There are N individuals in a population, some of whomhave a certai.pdf
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There are N individuals in a population, some of whom have a certain infection that spreads as follows. Contacts between two members of this population occur in accordance with a Poisson process having rate λ. When a contact occurs it is equally likely to involve any of the (N choose 2) pairs of individuals in the population. If a contact involves an infected and a noninfected individual, then the noninfected individual becomes infected with probability p. Once infected, and individual remains infected throughout. Let {X (t)} denote the number of infected members of the population at time t. i) {X (t)} is a pure birth process. Find the birth rates λi for each state i, and write down the Q matrix. ii) Starting with a single infected individual, ï¬nd an expression for the expected time until all individuals are infected. iii) Suppose now that each infected individual loses his or her infection independently at rate µ. (Or you can imagine that each individual once becoming infected, remains so for an exponential distributed length of time, independently of other infected members.) Assume that individuals do not gain immunity. Is this a birth and death chain, and if so, what are the birth and death rates? Solution www.tdx.cat/bitstream/10803/31943/2/tasc.pdf.txt?.
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I appreciate any answer. Thank you. For each of the following descri.pdf
I appreciate any answer. Thank you. For each of the following description!) formulate a
mathematical model as a system of differential equations. In each case give a suitable
compartment diagram and define any parameters or symbols that you introduce that were not
mentioned as part of the question. Consider a model for the spread of a disease where lifelong
immunity is attained after catching the disease. The susceptibles are continuously vaccinated at a
per-capita rate mu against the disease. Develop differential equations for the number of
susceptibles S(t). the number of infectives I(t). the number vaccinated V(t) and the number
recovered. R(t). assuming all who recovered from the infection become immune for life. The
infectious disease Dengue fever is spread by infected mosquitoes that transmit the disease to
humans when they bite susceptible humans. Similarly, when a susceptible mosquito bites an
infected human the mosquito becomes infected Assume that humans cannot directly infect other
humans nor infected mosquitoes infect other mosquitoes. Let Sm(t) and Im(t) denote the number
of susceptible and infected mosquitoes respectively and let S h(t) and I h(t) denote the member of
susceptible and infected humans. Assume that there are no birth or deaths of humans over the
time-scale of interest but that there is a per-capita birth-rate of mosquitoes 6 and per-capita death
rate of mosquitoes of a. Also, assume that humans are infected for a period of gamma-1 and then
recover with immunity. Mosquitoes do not recover. (Note: typically gamma-1 is about. 2 weeks
for dengue fever).
Solution
The number of Vaccinated people (V(t)) is a simple one.
dV/dt = mu * S(t), where S(t) is the number of susceptable people.
A certain percentage of the people who need to be vaccinated are vaccinated every year, and this
will slow down as less people need to be vaccinated.
Susceptable people are decreasing over time, once you have been vaccinated, become sick, or
recover you are set for life.
S(t) = 1 - V(t) - R(t) - I(t)
The growth of the infective population depends on how infective the disease is, but it is also
dependant on how many people can be infected still. Let\'s say every infective person infects
beta percent of other vulnerable people, and they recover x days later.
dI/dt = [beta * I(t) * S(t)] - I(t-x)
The number of recovered people will follow the number of infective people but unlike infective
people they remain recovered
dR/dt = I(t-x)
All of these equations use a total population size of one, you are modelling the percentage of the
population that has fallen under these conditions. Eventually, the equations should reach these
final end conditions when t = infinity:
S(t) = 0, V(t) + R(t) = 1, and I(t) = 0.
Part two is just using these equations and applying them..
Modelo matemático: el confinamiento disminuye significativamente la velocidad...
Los investigadores Mikel Casares (UPNA) y Hashmat Khan (Universidad de Carleton, Canadá) estudian la evolución de la epidemia y las consecuencias de las medidas de aislamiento para la población española
Modelisation of Ebola Hemoragic Fever propagation in a modern city
The study of epidemic disease has always been a topic where biological issues mix with social ones.
The aim of this presentation was to modelize in Python language the propagation of Ebola Hemoragic Fever in a modern city thus using SIR model based on Ordinary Differential Equations system and also to produce an amazing Cellular Automaton.
Sensitivity Analysis of the Dynamical Spread of Ebola Virus Disease
The deterministic epidemiological model of (S, E, Iu, Id, R) were studied to gain insight into the dynamical spread of Ebola virus disease. Local and global stability of the model are explored for disease-free and endemic equilibria. Sensitivity analysis is performed on basic reproduction number to check the importance of each parameter on the transmission of Ebola disease. Positivity solution is analyzed for mathematical and epidemiological posedness of the model. Numerical simulation was analyzed by MAPLE 18 software using embedded Runge-Kutta method of order (4) which shows the parameter that has high impact in the spread of the disease spread of Ebola virus disease.
Background- Suppose we want to model the spread of an infectious disea.pdf
Background: Suppose we want to model the spread of an infectious disease through a population.
