The first major inefficiency is the time-lag problem for distributing scientific ideas.
Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.
This time-lag problem is very difficult to solve and is intrinsic to longitudinal research in general.
We propose a spatio-temporal lumping of radar rainfall for modeling inflow forecasts to mitigate time-lag problems and improve forecasting accuracy.
The time lag may present a problem if the Justice Department wants to prosecute any individuals for bribery of Mexican officials.
First, it substitutes values of explanatory variables with one lag, using time lag to overcome the problem of reciprocal causation.
This time lag isn't a problem for wild plants, for which survival and reproduction are paramount, but it's a disadvantage for farmers who want to maximize biomass.
This time lag could be a problem for missing rapidly fluctuating capacity status but we believe that this is only likely to be a major factor in the setting of emotional crises (e.g. surrounding self-harm), or drug or alcohol intoxication.
We find that there is a time lag between dissatisfaction identification and problem resolution.
In recent years, many results concerning a synchronization problem of time lag neural networks have been investigated in the literature [5, 6, 8 15, 27, 36, 49, 50].
As a consequence, we cannot rely on just waiting for the visceral responses to the manifestations of what the scientists have warned about for so long, because the physics of the problem create this time lag.
