comes from a high–ranking page. In terms
of football this means a pass from a 'popular' player is more valuable than a pass from a player who
is hardly involved in the game. Player's statistics constantly change throughout the game and the
PageRank score of a player depends on the score of his teammates so all scores must be computed at
the same time and the algorithm is only finished once the game has ended. In summary, PageRank
algorithm roughly assigns to each player the probability that he will have the ball after a reasonable
number of passes have been made. [8]
Mathematical Background
One way to analyse players position is closeness centrality, which is defined as the inverse geodesic
distance of a node in the network [1]:
(1)
Where Aij is the total amount of passes from player i to j. This formula uses incoming and outgoing
passes as equal measure. This will show how well connected the players are to each other.
Another way is betweenness centrality, which measures the extent to which a node lies on paths
between other nodes[2]:
(2)
Where is the number of geodesic paths from j to k going through i and is the total number of
geodesic paths.
The PageRank algorithm can be defined by: (3)

Where = is the total number of passes made by player , is a heuristic parameter that represents the
probability of the player keeping the

Analysis Of Reliability Calculations On Mobile Ad Hoc...
Analysis of Reliability Calculations in Mobile Ad hoc Networks
Sai Charan Goud Kolanu, Tejaswi Reddy Karemma
Department of Computer Engineering and Computer Science
California State University, Long Beach
Abstract
With the increasing dependency on wireless networks, the need for proper reliability analysis for
Mobile ad hoc networks (Manets) is also increasing. Failure of Manets in areas like warfare, nuclear
reactors, medical equipment and airplanes can lead to catastrophe. Unlike traditional networks,
measuring the reliability of Manets is a tedious task as it involves dynamically changing topology.
The existing methods for calculating reliability use two terminal analysis as the basis for
calculation. It uses the same method used for traditional computer networks to calculate reliability.
However, the method is not very efficient when it comes to the wireless networks as they are far
different from traditional networks. It is also a time consuming task to identify all the nodes and
links in a wireless network as nodes move freely in the network. In This paper, We are going to
discuss about NLN(Node–Link–Node) technique which reduces the complexity of analyzing the
reliability in Manets.
Keywords: Manet, NodeLinkNode, Reliability.
INTRODUCTION:
The Mobile ad hoc networks is one of the emerging technologies today. The instability of the nodes
in a mobile ad hoc network makes it difficult to calculate the reliability of the network. When a node
moves freely move in a

Chronicle Of A Death Foretold By Gabriel Garcia Marquez
The novella Chronicle of a Death Foretold, a journalistic account of a historical murder, is written
by author Gabriel García Márquez. Continually through his career "Garcia Marquez employs
journalistic writing techniques in his fiction, and particularly in Chronicle of a Death Foretold in
order to produce a seemingly more authentic and credible work"( Gardener 3–4). This particular
novel reads as if it is fictional. However, readers are interested to know that the account is based on
a factual event. It is based on an event involving some of the authors closest friends thirty years
before the novel's date of publication. It is believed to be "A perfect integration of literature and
journalism"(Gardener 1). Marquez tells readers he uses ... Show more content on Helpwriting.net ...
The novel's "precise detailing of the time of each event and the matter–of–fact usage of language"
helps to bring this style to life (Pelayo 116). The technique of 'Chronicling' is presented from the
very beginning when the novella states, "On the day they were going to kill him, Santiago Nasar got
up at five–thirty in the morning to wait for the boat the bishop was coming on" (Marquez 169). This
type of exact factual evidence allows readers to be pulled back into reality. It also leaves the 'why' of
Santiago Nasar's death and the "social milieu that despises the murder" to be left unclear to readers
(Aghaei 13). This is a part of the style of "prolepsis" which entails the narration of an event before
an earlier event takes place. This helps the author to keep the reader in suspense of how it happens.
In this specific novel readers "follow the story step–by–step through the successive events" (Aghaei
13). Additionally, the narrator's lack of personal commentary keeps the novella to appear objective,
accurate, and neutral. This technique is used in real world journalism by reporters and journalists
worldwide. Garcia Marquez expresses his views on the presentation of facts by stating "'The key is
to tell it straight'"(Gardener 13).
The novella as a whole is written in a pseudo journalistic style. This means that the story is told
through a series of flashbacks and interviews used to help describe and support the events taking
place. This style

