Answer:
The speed at which the reactants change to products over a given time.
Explanation:
A chemical's <u>reaction rate</u><u> is the change in the concentration of a reactant or a product with time (in moles per second)</u>.
Remember that during a chemical reaction, reactants are converted to products. Or what is the same, products are formed at the expense of reactants. This can be represented:
reactants → products
Therefore,<u> the progress of a reaction can be followed measuring the decrease in concentration of the reactants or the increase in concentration of the products.</u>
According to the temperature and other parameters, the reaction rate can increase or decrease.
Answer:
Although humans can sometimes influence natural disasters (for example when poor levee design results in a flood), other disasters that are directly generated by humans, such as oil and toxic material spills, pollution, massive automobile or train wrecks, airplane crashes, and human induced explosions, are considered.
Answer:
During the Krebs cycle, pyruvic acid is broken down into carbon dioxide in a series of reactions that give off energy. The high-energy electrons that are produced are picked up by a series of electron carriers, and the energy is used to convert ADP into ATP.
Explanation:
A model for a company's revenue from selling a software package is R(p)=-2.5p² + 400p, where p is the price in dollars of the software. What price will maximize revenue? Find the maximum revenue.
Answer: p = $80, R = $16,000
Step-by-step explanation:
The maximum is the y-value of the Vertex.
Step 1: Use the Axis-Of-Symmetry (AOS) formula to find x:
x=
R(p) = -2.5p² + 400
a= -2.5 b=400
= 
=80
∴ In order to maximize the value, the company will sell the software package for $80
Step 2: Find the maximum by plugging the p-value (above) into the given equation.
R(80) = -2.5(80)² + 400(80)
= -16,000 + 32,000
= 16,000
Answer:
The exponential growth model shows a characteristic curve which is J-shaped while the logistic grown model shows a characteristic curve which is S-shaped.
The exponential growth model is applicable to any population which doesn’t have a limit for growth. The logistic growth model is applicable to any population which comes to a carrying capacity.
The exponential growth model typically results in an explosion of the population. The logistic growth model results in a relatively constant rate of population growth. This happens when the growth rate of the population arrives at its carrying capacity.
Exponential growth is ideal for populations that have unlimited resources and space – such as bacterial cultures. Logistic growth is more realistic and can be applied to different populations which exist in the planet.
The exponential growth model doesn’t have any upper limit. The logistic growth model has and upper limit, which is the carrying capacity.
Exponential growth happens when the rate of growth is in proportion to the existing amounts. This is also true for logistic growth but the difference is, it also includes competition and resources which are limited.