In the previous assignment, we did this with a discrete model. Here, we'll formulate things as a
continuous model. We'll keep track of the numbers of individuals susceptible to the disease S ( t
) , infected by the disease I ( t ) , and recovered from the disease R ( t ) at time t (measured in
days). Infected individuals have a chance to transmit the disease upon contact with a susceptible
individual at a rate > 0 . Infected individuals also recover from the infection at a rate > 0 . We
assume that all N individuals in the population produce offspring at a rate b > 0 and these
newborns add to the pool of susceptible individuals. Finally, individuals in all disease states can
die due to natural causes at a rate equal to the birth rate b . This description can be captured by
the following system of equations: d t d S = b N S I b S d t d I = S I I b I d t d R = I b R Problems
Problem 1 [3 points]: Given that S , I , and R are measured in number of people and t is
measured in days, what must be the units of b , , and ? Justify your answer. Problem 2 [3 points]:
Let N ( t ) = S ( t ) + I ( t ) + R ( t ) be the total population size at any point in time t . If the total
population size remains constant for all time (i.e., N ( t ) = N for all t , where N is a constant) we
say we have a "closed population". Show that the population in our model is closed. (Hint: Think
about the rate of change d t d N of the population size and what must be true for the population
to be constant.).
Recommended
I appreciate any answer. Thank you. For each of the following descri.pdf
I appreciate any answer. Thank you. For each of the following description!) formulate a
mathematical model as a system of differential equations. In each case give a suitable
compartment diagram and define any parameters or symbols that you introduce that were not
mentioned as part of the question. Consider a model for the spread of a disease where lifelong
immunity is attained after catching the disease. The susceptibles are continuously vaccinated at a
per-capita rate mu against the disease. Develop differential equations for the number of
susceptibles S(t). the number of infectives I(t). the number vaccinated V(t) and the number
recovered. R(t). assuming all who recovered from the infection become immune for life. The
infectious disease Dengue fever is spread by infected mosquitoes that transmit the disease to
humans when they bite susceptible humans. Similarly, when a susceptible mosquito bites an
infected human the mosquito becomes infected Assume that humans cannot directly infect other
humans nor infected mosquitoes infect other mosquitoes. Let Sm(t) and Im(t) denote the number
of susceptible and infected mosquitoes respectively and let S h(t) and I h(t) denote the member of
susceptible and infected humans. Assume that there are no birth or deaths of humans over the
time-scale of interest but that there is a per-capita birth-rate of mosquitoes 6 and per-capita death
rate of mosquitoes of a. Also, assume that humans are infected for a period of gamma-1 and then
recover with immunity. Mosquitoes do not recover. (Note: typically gamma-1 is about. 2 weeks
for dengue fever).
Solution
The number of Vaccinated people (V(t)) is a simple one.
dV/dt = mu * S(t), where S(t) is the number of susceptable people.
A certain percentage of the people who need to be vaccinated are vaccinated every year, and this
will slow down as less people need to be vaccinated.
Susceptable people are decreasing over time, once you have been vaccinated, become sick, or
recover you are set for life.
S(t) = 1 - V(t) - R(t) - I(t)
The growth of the infective population depends on how infective the disease is, but it is also
dependant on how many people can be infected still. Let\'s say every infective person infects
beta percent of other vulnerable people, and they recover x days later.
dI/dt = [beta * I(t) * S(t)] - I(t-x)
The number of recovered people will follow the number of infective people but unlike infective
people they remain recovered
dR/dt = I(t-x)
All of these equations use a total population size of one, you are modelling the percentage of the
population that has fallen under these conditions. Eventually, the equations should reach these
final end conditions when t = infinity:
S(t) = 0, V(t) + R(t) = 1, and I(t) = 0.
Part two is just using these equations and applying them..
Modelo matemático: el confinamiento disminuye significativamente la velocidad...
Los investigadores Mikel Casares (UPNA) y Hashmat Khan (Universidad de Carleton, Canadá) estudian la evolución de la epidemia y las consecuencias de las medidas de aislamiento para la población española
Modelisation of Ebola Hemoragic Fever propagation in a modern city
The study of epidemic disease has always been a topic where biological issues mix with social ones.
The aim of this presentation was to modelize in Python language the propagation of Ebola Hemoragic Fever in a modern city thus using SIR model based on Ordinary Differential Equations system and also to produce an amazing Cellular Automaton.
Sensitivity Analysis of the Dynamical Spread of Ebola Virus Disease
The deterministic epidemiological model of (S, E, Iu, Id, R) were studied to gain insight into the dynamical spread of Ebola virus disease. Local and global stability of the model are explored for disease-free and endemic equilibria. Sensitivity analysis is performed on basic reproduction number to check the importance of each parameter on the transmission of Ebola disease. Positivity solution is analyzed for mathematical and epidemiological posedness of the model. Numerical simulation was analyzed by MAPLE 18 software using embedded Runge-Kutta method of order (4) which shows the parameter that has high impact in the spread of the disease spread of Ebola virus disease.
Background- Suppose we want to model the spread of an infectious disea.pdf
Background: Suppose we want to model the spread of an infectious disease through a population.
