The Jacobi iteration uses the previous values of the iteration to advance, but Gauss-Seidel uses new component values as soon as they are computed.
To that end, we introduce a measure of the local volatility, as well as a volatility threshold, so that the system changes from a normal dynamics, in which it uses the previous values of the variable to determine its next value, to a situation in which it recalls the past and compares the current state with previous states of high volatility, even if this past is far.
Using the previous L values of a particular pixel value {I t−L, I t−L+1, …, I t−1}, the probability that the next pixel value I t has a value x is estimated as f ( p ) = 1 Lh ∑ i = t − L t − 1 K p − I i L. (17).
The updated trust value is calculated using the previous trust value and impact of current interaction in the form of reward/penalty rate using following equation: T i = T i - 1 + Δ p for I cur = positive interaction T i - 1 - Δ n for I cur = negative interaction.
We show that the best-performing prediction functions are 25% more accurate on average than simply using the previous centrality value.
According to the WHO in developed countries the maximum admissible concentration of As in drinking water is 10 μg/L; however, the developing countries, where arsenicosis is more widespread, are still using the previous guideline value (50 μg/L) due to the lack of facilities to analyze smaller concentrations precisely [ 20].
For periods without reported information, we calculated average abundance using the previous and subsequent values.
The probability of the null hypothesis is calculated using the previous formula and these values of CORIG and CSurrogate.
They estimate the evolutionary state at each step using the previous state and the value of the "evolutionary factor" which is computed using distance between particles.
These values are approximately 20%% lower than the values calculated using the previous methods.
