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.mfunction [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
$\{N,S\}$ 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')
inventory1.m Calculates the transition matrix for an
$(m,M)$ 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')
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
Azam
anybody can imagine what will be happen after 100 years from now in nano tech world
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
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
Prasenjit
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
Damian
silver nanoparticles could handle the job?
Damian
not now but maybe in future only AgNP maybe any other nanomaterials
Azam
Hello
Uday
I'm interested in Nanotube
Uday
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
Prasenjit
can nanotechnology change the direction of the face of the world
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.