In the previous assignment, we did this with a discrete model. Here, we'll formulate things as a
continuous model. We'll keep track of the numbers of individuals susceptible to the disease S ( t
) , infected by the disease I ( t ) , and recovered from the disease R ( t ) at time t (measured in
days). Infected individuals have a chance to transmit the disease upon contact with a susceptible
individual at a rate > 0 . Infected individuals also recover from the infection at a rate > 0 . We
assume that all N individuals in the population produce offspring at a rate b > 0 and these
newborns add to the pool of susceptible individuals. Finally, individuals in all disease states can
die due to natural causes at a rate equal to the birth rate b . This description can be captured by
the following system of equations: d t d S = b N S I b S d t d I = S I I b I d t d R = I b R Problems
Problem 1 [3 points]: Given that S , I , and R are measured in number of people and t is
measured in days, what must be the units of b , , and ? Justify your answer. Problem 2 [3 points]:
Let N ( t ) = S ( t ) + I ( t ) + R ( t ) be the total population size at any point in time t . If the total
population size remains constant for all time (i.e., N ( t ) = N for all t , where N is a constant) we
say we have a "closed population". Show that the population in our model is closed. (Hint: Think
about the rate of change d t d N of the population size and what must be true for the population
to be constant.).
The SIR Model and the 2014 Ebola Virus Disease Outbreak in Guinea, Liberia an...
This research presents a mathematical model aimed at understanding the spread of the 2014 Ebola Virus Disease (EVD) using the standard SIR model. In modelling infectious disease dynamics, it is necessary to investigate whether the disease spread could attain an epidemic level or it could be wiped out. Data from the 2014 Ebola Virus Disease outbreak is used and Guinea where the outbreak started is considered in this study. A three dimensional non-linear differential equation is formulated and solved numerically using the Runge-Kutta 4th order method in the Vensim Personal Learning Edition Software. It is shown from the study that, with public health interventions, the effective reproductive number can be reduced making it possible for the outbreak to die out. It is also shown mathematically that the epidemic can only die out when there are no new infected individuals in the population.
Temporal dynamics of human behavior in social networks (ii)
Second Lecture on Temporal Dynamics given at 2014 Les Houches School on Complex Networks
http://leshouches2014.weebly.com/lecturers.html
Infectious disease modelling - the math behind Corona
See. also live blog at:
https://food4innovations.blog/2020/03/16/live-blog-over-de-corona-crisis-covid-19-dagelijkse-beschouwingen-van-wouter-de-heij-food4innovations/
Mathematical Model for Infection and Removal
The mathematical model of infectious diseases is a tool that has been used to study the mechanisms by which disease spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic. We envisaged a community of n individuals comprising at time t, x, susceptible, y infectious in circulation and z individuals who were isolated, dead, or recovered and immune. We further postulated infection and removal rates ßand , so that there wave ßxydt new infections and ydt removals in time tithe simplest way to do this is to introduce a birth parameter µ, so at to give µdt new susceptible in time dt. If the population is to remain stable the arrival of new susceptible must be balanced by an appropriately defined birth rate. The present paper represents the model in special way, in which the infection occurs in human`s body then the resistance of body gradually decays same as motion decays in damped oscillation. On solving the equation of model, we get solution that gives the idea about the seasonal variation in infection. Shukla Uma Shankar "Mathematical Model for Infection and Removal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38523.pdf Paper Url: https://www.ijtsrd.com/mathemetics/other/38523/mathematical-model-for-infection-and-removal/shukla-uma-shankar
The local and global stability of the disease free equilibrium in a co infect...
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Simulation rules are almost the same to what I coded using Python bu.pdf
Simulation rules are almost the same to what I coded using Python but with one interesting
addition a partial immunity (see details below). The main difference between the Python version
and R-version of the code would be the use of vectorization. You should use as little as possible
of loops. In fact, there should be just one loop for the days-count. If anyone tries to create the
exact copy of my Python code but in R, the grade will be quite low. Below are rules that should
help you building a simulation model:
1. We assume that there are N_population citizens in a population. N_population is an input
parameter. During development and testing you can have it small, but your code should be able
to handle a reasonably large value for population size.
2. Every citizen has a health state healthy, sick, dead (or you can code it as 0, 1, 2). When we
start, all citizens are alive and healthy
3. To start the pandemic, you randomly mark a small number of citizens as sick there can be a
parameter for the number of initially infected citizens. You can start with 1 or 2 initial sick cases.
4. One iteration is one day. During the day, every citizen can meet a random number (say
between 0 and 20 inclusive) of randomly selected citizens. You dont really need to control all
citizens as we are interested in meetings of sick people only. We dont care how many healthy
people meet each other.
5. Every sick citizen can stay sick and infectious for 10 days, hence you should have some
counter for each sick citizen. After 10 days a sick citizen becomes healthy and stops spreading
the virus
6. Obviously, dead citizens cannot become sick, they dont meet anyone and, as a result, cannot
infect anyone.
7. During the day every sick citizen has a probability to die (mortality rate) from the disease with
probability 0.5% (quite low probability).
8. If a sick citizen does not die, then they can meet other people as per the rule 4 above, and if
they meet a healthy person, that person might become sick too and start infecting other people
starting from the next day (that is an infection day is day 0 of their sickness). The probability for
a citizen of becoming sick (infection rate) after a contact is 30% (this is quite high).
