Smashed Past IBPS Technical Officer Exam in first attempt and that too in the First ever IBPS

Won the coveted " Best Support team " Award & " Runners up Group Discussion Award"

Fascinating Science Facts

I always thought chocolates were harmful but according to this newsmail article i found it was beneficial ------

The Oxygen Radical Absorbance Capacity or ORAC is a scientific measurement of the antioxidant capabilities of a food. The higher the ORAC value, the greater its antioxidant properties. In tests, surprisingly chocolate has a greater ORAC value than many healthy foods which are touted as being high in antioxidants. For example, blueberries are one of the healthiest fruits around. Blueberries have an ORAC value of around 8700. Dark chocolate has an ORAC value of 13,000 units. And even milk chocolate, which has only a fraction of the antioxidants of dark chocolate, has an ORAC value of 6,700.
BUT
Just because chocolate can be good for you doesn’t mean that you can eat all that you want. Chocolate has a high fat content, and even though much of it is good fat, too much of any kind of fat is not good for you. In addition, just because chocolate is healthy, doesn’t mean that candy bars or chocolate cake is healthy. Candy bars, in addition to chocolate, have caramel, nuts, nougat, and all kinds of other materials which are not particularly healthy. The same is true for chocolate cake which usually comes with unhealthy frosting – which is essentially butter and sugar.

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APPLES PRODUCED TODAY ARE MOSTLY CLONES..THIS I KNEW BUT I DID NOT HAVE THE EXACT INFO OF QUANTITIES ..I GOT THAT INFO TOO TODAY..

The science of growing apples is called pomology
* The Greeks, Etruscans, and Romans all practiced grafting their favorite apple varieties to other apple rootstocks
* 7,500 varieties of apples are grown throughout the world
* In 2001, Americans ate an average of 45.2 pounds of fresh apples and processed apple products
* The pilgrims planted the first U.S. apple trees in the Massachusetts Bay Colony
* The largest apple ever picked weighed three pounds
* Actress Gwyneth Paltrow named her baby daughter Apple

Fascinating Facts

hoW LONG DOES IT TAKE TO DECOMPOSE

Banana Peel- 3-4 weeks
Orange peels- 6 months
Apple Core- 2 months
Paper Bag- 1 month
Cardboard- 2 months
Milk Cartons- 5 years
Newspaper- 6 weeks
Paper Towel- 2-4 weeks
Cotton Glove- 3 months
Tinned Steel Can- 50 years
Aluminum Can- 200-500 years
Disposable Diapers- 550 years
Plastic Bags- 20-1000 years
Glass- 1-2 million years
Cigarette Butts- 10-12 years
Leather shoes- 25-40 years
Rubber-Boot Sole- 50-80 years
Plastic containers- 50-80 years
Monofilament Fishing Line- 600 years
Foamed Plastic Cups- 50 years
Wool Sock- 1-5 years
Plywood- 1-3 years
Plastic Bottles- 450 years


Previously I used to think that this was not true !!! And that it doesnot bother me to think about this but then when i came across this i thought well it is great to know about it as it is not just fascinating but also enlightning for us as we should use these wisely as later it might cause problems for our future generations.

Best Inventions so far ... ( Some thing that all engineers should always rever)

how it was done before and approx times----

The values of Pi through time

Person/People Year Value Babylonians ~2000 B.C. 3 1/8 Egyptians ~2000 B.C. (16/9)^2= 3.1605 Chinese ~1200 B.C. 3 Old Testament ~550 B.C. 3 Archimedes ~300 B.C. proves 3 10/71uses 211875/67441=3.14163 Ptolemy ~200 A.D. 377/120=3.14166... Chung Huing ~300 A.D. sqrt(10)=3.16... Wang Fau 263 A.D. 157/50=3.14 Tsu Chung-Chi ~500 A.D. proves 3.1415926 Aryabhatta ~500 3.1416 Brahmagupta ~600 sqrt(10) Fibonacci 1220 3.141818 Ludolph van Ceulen 1596 Calculates Pi to 35 decimal places Machin 1706 100 decimal places Lambert 1766 Proves Pi is irrational Richter 1855 500 decimal places Lindeman 1882 Proves Pi is transcendental Ferguson 1947 808 decimal places Pegasus Computer 1957 7,840 decimal places IBM 7090 1961 100,000 decimal places CDC 6600 1967 500,000 decimal places

A SOAP EXPERIMENT dated 11/5/2005

well it was just a trial ---- i was taking bath and suddenly i felt why donot i try to find a locus of the soap..

well actually i was reading in class 12 then i was constantly grilled for not being able to find out appropriate locus of points in mathematics .It was simple though but whenever i used to think deep of the various possibilities that are possibly i used to find myself losing track .......

locus formula told to us was simple take any point(h,k) and from the geometrical condition given try to find the expression relating h,k.

Now at around 10:00 am i began this nonsense of experiment as u may call it ---

i first wrote the following equation initial energy=final energy of a body with all other factors(physical remaining constant).
now weight of soap was 50 grams
and i weight using the spring balance my parents brought me.
I placed the soap on water and was expecting it to move..

OBservation
the soap simply curved here and there and settled down at the bottom of the container.

