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The Continuous Glucose Monitoring System (CGMS) is an effective tool which enables the users to monitor their blood glucose (BG) levels. Based on the CGM data, we aim at predicting future BG levels so that appropriate actions can be taken in advance to prevent hyperglycemia or hypoglycemia. Due to the time-varying non-stationarity of CGM data, verified by Augmented Dickey-Fuller (ADF) test and Analysis of Variance (ANOVA), an Autoregressive Integrated Moving Average (ARIMA) model with an adaptive identification algorithm of model orders is proposed in the prediction framework. Such identification algorithm adaptively determines the model orders and simultaneously estimates the corresponding parameters using Akaike Information Criterion (AIC) and least square estimation (LSE). A case study is conducted with the CGM data of diabetics under daily living conditions to analyze the prediction performance of the proposed model together with the early hypoglycemic alarms. Results show that the proposed model outperforms the adaptive univariate model and ARIMA model.

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Summary Proposed syntrophic interactions between the archaeal and bacterial cells mediating anaerobic oxidation of methane coupled with sulfate reduction include electron transfer through (1) the exchange of H2 or small organic molecules between methane‐oxidizing archaea and sulfate‐reducing bacteria, (2) the delivery of disulfide from methane‐oxidizing archaea to bacteria for disproportionation and (3) direct interspecies electron transfer. Each of these mechanisms was implemented in a reactive transport model. The simulated activities across different arrangements of archaeal and bacterial cells and aggregate sizes were compared to empirical data for AOM rates and intra‐aggregate spatial patterns of cell‐specific anabolic activity determined by FISH‐nanoSIMS. Simulation results showed that rates for chemical diffusion by mechanism (1) were limited by the build‐up of metabolites, while mechanisms (2) and (3) yielded cell specific rates and archaeal activity distributions that were consistent with observations from single cell resolved FISH‐nanoSIMS analyses. The novel integration of both intra‐aggregate and environmental data provided powerful constraints on the model results, but the similarities in model outcomes for mechanisms (2) and (3) highlight the need for additional observational data (e.g. genomic or physiological) on electron transfer and metabolic functioning of these globally important methanotrophic consortia.