9. (New rule!) After surviving the infection and getting healthy, the person becomes immune.
This is a partial immunity. It does not make the person invincible but reduces the chance of
infection in future meetings with sick people. Take the immunity coefficient as 0.1. That is, a
probability of being infected for immune person is ten times lower than for the person without an
immunity. The infection rate for the immune person is the original infection rate multiplied by
the immunity coefficient, e.g. (0.3*0.1).
For the test, you can set immunity coefficient equal 1, which means no benefits of immunity and
the result should be the same as in Python example I presented.
10. You should run this simulation for a number of days (iterations) and store each da.
Jussi parikka-contagion-and-repetition-on-the-viral-logic-of-network-culture-1
jussi parikka contagion and repetition on the viral logic of network culture
populationecology-120221024008-phpapp02.pdf
Population ecology is a field of scientific research that examines the dynamics of populations of living organisms within a given environment. It involves the study of various aspects of populations, including their growth, distribution, density, age structure, and the factors that affect these attributes. Key components of population ecology include:
Population Dynamics: Population ecologists study how the size of a population changes over time. This involves examining birth rates (natality), death rates (mortality), immigration, and emigration.
Population Distribution: Understanding how individuals in a population are spatially distributed is essential. Populations can be clumped, evenly dispersed, or randomly distributed in a habitat.
Population Density: This refers to the number of individuals of a species per unit area or volume of habitat. Population density can have significant ecological and environmental implications.
Age Structure: The age distribution within a population can provide insights into its growth potential and reproductive capacity. It can help in predicting future population trends.
Population Growth Models: Population ecologists use mathematical models to describe and predict population growth, such as exponential and logistic growth models.
Limiting Factors: Population growth is limited by various factors, including availability of resources, predation, competition, disease, and environmental conditions. Population ecologists study how these factors influence population dynamics.
Carrying Capacity: The carrying capacity of an environment is the maximum population size that can be sustained by available resources without causing environmental degradation or resource depletion.
Interactions: Populations do not exist in isolation. Interactions with other species, such as predation, competition, and mutualism, are essential considerations in population ecology.
Conservation and Management: Population ecology plays a critical role in the conservation and management of endangered species and ecosystems. It helps in making informed decisions to protect and sustainably manage populations.
Research Methods: Population ecologists employ various field and laboratory techniques, including population censuses, mark and recapture studies, and modeling, to gather data and understand population dynamics.
Ijetr021115
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Ijetr021115
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
There is quite a bit of literature on the economics of education. On.pdf
There is quite a bit of literature on the economics of education. One of the most debated aspects
of it arises when estimating the achievement of the student (usually in the form of standardized
test scores). Which are the most relevant inputs? Which are not?
A few usual suspects are:
Parent\'s education
Teacher \"quality\", which is by itself an issue. Sometimes it is represented by years of
experience, majors acquired, certification, test scores, etc.
School inputs
Peer effects
Teacher absenteeism
The list goes on...
Solution
There is no solid consensus on the inputs that go into educational achievement or attainment. The
list of variables is vast and varied.
One of the most common applications of this is attempting to calculate the returns to education -
i.e. the average increase in income that results from one additional year of schooling.
I myself have performed research on this topic in grad school, and a good reference is:.
There is a story about Archimedes that he used aSolutionthe ra.pdf
There is a story about Archimedes that he used a
Solution
the rays get concentrated at focus of the mirror.
SO FOCAL LENGTH = 100 M ...TAKING VERTEX AS ORIGIN AND THE X AXIS AS THE
AXIS OF MIRROR ...EQN.IS ...
Y^2= 4AX ...WHERE FOCUS IS AT [100,0]
SO WE GET THE EQN. AS ......Y^2=4*100*X=400*X......ANSWER
HENCE TAKING THE MIRROR UP TO ITS LATUS RECTUM , ITS SIZE WOULD BE ..
400 M ...
THEORETICALLY THIS IS POSSIBLE , BY EMPLOYING NOT ONE BUT SERIES OF
PARABOLIC MIRRORS WHEN TEMPERATURES OF THOUSANDS OF DEGREES
COULD BE ATTAINED.
BUT WHETHER IT IS FEASIBLE UNDER BATTLE CONDITIONS IS OPEN TO
QUESTION.
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The SIR Model and the 2014 Ebola Virus Disease Outbreak in Guinea, Liberia an...
This research presents a mathematical model aimed at understanding the spread of the 2014 Ebola Virus Disease (EVD) using the standard SIR model. In modelling infectious disease dynamics, it is necessary to investigate whether the disease spread could attain an epidemic level or it could be wiped out. Data from the 2014 Ebola Virus Disease outbreak is used and Guinea where the outbreak started is considered in this study. A three dimensional non-linear differential equation is formulated and solved numerically using the Runge-Kutta 4th order method in the Vensim Personal Learning Edition Software. It is shown from the study that, with public health interventions, the effective reproductive number can be reduced making it possible for the outbreak to die out. It is also shown mathematically that the epidemic can only die out when there are no new infected individuals in the population.