I was inquisitive for i felt that given the fact that soaps weight must have been more than the weight of the equivalent amount of water that it replaced(equal to its volume)


Next phase 10:15(15 minutes gone in bringing the copy to note observation and also to write the first ever equation of mine on practical implementations..)

but then when i took the container out in the sun i thought now that sun rays are falling some energy would be given to the water molecules and it would show some zigzag movement.

observation
no such movement observed .my shoulder s dropped a bit because i have been knowing that once energy is supplied that is some work is done every object would show some physical or chemical response to it.
because energy conservation has to be followed always..

at about 11:50 am(when sun was almost near the top of the sky)i saw a slight movement in the soap ... it looked as if it had moved a little...
i was i thought maybe due to the molecules of water getting heated up by the sun energy..

now at this point i had three basic question s in my mind ------

first i could not understand why did the soap move was it the wind blowing around which moved the upper molecules of water ?
but then had that been the case then it wont have been like that because if wind was the factor that moved the upper water molecules then why did not the soap keep on moving after sometime when i was feeling some breeze blowing ... the coconut trees leaves were moving.

second i could not understand why did the sun s rays which were falling on soap too were not being able to move it at the very first?

well the answer to this that i was contemplating was that may be because constituent of soap is na2co3 and that of water is h2o have a different bond structure and electronegativity difference of sodium and carbon and carbon oxygen bond electronegativity diff may have had a stronger dipole moment then h2o and so it would have been difficult to force a movement in soap because the surrounding water molecules were not stirred up to the same extent so resistence was provided. later when all the surrounding molecules of water which was in bulk so needed more time to heat up was hot enough the soap had less resistance as
t directly proportional to 1/resistance...

now coming back to finding locus i took one corner of soap in the media (h,k)
and taking the moment about the other nearest corner i was seeking to get to a relationship between hand k in both normal conditions and also in heated condition
i took water exerted thrust to be w N
and was acting uniformly on soap all over.

A note on Cybernetics and Artificial intelligence by Uddalak Banerjee , NIT Raipur , CSE

Definition:Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it.It can also be defined as the field as "the study and design of intelligent agents ,"where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. John Mcarthy coined the term in 1956 defined it as AI .
Problems of AI:As is obvious while we stand to implement the aforesaid model by which or on whose a machine would be able to equate an human in intelligence ,problems are obvious .The most prominent of them include:
1.Deduction, reasoning, problem solving
2.Knowledge representation
a.Default reasoning and the qualification problem.
b.The breadth of commonsense knowledge
3.Planning
4.Learning
5.perception

6.creativity

Cybernetics and brain simulation--The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated.In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.

Uses of AI:
1.AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics
2.Brandeis University researchers Hod Lipson and Jordan Pollack have developed a computer that can build robots
3.In producing advanced computer games
4.In performing complex calculations scientific and nonscientific
5.In performing all the day to day jobs that a human has to perform.

TESTING OF AI:--- In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.
The broad classes of outcome for an AI test are:

1. optimal: it is not possible to perform better
2. strong super-human: performs better than all humans
3. super-human: performs better than most humans
4. sub-human: performs worse than most humans
#The Loebner Prize for artificial intelligence ( AI ) is the first formal instantiation of a Turing test. The test is named after Alan Turing the brilliant British mathematician
A simple illustration of complexity of the procedure:
How complex could agent-based AI really get? While dealing with 32,000 rows of data culled from a batch of over 400,000 items taken from multiple systems, through multiple processes and multiple filters.
Let’s start with a simple hypothetical:

• 1 agent with 5 states
• 5 states with 1 transition each = 5 transitions (never mind that…)
• 5 states with 1 transition to each of the other 4 states = 5 * 4 = 20 transitions?
• 5 interacting agents, each with 5 states = 3125 combinations of the agents’ states
• 3125 agent-state combinations * 4 potential transitions for each of 5 agents (20) = 62500 potential individual transitions at any given moment.

At an average of one state transition per agent per second, over a 5 second period, there could be 3125 potential sequences of the 5 states. The combination of sequences over the 5 seconds between all 5 agents is… uh… 3125^5 = 298,023,223,876,953,125
So if we change that one parameter for that one transition threshold for that one agent by 0.5%, it’s only a small change, right? If we wanted to test the ramifications of that parameter and how the 5 agents interact over time we would only have to test… how many situations? 298 * 10^15? You know what? Never mind. My 32,000 rows of simple data starts to look attractive.
concept of neural networks:
Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes.
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
2. Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.

An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
Implementation of AI:
AI paradigms and techniques have, by and large, been developed under the influence of sequential computational models targeted at the von Neumann processor. This has, of necessity, determined the common languages and data structures employed in AI-related problem-solving. An exception is the cognitive modelling wing of AI which has consistently looked forward to a connectionist, or so-called parallel distributed processing, environment. But here again, AI researchers have usually been obliged to simulate their networks on sequential processors.. Now that there exists the technology to implement affordable parallelism, designers of novel hardware should seek to identify AI primitives at the level of declarative and representational formalisms, rather than at the level of (sequential) programming languages and data structures. The potential gain in taking the higher view is that not only are execution speeds improved but software becomes less complex
It involves:

1.The Inference Engine (Hardware Parser)
2. The Unification Mechanism (Attribute Evaluator)
a sample code --------------------------------------------------------------------------------------------------------------------------------

//This class represents a node which will contain an arraylist of cards plus pointers to parent and child nodes.


//The root node will be a special node, It will not have any parents
//This program illustrates the easiest procedure to implement AI by java

import java.util.ArrayList;

public class Node
{

//Object Attributes


//Declaring instance variables.
int nodeID;
int depth;
Card nodeCard;
Node parent;
ArrayList children;

public Node(Card rootCard, ArrayList rootChildren) //constructor for the root node
{


 nodeID = 1;
 depth = 1;
 nodeCard = rootCard;
 parent = null;
 children = rootChildren;
}

public Node(int nodeid, int deppth, Card nodeCard, Node Parent, ...) //constructor for the other, children nodes


{

}

public Card getData()
{
 return Card;
}

public int getDepth()
{
 return depth;
}
            ...
}