Description Of A Graph
Use the map to create a graph where vertices represent street intersections and edges represent
streets. Define c(u,v) = 1 for all edges (u,v). Since a street can be traversed, start off by creating a
directed edge in each direction, then make the transformation to a flow problem with no antiparallel
edges as described in the section. Make the home the source and the school the sink. If there exist at
least two distinct paths from source to sink then the flow will be at least 2 because we could assign
f(u,v) = 1 for each of those edges. However, if there is at most one distinct path from source to sink
then there must exist a bridge edge (u, v) whose removal would disconnect s from t. Since c(u, v) =
1, the flow into u is at most 1. We may ... Show more content on Helpwriting.net ...
Exercise 26.2–10
Suppose we already have a maximum flow f. Consider a new graph G where we set the capacity of
edge (u, v) to f (u, v). Run Ford–Fulkerson, with the mod– ification that we remove an edge if its
flow reaches its capacity. In other words, if f(u,v) = c(u,v) then there should be no reverse edge
appearing in residual network. This will still produce correct output in our case because we never
exceed the actual maximum flow through an edge, so it is never advantageous to cancel flow. The
augmenting paths chosen in this modified version of Ford– Fulkerson are precisely the ones we
want. There are at most |E| because every augmenting path produces at least one edge whose flow is
equal to its capacity, which we set to be the actual flow for the edge in a maximum flow, and our
modification prevents us from ever destroying this progress.
Problem 26–5
a. Since the capacity of a cut is the sum of the capacity of the edges going from a vertex on one side
to a vertex on the other, it is less than or equal to the sum of the capacities of all of the edges. Since
each of the edges has a capacity that is ≤ C, if we were to replace the capacity of each edge with C,
we would only be potentially increasing the sum of the capacities of all the edges. After so changing
the capacities of the edges, the sum of the capacities of all the edges is equal to C|E|, potentially an
overestimate of the original capacity of any cut, and so of the minimum cut.
b.

The Plan For The Group Adventure
For our group adventure, we did something that no one would expect. After considering Professor
Carly's recommendation, we decide to go with it. We decide to meet up before class on Wednesday
at the nearby Starbucks. But, we met at Starbucks an hour before class, which is 0700 am. It was the
only option for all of us since there wasn't any time slot that is available for all of us. It was really
tough waking up for the group adventure, but it was fun. While drinking coffee and eating breakfast,
we get to know more about each other and we got close very well. Other than meeting at Starbucks,
we get together again at Walter Library, where we discussed for our upcoming group presentation.
There, we discussed various of things, and cleared ... Show more content on Helpwriting.net ...
For example, while making for our group presentation, I recommend to the group using Prezi, which
is an online based presentation slide. Although it was my responsibility in making the slide look
good during the presentation, I decided that we all should have the chance to edit the slide, and
make it look good, rather than I doing it by myself, therefore, by doing that way, there won't be any
superiority feeling towards one another. Another concepts is Group Cohesiveness. Having a good
bond within the group members is a must, since it will allow us all to work more efficiently,
resulting higher quality of result. Building cohesiveness is like bringing us together. The main
strategic to build group cohesiveness are encouraging compatible membership, develop shared
goals, accomplish task, develop a positive history of cooperation, and promote acceptance of group
members (80). During our group adventure, we developed our group cohesiveness very well. Our
first meeting was very awkward, we encourage one another, talk about where we came from. During
our first meeting at Starbucks, we enjoy each other's company where we talk about life, school,
traveling, and work experiences. We shared the same goal from the beginning where we all aim to
get good grades from this class through the small group projects. Our first time working together is
when preparing our group adventure presentation. We work very cooperatively, dividing jobs within
one another,