Temporal dynamics of human behavior in social networks (ii)
Second Lecture on Temporal Dynamics given at 2014 Les Houches School on Complex Networks
http://leshouches2014.weebly.com/lecturers.html
Infectious disease modelling - the math behind Corona
See. also live blog at:
https://food4innovations.blog/2020/03/16/live-blog-over-de-corona-crisis-covid-19-dagelijkse-beschouwingen-van-wouter-de-heij-food4innovations/
Mathematical Model for Infection and Removal
The mathematical model of infectious diseases is a tool that has been used to study the mechanisms by which disease spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic. We envisaged a community of n individuals comprising at time t, x, susceptible, y infectious in circulation and z individuals who were isolated, dead, or recovered and immune. We further postulated infection and removal rates ßand , so that there wave ßxydt new infections and ydt removals in time tithe simplest way to do this is to introduce a birth parameter µ, so at to give µdt new susceptible in time dt. If the population is to remain stable the arrival of new susceptible must be balanced by an appropriately defined birth rate. The present paper represents the model in special way, in which the infection occurs in human`s body then the resistance of body gradually decays same as motion decays in damped oscillation. On solving the equation of model, we get solution that gives the idea about the seasonal variation in infection. Shukla Uma Shankar "Mathematical Model for Infection and Removal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38523.pdf Paper Url: https://www.ijtsrd.com/mathemetics/other/38523/mathematical-model-for-infection-and-removal/shukla-uma-shankar
The local and global stability of the disease free equilibrium in a co infect...
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Simulation rules are almost the same to what I coded using Python bu.pdf
Simulation rules are almost the same to what I coded using Python but with one interesting
addition a partial immunity (see details below). The main difference between the Python version
and R-version of the code would be the use of vectorization. You should use as little as possible
of loops. In fact, there should be just one loop for the days-count. If anyone tries to create the
exact copy of my Python code but in R, the grade will be quite low. Below are rules that should
help you building a simulation model:
1. We assume that there are N_population citizens in a population. N_population is an input
parameter. During development and testing you can have it small, but your code should be able
to handle a reasonably large value for population size.
2. Every citizen has a health state healthy, sick, dead (or you can code it as 0, 1, 2). When we
start, all citizens are alive and healthy
3. To start the pandemic, you randomly mark a small number of citizens as sick there can be a
parameter for the number of initially infected citizens. You can start with 1 or 2 initial sick cases.
4. One iteration is one day. During the day, every citizen can meet a random number (say
between 0 and 20 inclusive) of randomly selected citizens. You dont really need to control all
citizens as we are interested in meetings of sick people only. We dont care how many healthy
people meet each other.
5. Every sick citizen can stay sick and infectious for 10 days, hence you should have some
counter for each sick citizen. After 10 days a sick citizen becomes healthy and stops spreading
the virus
6. Obviously, dead citizens cannot become sick, they dont meet anyone and, as a result, cannot
infect anyone.
7. During the day every sick citizen has a probability to die (mortality rate) from the disease with
probability 0.5% (quite low probability).
8. If a sick citizen does not die, then they can meet other people as per the rule 4 above, and if
they meet a healthy person, that person might become sick too and start infecting other people
starting from the next day (that is an infection day is day 0 of their sickness). The probability for
a citizen of becoming sick (infection rate) after a contact is 30% (this is quite high).
9. (New rule!) After surviving the infection and getting healthy, the person becomes immune.
This is a partial immunity. It does not make the person invincible but reduces the chance of
infection in future meetings with sick people. Take the immunity coefficient as 0.1. That is, a
probability of being infected for immune person is ten times lower than for the person without an
immunity. The infection rate for the immune person is the original infection rate multiplied by
the immunity coefficient, e.g. (0.3*0.1).
For the test, you can set immunity coefficient equal 1, which means no benefits of immunity and
the result should be the same as in Python example I presented.
10. You should run this simulation for a number of days (iterations) and store each da.
Jussi parikka-contagion-and-repetition-on-the-viral-logic-of-network-culture-1
jussi parikka contagion and repetition on the viral logic of network culture
populationecology-120221024008-phpapp02.pdf
Population ecology is a field of scientific research that examines the dynamics of populations of living organisms within a given environment. It involves the study of various aspects of populations, including their growth, distribution, density, age structure, and the factors that affect these attributes. Key components of population ecology include:
Population Dynamics: Population ecologists study how the size of a population changes over time. This involves examining birth rates (natality), death rates (mortality), immigration, and emigration.
Population Distribution: Understanding how individuals in a population are spatially distributed is essential. Populations can be clumped, evenly dispersed, or randomly distributed in a habitat.
Population Density: This refers to the number of individuals of a species per unit area or volume of habitat. Population density can have significant ecological and environmental implications.
Age Structure: The age distribution within a population can provide insights into its growth potential and reproductive capacity. It can help in predicting future population trends.
Population Growth Models: Population ecologists use mathematical models to describe and predict population growth, such as exponential and logistic growth models.
Limiting Factors: Population growth is limited by various factors, including availability of resources, predation, competition, disease, and environmental conditions. Population ecologists study how these factors influence population dynamics.
Carrying Capacity: The carrying capacity of an environment is the maximum population size that can be sustained by available resources without causing environmental degradation or resource depletion.
