Time-series temperature analyses indicate conduction and diffusion are dominant heat-transfer processes in fine sediment, low-flow streams.

Stream-groundwater exchange has been investigated in a wide range of hydrologic settings, though very few studies have focused on fine-sediment streambeds. Well-established thermal methods (i.e., analytical and numerical solution of time-series temperature depth-profiles) in combination with Darcy's and electrical resistivity (ER) evaluations were implemented to improve understanding of processes dominating flow and transport in a low permeability and low-flow coastal stream such as Oso Creek, Texas. The seasonal-trend decomposition using Loess (STL) is tested as a potential means to differentiate between advection and conduction and is validated against groundwater fluxes derived from the other well-established thermal methods. The numerical and analytical solutions indicate groundwater upward discharge was 9 mm d-1 for summer and 3.5 mm d-1 for winter, corresponding to the region's extreme drought conditions. These types of low flow conditions are usually accompanied by hyporheic flow, limiting the vertical flow assumption. While the numerical and analytical methods provide good insight into streambed hydrology for a low-permeability and low-flow stream in a semiarid coastal area, there are limitations associated with the STL method. The analytical and numerical thermal methods employed herein confirm that conduction and diffusion are the dominant processes of heat and solute transfer in fine-sediment streambeds, providing an improved understanding of process-based groundwater-stream interaction and water resources in this type of settings.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.