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This study utilized a time series of five sequential light detection and ranging (LiDAR) derived digital elevation models (DEMs) to track watershed geomorphic changes within two watersheds throughout development (2002 2008) and across multiple spatial scales (0.01 1 km2).
Building on theories developed in natural disaster research and borrowing several core constructs as a starting point to comprehend this "social disaster", we utilized a time series design and DD analysis to explore the ecology of Jasper's response to this event by examining crime, economic, education, medical and social capital measures.
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Throughout this study, we utilized a time series-based model namely Autoregressive Integrated Moving Average (ARIMA), as well as SARIMA when seasonality is included.
First, we utilized a time-series model to assess the relationship between ED visits and ambient temperature, while controlling for air pollutants (PM10 2.5, SO2 and NO2), relative humidity and wind speed.
The principal tools utilized for analysis of a time series are the autocorrelation and partial autocorrelation functions (PACFs).
We utilized an annotated time series analysis to correlate the introduction of specific changes with resulting PMTCT process performance shifts in order to help understand which changes resulted in the improvement described above.
A forecasting model is then computed by resorting to multivariate time series analysis, in order to predict the future values of a time series by utilizing not only its own historical values, but also information from other cluster-time series.
That is, they do not consider the dependency between different time points of a time series and thus do not utilize the dynamic nature of time series data.
This study utilizes field-collected depth sounding data to perform Lyzenga Depth Correction on a time series of high-resolution coastal satellite images covering a marine preserve off the coast of Belize.
We therefore conclude that the advantage of discrete DBNs to capture also non-linear effects can only be utilized with much larger data sizes than we consider and that are usually available in a time series microarray experiment.
The objective of this study was to utilize DNA microarray technology to assess the transcriptional response to insulin in endothelial cells, in a time series microarray experiment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com