Interactions: Populations do not exist in isolation. Interactions with other species, such as predation, competition, and mutualism, are essential considerations in population ecology.
Conservation and Management: Population ecology plays a critical role in the conservation and management of endangered species and ecosystems. It helps in making informed decisions to protect and sustainably manage populations.
Research Methods: Population ecologists employ various field and laboratory techniques, including population censuses, mark and recapture studies, and modeling, to gather data and understand population dynamics.
Ijetr021115
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Ijetr021115
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
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There is quite a bit of literature on the economics of education. On.pdf
There is quite a bit of literature on the economics of education. One of the most debated aspects
of it arises when estimating the achievement of the student (usually in the form of standardized
test scores). Which are the most relevant inputs? Which are not?
A few usual suspects are:
Parent\'s education
Teacher \"quality\", which is by itself an issue. Sometimes it is represented by years of
experience, majors acquired, certification, test scores, etc.
School inputs
Peer effects
Teacher absenteeism
The list goes on...
Solution
There is no solid consensus on the inputs that go into educational achievement or attainment. The
list of variables is vast and varied.
One of the most common applications of this is attempting to calculate the returns to education -
i.e. the average increase in income that results from one additional year of schooling.
I myself have performed research on this topic in grad school, and a good reference is:.
There is a story about Archimedes that he used aSolutionthe ra.pdf
There is a story about Archimedes that he used a
Solution
the rays get concentrated at focus of the mirror.
SO FOCAL LENGTH = 100 M ...TAKING VERTEX AS ORIGIN AND THE X AXIS AS THE
AXIS OF MIRROR ...EQN.IS ...
Y^2= 4AX ...WHERE FOCUS IS AT [100,0]
SO WE GET THE EQN. AS ......Y^2=4*100*X=400*X......ANSWER
HENCE TAKING THE MIRROR UP TO ITS LATUS RECTUM , ITS SIZE WOULD BE ..
400 M ...
THEORETICALLY THIS IS POSSIBLE , BY EMPLOYING NOT ONE BUT SERIES OF
PARABOLIC MIRRORS WHEN TEMPERATURES OF THOUSANDS OF DEGREES
COULD BE ATTAINED.
BUT WHETHER IT IS FEASIBLE UNDER BATTLE CONDITIONS IS OPEN TO
QUESTION.
There is a sine wave with frequency 5.2 Hz. What is its period (unit.pdf
There is a sine wave with frequency 5.2 Hz. What is its period? (unit:s) Tries 0/3 What is the
period of the following sine wave? (unit:s) What is its amplitude?(unit:V) 5.3 s 4.2v
Solution
The Period for 7 cycles =5.3 sec
so the time period =5.3/7=0.76s
Peak to peak amplitude =4.2V
so the amplitude =2.1 V
There is a sine wave with frequency 5.2 Hz. What is its period?
Time period =1/frequency=1/5.2=0.192s.
There is a normal distribution with standard deviation = 1.1. The p.pdf
There is a normal distribution with standard deviation = 1.1. The proportion of values greater
than 6 is 0.065. What is the mean of the normal distribution?
Solution
This means that the z-score for 6 is roughly 1.51. Hence, the mean is 6 - 1.51(1.1)
= 4.34.
There is a jar filled with sixteen colored ping-pong balls. Six are .pdf
There is a jar filled with sixteen colored ping-pong balls. Six are green, four are blue, four are
yellow, and two are purple. Amy reaches into the jar five times and picks five balls. After each
pick, she replaces the ball. What is the probability that she picked exactly three yellow ping-pong
balls? You must show your work.
Solution
probability of yellow balls = 4/16 = 1/4.
probability of other balls = 3/4.
probability = 5C3 * (1/4)^3 * (3/4)^2 = 10*9/1024 = 0.0878.
There is a basic trade-off between efficiency and equity because A. .pdf
There is a basic trade-off between efficiency and equity because A. Income redistribution tends
to reduce incentives for efficient behavior. B. People who are efficient dislike equity. C. Pareto
improvements can only be made by sacrificing efficiency. D. The economy can only achieve a
Pareto efficient allocation by starting from an unequal allocation of resources.
Solution
A. Income redistribution tends to reduce incentives for efficient behavior..
There have been many business cycles in the US over the past 50 year.pdf
There have been many business cycles in the US over the past 50 years (although less so since
the great moderation). Many of us youngsters are too young to remember many of these.
Is there some summary article / plot containing these, including their causes? For example:
1990: Gulf war, drop in oil supply
2000: crash of internet related stocks
2008: crash of mortgages/housing market in the US, amplified through banking sector
Solution
This Time Is Different
by Reinhart and Rogoff
Detailed, structured, and comprehensive. A brief paper by the same authors on the same topic:
This Time is Different: A Panoramic View of Eight Centuries of Financial Crises. The paper\'s
abstract:
This paper offers a \"panoramic\" analysis of the history of financial crises dating from
England\'s fourteenth-century default to the current United States sub-prime financial crisis. Our
study is based on a new dataset that spans all regions. It incorporates a number of important
credit episodes seldom covered in the literature, including for example, defaults and
restructurings in India and China. [...] We find that serial default is a nearly universal
phenomenon as countries struggle to transform themselves from emerging markets to advanced
economies. Major default episodes are typically spaced some years (or decades) apart, creating
an illusion that \"this time is different\" among policymakers and investors. [...] We also confirm
that crises frequently emanate from the financial centers with transmission through interest rate
shocks and commodity price collapses. [...] Our data also documents other crises that often
accompany default: including inflation, exchange rate crashes, banking crises, and currency
debasements..
