# Appendix a to applied probability: directory of m-functions and m  (Page 23/24)

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mgd.m Uses coefficients for the generating function for N and the distribution for simple Y to calculate the distribution for the compound demand.

% MGD file mgd.m Moment generating function for compound demand % Version of 5/19/97% Uses m-functions csort, mgsum disp('Do not forget zeros coefficients for missing')disp('powers in the generating function for N') disp(' ')g = input('Enter COEFFICIENTS for gN '); y = input('Enter VALUES for Y ');p = input('Enter PROBABILITIES for Y '); n = length(g); % Initializationa = 0; b = 1;D = a; PD = g(1);for i = 2:n [a,b]= mgsum(y,a,p,b); D = [D a]; PD = [PD b*g(i)]; [D,PD]= csort(D,PD); endr = find(PD>1e-13); D = D(r); % Values with positive probabilityPD = PD(r); % Corresponding probabilities mD = [D; PD]'; % Display details disp('Values are in row matrix D; probabilities are in PD.')disp('To view the distribution, call for mD.')

mgdf.m function [d,pd] = mgdf(pn,y,py) is a function version of mgd , which allows arbitrary naming of the variables. The input matrix $pn$ is the coefficient matrix for the counting random variable generating function. Zeros for the missing powers must be included.The matrices $y,py$ are the actual values and probabilities of the demand random variable.

function [d,pd] = mgdf(pn,y,py)% MGDF [d,pd] = mgdf(pn,y,py) Function version of mgD% Version of 5/19/97 % Uses m-functions mgsum and csort% Do not forget zeros coefficients for missing % powers in the generating function for Nn = length(pn); % Initialization a = 0;b = 1; d = a;pd = pn(1); for i = 2:n[a,b] = mgsum(y,a,py,b);d = [d a];pd = [pd b*pn(i)];[d,pd] = csort(d,pd);end a = find(pd>1e-13); % Location of positive probabilities pd = pd(a); % Positive probabilitiesd = d(a); % D values with positive probability

randbern.m Let S be the number of successes in a random number N of Bernoulli trials, with probability p of success on each trial. The procedure randbern takes as inputs the probability p of success and the distribution matrices $N,PN$ for the counting random variable N and calculates the joint distribution for $\left\{N,S\right\}$ and the marginal distribution for S .

% RANDBERN file randbern.m Random number of Bernoulli trials % Version of 12/19/96; notation modified 5/20/97% Joint and marginal distributions for a random number of Bernoulli trials % N is the number of trials% S is the number of successes p = input('Enter the probability of success ');N = input('Enter VALUES of N '); PN = input('Enter PROBABILITIES for N ');n = length(N); m = max(N);S = 0:m; P = zeros(n,m+1);for i = 1:n P(i,1:N(i)+1) = PN(i)*ibinom(N(i),p,0:N(i));end PS = sum(P);P = rot90(P); disp('Joint distribution N, S, P, and marginal PS')

## Simulation of markov systems

inventory1.m Calculates the transition matrix for an $\left(m,M\right)$ inventory policy. At the end of each period, if the stock is less than a reorder point m , stock is replenished to the level M . Demand in each period is an integer valued random variable Y . Input consists of the parameters $m,\phantom{\rule{0.166667em}{0ex}}M$ and the distribution for Y as a simple random variable (or a discrete approximation).

% INVENTORY1 file inventory1.m Generates P for (m,M) inventory policy % Version of 1/27/97% Data for transition probability calculations % for (m,M) inventory policyM = input('Enter value M of maximum stock '); m = input('Enter value m of reorder point ');Y = input('Enter row vector of demand values '); PY = input('Enter demand probabilities ');states = 0:M; ms = length(states);my = length(Y); % Calculations for determining P[y,s] = meshgrid(Y,states);T = max(0,M-y).*(s<m) + max(0,s-y).*(s>= m); P = zeros(ms,ms);for i = 1:ms [a,b]= meshgrid(T(i,:),states); P(i,:) = PY*(a==b)';end disp('Result is in matrix P')

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s.
what is the Synthesis, properties,and applications of carbon nano chemistry
so some one know about replacing silicon atom with phosphorous in semiconductors device?
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
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What is lattice structure?
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Ebrahim
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s.
Graphene has a hexagonal structure
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I start with an easy one. carbon nanotubes woven into a long filament like a string
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Cesar
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Uday
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Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
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Prasenjit
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
Azam
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Prasenjit
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At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
how did you get the value of 2000N.What calculations are needed to arrive at it
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A fair die is tossed 180 times. Find the probability P that the face 6 will appear between 29 and 32 times inclusive