What Is Graph Theoretical Analysis
1.3.4. Introduction to graph theoretical analysis. The case of brain perfusion SPECT.
In the field of quantitative neuroimaging, graph theoretical analysis is one of the methods to study
brain connectivity [93–95, 100, 101]. A key concept of this method is the notion of topology. This
concept can be illustrated with a simple idea which is used when we travel in the subway of any
large city. In figure 3 two maps of the London subway appear, the first map shows a precise spatial
description of the railways (or lines) through which trains travel (i.e., the subway topography),
whereas the second one is only concerned with the relative locations of subway stations and
connecting lines (i.e., the subway topology). These two maps do not coincide with regards to the
relative position of the stations, neither in the distances nor in the location of the lines. However, the
topological map simplifies the problem for the traveler. For example, two stations may be physically
(topographically) ... Show more content on Helpwriting.net ...
By simply inspecting the topological maps, it is not easy to know if one subway is better organized
than the other (e.g., subway efficiency). One way to simplify this problem is to use metrics that
quantify or analyze the subway network using graph theory [94, 101]. Hence the name of graph
theoretical analysis.
A first aspect to measure could be how easy it is to travel between any two stations (e.g. the number
of stations on average, between the start and end of the trip). This aspect is relevant, especially if the
traveler wants to visit different parts of the city on the same day. This example illustrates the
concept of global efficiency of a graph [94, 101]. The metric of global efficiency is a way to
quantify the global connectivity (integration) of the network. In this example, it is assumed that the
number of stations (nodes) and lines (connectors) are the same in the two subways that are

Distribution Of Minimum Spanning Trees
Distributed Veriﬁcation of Minimum Spanning Trees
Authors: Amos Korman, Shay Kutten
Problem Statement: A graph and a tree is given as input in a distributed manner and the algorithm is
should be able to verifiy if the tree is a Minimum Spanning Tree (MST).
Detailed Explanation of the Problem Statement
Definitions
Spanning tree: A spanning tree of an undirected graph is a subgraph which is a tree and includes all
the vertices of G.
Minimum Spanning Tree (MST) : A spanning tree of a graph whose weight (sum of weight of its
edges) is less than or equal to the weight of all other spanning trees of the graph.
Verification of a Spanning Tree: A graph and a tree is given as input and the algorithm should check
if the tree is an MST for the graph.
In distributed verification of Minimum Spanning Trees, the input is provided in a distributed manner
which means that each node of the graph knows which of its edges belong to the tree. A node does
not have any knowledge of the edges which do not emanate from it. The verification algorithm
should label the vertices of the graph so that every node, given its own label and the labels of its
neighbours only is able to detect if these edges are MST edges or not and whether the input tree is
an MST.
Motivation
The motivation for working on verification algorithms is that verification is easier than computation.
In a distributed setting verification is even more important because the tree is given in a distributed
manner and computing such a

Application Of A Distributed Agricultural Sensor Network
Abstract– As the world's population continues to grow, new agricultural technologies will be needed
to keep up with the growing demand. To accomplish this, agricultural systems must increase
production while at the same time more effectively utilizing and conserving the resources that go
into that production. One possible solution is the collection and analysis of environmental data. IoT
sensor networks can cheaply provide distributed data that can be used to improve plant health,
increase harvest yields, and decrease waste. The primary focus of this project will be to prototype a
distributed agricultural sensor network that will be able to monitor the environment, network and
analyze the data on the cloud, and finally provide feedback to ... Show more content on
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This is an active area of IoT research and will be the primary focus of this project. If data can be
effectively gathered, networked, and analyzed, then that data can then be used to impart real and
significant change on the environment. For agricultural systems this will result in improved plant
health, increased harvest yields, and decreased waste. A. Background One of the most utilized and
standardized IoT stacks is the IEEE 802.11.4, 6LoWPAN, RPL, and CoAP stack. The IoT stack
layers are similar to OSI and they share many of the same abstraction layer divisions. These
similarities assist with the interoperability of IoT protocols with those of the Internet. The
IEEE802.15.4 protocol defines the physical and link layers of stack. These layers define how data is
modulated onto the channel, how the channel is shared between multiple users, as well as the low–
level packet checks and retransmissions. IEEE802.15.4 operates in the 2.4GHz ISM band using
Quadrature Phase–Shift Keying with additional Direct Sequence Spread Spectrum encoding. This
allows the radio to better reject noise and other interference. IEEE802.15.4 utilizes 16, 5MHz
channels in order to generate a data throughput of 250kbps. The maximum payload size is 127 Bytes
[1]. Compared to WiFi, the data rate and maximum packet size is significantly less. These
characteristics are the result of the limited hardware used for