There has been a new surge of mergers and acquisitions (M and A) in .pdf
There has been a new surge of mergers and acquisitions (M and A) in the last several years. Why
is this? In the past, why have so many not been successful? In your opinion, what are the most
important factors to be taken into account for a successful M and A? Provide an example to
support your position.
Solution
Merger/Consolidation: In a merger, the acquiring firm gets all the assets and liabilities of the
acquired firm, the acquired firm ceases to exist, and the acquiring firm keeps its name and
identity. In a consolidation, the two pre-existing firms cease to exist and become part of a new
business entity.
2. Acquisition of Stock: A tender offer is a public offer made directly to the shareholders of the
target corporation to buy their shares for either cash or securities. These offers are often
contingent on the acquiring company.
There arenunit circles andnlines drawn in the plane. Prove that th.pdf
There arenunit circles andnlines drawn in the plane. Prove that the regions in the plane separated
by the lines and circles can be colored with two colors in such a way that no two adjacent regions
have the same color.
There arenunit circles andnlines drawn in the plane. Prove that the regions in the plane separated
by the lines and circles can be colored with two colors in such a way that no two adjacent regions
have the same color.
Solution
We prove it by induction in the number n of lines. For n = 1 we will have two regions, and we
can color them with just two colors, say one in red and the other one in blue.
Next assume that the regions obtained after dividing the plane with n lines can always be colored
with two colors, red and blue, so that no two regions that share a boundary have the same color.
We need to prove that such kind of coloring is also possible after dividing the plane with n + 1
lines.
So, the plane divided by n lines has been colored in the desired way. After we introduce the (n +
1)th line we need to recolor the plane to make sure that the new coloring still verifies that no
Two regions that share a boundary have the same color. We do it in the following way. The (n +
1)th line divides the plane into two half-planes. We keep intact the colors in all the regions that
lie in one half-plane, and reverse the colors (change red to blue and blue to red) in all the regions
of the other half-plane. So if two regions share a boundary and both lie in the same half-plane,
they will still have different colors. Otherwise, if they share a boundary but are in opposite half-
planes, then they are separated by the (n + 1)th line; which means they were part of the same
region, so had the same color, and must have acquired different colors after recoloring..
There are two trac lights on my way to work. Let X1 be the number .pdf
There are two trac lights on my way to work. Let X1 be the
number of lights at which I must stop on my way to work and let X2 be my way back.both
variables are independent and each has pmf of:
x 0 1 2
p(x) .2 .5 .3
we are also given: µ = 1.1, ^2 = .49
d. let x3 and x4 be the the number of stops at a red light the following day, assuming
independance. T_0 is the sum of all 4. What are the values of E(T_0) and V(T_0)?
e. refering to d - what is the value for p(T_0=8) and P(T_0<=7).
up until now I did everything manually using a table, now the numbers are getting too big for
tables.
Which formula do I implement and how?
Thanks
Solution
d)
E(T_0) = 4* = 4*1.1 = 4.4
V(T_0) = 42*2 = 16*0.49 = 7.84
e)
P(T_0=8) = 0.3*0.3*0.3*0.3 = 0.0081
P(T_0<=7) = 1-P(T_0=8) = 1-0.0081 = 0.9919.
There are four candidates. Two men (Tim and Tom) and two women (Sue .pdf
There are four candidates. Two men (Tim and Tom) and two women (Sue and Mary) applying
for two positions, what is the probability that Mary gets one of the two positions?
Please show work or rationale so that I know how to work similar problems on my exam..Please
Solution
for the first position mary has the probability 0.25 as there are four candidates same
for the second so, total prob is 0.5.
there are twelve balls numbered from 1 to 12placed in a urn. if one .pdf
there are twelve balls numbered from 1 to 12placed in a urn. if one ball is selected at random,
find the probability that it is an odd-numbered ball.
Solution
There are 6 odd balls, 1, 3, 5, 7, 9, 11, and 12 balls total. The probability that an odd
ball is selected is the number of odd balls divided by the number of balls equals 6/12 = 1/2..
There are three methods of converting from a partnership to a corpor.pdf
There are three methods of converting from a partnership to a corporation:
- Contribution by the partnership or two owners LLC of all their assets to a corporation in
exchange for stock (should meet the requirements of Section 351)
- Distribution of the partnership assets to the owners, which they will contribute all those
assets to this newly corporation in exchange for stock.
- Exchange by their interests for stock of a corporation.
Expand in details for the above essay
Solution
The process for converting from an LLC to a corporation will vary depending on multiple
factors.
There are multiple methods for conversion including statutory conversion, statutory mergers and
non -statutory conversions.
