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This paper presents a structured methodology for the constrained multi-objective optimization (MO) of a continuous cross-flow atomic layer deposition (ALD) reactor model with temporal precursor pulsing.
A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming.
(2) If we employ a dynamic model with temporal variation and introduce a different distribution between April 1991 and June 1992, RMSE is further improved from the static model.
Specialized data processing routines have been developed to link the network data model with temporal hydrological modeling to allow computation of important network parameters such as flow travel time, discharge and capacity, and to dynamically simulate drainage response to individual rainfall events.
In a case study, the predictive performance of the four models was assessed using data collected in 2007 and 2008 for the Greater Toronto Area by the mosquito surveillance program of Ontario Ministry of Health and Long-term Care: the local space-time kriging model outperforms others, but closely followed by a site-specific linear mixed model with temporal correlation.
Computationalists respond that we can supplement an abstract computational model with temporal considerations (Piccinini 2010; Weiskopf 2004).
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Several systems change their structure over time, and their properties can be modelled with temporal networks (Holme and Saramäki 2012).
Since the neuron charging time, interconnection weights and external inputs often change during the course of time, neural models with temporal structure of neural activities are much closer to real systems, and hence studies on such systems and their discrete versions are of great practical and theoretical value.
For each trait we previously fitted univariate models with temporal autocorrelation using the nlme package [ 80].
Variations in exposure were mostly attributable to spatial rather than temporal variations in both models, with temporal variation making a larger overall contribution to total variation in the TAG model than in the nearest-AQMS model.
After finding the most supported capture probability model, we continued by fitting dead encounter probability models under the most supported capture probability model and with temporal variation in survival.
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