Nt3110 Unit 1 Algorithm Application Paper
section{Design Procedure}
label{Design}
We divide the system into four main parts as follows.
begin {itemize}
item [1.] Modularize. item [2.] Evaluation. item [3.] Estimation. item [4.] Testing end{itemize}
We represent this fact graphically in the following figure ref{Figure:Phase}. Each part of the figure
describes briefly.
begin{figure}[htp] includegraphics[width=.48textwidth]{figure/arc2.eps} caption{Architecture
of design procedure. } label{Figure:Phase}
end{figure}
subsection{Modularize:}
Social networks are growing day by day. For modular representation of Graph $G(V,E)$ first phase
of the design issue is to modularize the network having border nodescite{newman2006modularity}.
Boarder nodes ... Show more content on Helpwriting.net ...
Then users of these groups are recommended .We use an effective technique of identify the best user
to be recommended. When we are in the distance based group then apply probability based function
and gets the user with high concentration of communication. For example, we need two users but as
many as fifty users have same distance from the recommender. Then we use the probability function
and set a threshold value 2 this will identify the best two users for the best solution. Again if we are
in the probability based group then calculate shortest distance among the users who have same
probability value. For above example, assume 130 nodes have same probability (suppose 0.9) then
run BFS for these nodes (130) the users having shortest distance from the user are recommended.
Though our approach is to work efficiently and succeed to produce a result as much effective as we
want, we have same

M14056009 Key Questions
1. 7.5/8
The height in metres of a ball dropped from the top of the CN Tower is given by h(t)= –4.9t2+450,
where t is time elapsed in seconds.
(a) Draw the graph of h with respect to time
(b) Find the average velocity for the first 2 seconds after the ball was dropped h(0)=(0,450), h(2)=
(2,430.4)
= (430.4–450)/(2–0)
= –9.8m/s √
(c) Find the average velocity for the following time intervals
(1) 1 ≤ t ≤ 4 h(1)=(1,445.1) h(4)=(4,371.6)
= (371.6–445.1)/(4–1)
= –24.5m/s √
(2) 1 ≤ t ≤ 2 h(1)=(1,445.1) h(2)=(2,430.4)
= (430.4–445.1)/(2–1)
= –14.7m/s √
(3) 1 ≤ t ≤ 1.5 h(1)=(1,445.1) h(1.5)=(1.5, 438.98)
= (438.98–445.1)/(1.5–1)
= –12.25m/s √
(d) Use the secant method to approximate the instantaneous velocity at t=1 h(0.5) = (0.5, 448.78) ...
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H(2)= –5(2)2+20(2)+1 = –20+40+1 =21 H(2+h)= –5(2+h)2 + 20(2+h) +1 = –5(4+4h+h2) +
40+20h+1 = –20–20h–5h2+40+20h+1= –5h2+21 lim(h–>0) H(2+h)–H(2)/h = –5h2+21–21/h = –5h
= –5(0) = 0 √
(b) A particle's motion is described by the equation d=t2–8t+15 where d and t are measured in
metres and seconds. Show that the particle is at rest when t = 4.
D(4)= (4)2–8(4)+15 =16–32+15= –1 D(4+h)= (4+h)2–8(4+h)+15= 16+8h+h2–32–8h+15= h2–1
limD(4+h)–D(4)/h = h2–1+1/h = h =0 √
6. 4/4
For the following graph
(a) Determine the intervals between which the rate of change is positive and negative. The function
is increasing at x<–1 and x>1, hence its rate of change is positive. The function is decreasing at –
1<x<1, hence its rate of change is negative. √

(b) State where the rate of change is zero. The instantaneous rate of change is zero at x=–1 and x=1
√
(c) List the local maximums and minimums of the function. F(–1) is a local maximum and F(1) is a
local minimum. √
7. 10/10
For the function f(x) =2x3–7x2+4x+1 (a) Find the instantaneous rate of change at x=0 and x=1 (1)
F(0+h) = 2(h)3 – 7(h)2+4(h)+1 =2h3–7h2+4h+1 F(0) = 1 lim(h–>0) (F(0+h)–F(0))/h = (2h3–
7h2+4h+1–1)/h = (2h3–7h2+4h)/h = 2h2–7h+4 = 4 ∴the instantaneous rate of change at x=0 is 4 √