1)
Statutory conversion:
It is a new procedure that allows conversion by filing a few forms with the secretary of state’s
office.
Statutory conversion is usually the quickest and most inexpensive way.
2)
Statutory merger:
It is more complicated than statutory conversion.
It involves following steps:
3)
Non -statutory conversion:
It is most complicated and expensive way to conversion..
There are numerous interest groups influencing government action in .pdf
There are numerous interest groups influencing government action in the United States today.
Given your personal situation, interests, activities, and aspirations, list five special interest
groups, which you might consider actively supporting, at least to some degree. For each group
you list, provide an action, or a piece of legislation, which you believe the group should endorse.
Solution
List of five interest groups:
1. Business Most scholars would agree that business plays a central role in American politics.
Major corporations carry the prestige of being important players in the U.S. economy. Because
elected officials are held accountable for the nation\'s economic performance, they often fear
anti-business policies will harm that performance.
2. Labor unions Labor unions grew slowly in the early part of the 20th century, but they gained a
significant place in the American political system in the 1930s. The National Labor Relations
Act protected collective bargaining and enabled unions to grow much faster. They reached a
peak membership of 35 percent of the labor force in the 1950s. However, in the 1960s, union
membership began to decline toward its current level of approximately 15 percent of the working
population, and the political power of unions declined along with their economic power. The
reasons for this decline in union membership, too complex to discuss in detail here, lie in the
changing nature of the global economy, and the shift in the United States from a manufacturing-
based economy to one more service-oriented. Unions, however, still exert considerable clout
when they focus their energies on an election or an issue
3. Professional associations Another important type of interest group is the association of
professionals. Groups like the American Medical Association and the American Bar Association
focus on the collective interests, values, and status of their profession. Less powerful, but
nonetheless well organized, are professionals in the public sector. Virtually every specialty
within state and local governments has its own national organization. In housing policy, for
example, groups include the National Association of Housing and Redevelopment Officials, the
National Council of State Housing Agencies, and the Council of Large Public Housing
Authorities. Such groups are restricted from partisan activities by state and federal laws.
4. Intergovernmental groups A related category consists of interest groups representing units of
state and local government, lobbying for their interests on the national level. While these groups
have no official role in the U.S. federal system that divides authority among national, state, and
local governments, they function much as other interest groups do. That is, they present the
views of their members to Congress and the administration and make the case for their positions
in the media.
5. Public interest groups The type of interest group experiencing the most rapid growth since
1970 .
There are k kinds of postcards, each with a limited amount. Let ther.pdf
There are k kinds of postcards, each with a limited amount. Let there be ai copies of the i-th
postcard. How many ways are there to send these postcards to n friends? (Each friend can
receive zero cards, as well as multiple copies of the same card.)
Solution
no.of ways=(ak+ n-1)C(n-1) ).
There are four defective items in ten inspected items sampled from a.pdf
There are four defective items in ten inspected items sampled from a larger lot. What is the
probability that at most 20%
of the total number of items in the lot are defective?
Solution
phat = 4/10 = 0.4
p(x<20%) = p(z<0.4 - 0.2/sqrt(0.2 * 0.8) ) = p(z<0.5) = 0.6915 = 69.15%.
There are a set of 10 matchsticksthe first is vertical(I), the 2nd.pdf
There are a set of 10 matchsticks
the first is vertical(I), the 2nd/3rd are in a V, the 4th is horizontal ( - ), the 5th/6th are vertical and
parallel to one another (II), the 7th/8th are horizontal and parallel to one another (=), the
remaining 9th/10 are in a V formation.
like this: I V - II = V
How can you move just ONE match to make the statement correct?
Solution
IV+I=V
4+1=5
-I MOVING +.
There are 8 CDs in a rack 5 are jazz and 3 are classic rock. Two CD.pdf
There are 8 CDs in a rack: 5 are jazz and 3 are classic rock. Two CDs are selected at random,
one after the other with replacement, and the type of each is noted. Find the probabilty that the
first is jazz given the last one is jazz.
write answer as a decimal rounded to the fourth places.
Solution
= (5/8 x 4/8) / (5/8 x 4/8 + 3/8 x 5/8 ) = 4/7 = 0.5714.
There are 42 faculty members who make an education committeeA. Th.pdf
There are 42 faculty members who make an education committee:
A. There are five seats labeled chair, vice chair, secretary, treasurer, and member. How many
committees can be formed?
B. there are five seats and no particular order to the members that need to serve on the
committee. How many committees can be formed?
Please show work ! Thanks!
Solution
A) 42P5 = 42! / (42 - 5)! = 42! / 37! = 38*39*40*41*42
B) 42C5 = 42! / 37! * 5! = 38*39*40*41*42 / (2*3*4*5).
There are 50 applicants for a job. Ten are members of ethic group A,.pdf
There are 50 applicants for a job. Ten are members of ethic group A, 15 of ethnic group B, and
25 of ethic group C. The numbers of females (F) in the three groups are 2, 9, and 15,
respectively. A person is selected at random to fill the job. Find the probability that this person
will be A and F
Solution
P(A and F) = (10/50)*(2/10) = 1/25.
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