Category: SCIENCE BLOG

It is first coined by J.Fourier in 1827 is also called atmospheric effect, global warming or carbon dioxide problem. All the IR Radiations do not go to the space. In fact a portion of the radiation re-emitted from earth is not immediately radiated into space rather; it is absorbed by gases such as carbon dioxide that contributes to global warming.
The Green House Effect is a measure of the effect to which this absorption retains heat at the earth surface .Green house means that a building made of glass with heat and humidity regulated by plant.
A Green House is that body which allows the short wavelength incoming solar radiation to come in, but does not allow the long Wavelength outgoing terrestrial Infra Red radiation to escape.
Green House Effect is progressive, warming, up of the Earth’s surface due to blanketing effect of manmade Carbon Di Oxide in the atmosphere.
Green House Effect is the phenomenon due to which the Earth retains heat. Green House Effect means the excessive presence of Carbon Di Oxide, Methane, Nitrous Oxide, Chloro Floro Carbon etc. Blocked in the IR radiation from the Earth surface to the atmosphere leading to an increase in temperature which in turn would make life difficult on earth.
Key Word: – Solar and Infra Red radiations, high and normal Carbon Di Oxide content at atmosphere, short and long Waves, light and heat energy, plants, earths, sun.
Control Measures of Green House Effect:-
Reducing the consumption of fossil fuels.
Depending more on none -conventional renewable source of energy such as solar wind nuclear and biogases energies.
Disposing Green House Carbons as they are formed elsewhere then in the atmosphere .

Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Many sources of data are used in mathematical modeling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important. We stretch existing knowledge on model formulation and develop an enhanced mathematical model to study the pattern of spread of infectious diseases. The SIR model formalism was used to compartmentalize the population and the resulting model equations were solved numerically. The disease free equilibrium and endemic equilibrium of the system were established and analyzed for stability. A graph representation of the sub groups is presented and discussed based on the results from simulation.

In many countries of the developed World, infectious diseases of the childhood in particular have been generally conquered. This however, is far from being the case in developing countries, where they are responsible for 45% of all death. Coming down home, the data of infectious disease prepared by the Department of Health Planning and Research, Federal Ministry of Health at our disposal shows the same trend in Rivers State.
A mathematical model is a set of equations, which are the mathematical translation of hypothesis (or assumptions) when interpreting model prediction. It is thus important to bear in mind the underlying assumptions. An assumption, by our definition, is an unverified proposition, tentatively accepted to explain certain facts or to provide a basis for further investigation.
Following analysis, modeling infectious diseases demands that we investigate whether the disease spread could attain a pandemic level or it could be wiped out. The equilibrium analysis helps to achieve this.