Connectivity And Related Survivability Issues Of Wsns
In the previous section, we mainly focus on the connectivity and related survivability issues of
WSNs. It is the foundation that we deploy WSNs to achieve its main objective which is to monitor
the field of interest / detect desired data and it is coverage that determines whether the field of
interest is under strict surveillance or not. So, in this section, we will summarize the related work on
integrated connectivity and coverage problem in WSN. In [68], [69], it's clear that connectivity only
requires that the location of any active node be within the communication range of one or more
active nodes such that all active nodes can form a connected communication backbone, while
coverage requires all locations in the coverage region be ... Show more content on Helpwriting.net
...
The objective of the research efforts on relationship between coverage and connectivity is to utilize
the minimum number of sensor nodes to achieve required coverage degree while maintaining
desired system connectivity.
In the following parts of this section, we will firstly summarize the related work on analyzing the
relationship between the sensing range Rs and communication range Rc. Then, we demonstrate the
research efforts on finding critical conditions for achieving connectivity and coverage. Finally, we
survey the approaches on connectivity and coverage in WSNs.
Critical condition for 1–coverage to achieve 1–connectivity:
In [68], [69], authors first time proved the sufficient condition for 1–coverage imply to 1–
connectivity: "for a set of sensors that at least 1–cover a convex region A, the communication graph
is connected if Rc≥ 2Rs". Based on this result, when we design a WSN system, we can focus on
node deployment strategy and elimiate the connectivity problem by assuming the Rc≥ 2Rs. Zhang
and Hou in [70] present a distributed Optimal Geographical Density Control (OGDC) scheme that
considers the integrated combine coverage and connectivity problem. The objective of this work is
to minimize the number of active nodes in the WSN. Similar to [68], [69], the authors also proved
that coverage implies connectivity when Rc≥ 2Rs. In OGDC, the nodes can automatically

The Provider And Patient Attributes
Provider and Patient Attributes
We collected a set of attributes for providers and patients from the EDW. For providers, we
extracted employee ID, employee role, and a physician index (a Boolean value; 1 if provider is a
type of physician, 0 if not). For patients, we extracted age, encounter type (all inpatient in this data
set), admission and discharge times, primary diagnosis, discharge location, length of stay, med
service (department to which the patient was admitted), discharge disposition (where the patient was
discharged to), and an index noting whether or not the patient had expired.
Network Visualization
We created two types of networks in this study. The first is a directed bipartite network and
represents interactions between providers and patient records (Figure 2). The second network is
undirected and depicts shared patient record access between providers (Figure 3). Visualization for
both networks was performed using Gephi. [59] Further description of these networks follows.
Provider–patient Network
EDW data indicating provider access to a patients EHR was depicted as a directed bipartite graph
(see Figure 2 for a one–patient example). The source node designates a provider of type physician,
nurse, pharmacist, etc. The target node designates a patient with a diagnosis of heart failure who was
admitted to NMH in 2012. An edge between them is an indication that the provider has accessed the
patient record. The complete bipartite graph included all providers as

The Edge Geodetic Domination Number
ABSTRACT In this paper the concept of upper edge geodetic domination number (UEGD number)
and upper connected edge geodetic domination number (UCEGD number) of a graph is studied. An
edge geodetic domination set (EGD set) S in a connected graph is minimal EGD set if no proper
subset of S is an edge geodetic domination set. The maximum cardinality of all the minimal edge
geodetic domination set is called UEGD number. An EGD set S in a connected graph is minimal
CEGD set if no proper subset of S is a CEGD set. The maximum cardinality of all the minimal
connected edge geodetic domination set is called UCEGD number. Here the UEGD number and
UCEGD number of certain graphs are identified. Also for two positive integers p and q there exist
some connected graph with EGD number p and UEGD number q. Similarly for two positive
integers p and q there exist some connected graph with CEGD number p and UCEGD number q.
Keywords
Geodetic domination number, edge geodetic domination number, upper edge geodetic domination
number, upper connected edge geodetic domination number.
AMS subject Classification: 05C12, 05C05
1 INTRODUCTION
By a graph G = (V, E) we consider a finite undirected graph without loops or multiple edges. The
order and size of a graph are denoted by p and q respectively. For the basic graph theoretic notations
and terminology we refer to Buckley and Harary [4].For vertices u and v in a connected graph G,
the distance d(u, v) is the length of a shortest uv path in G. A

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