Subsurface transport behavior of micro-nano bubbles and potential applications for groundwater remediation.

Micro-nano bubbles (MNBs) are tiny bubbles with diameters on the order of micrometers and nanometers, showing great potential in environmental remediation. However, the application is only in the beginning stages and remains to be intensively studied. In order to explore the possible use of MNBs in groundwater contaminant removal, this study focuses on the transport of MNBs in porous media and dissolution processes. The bubble diameter distribution was obtained under different conditions by a laser particle analyzer. The permeability of MNB water through sand was compared with that of air-free water. Moreover, the mass transfer features of dissolved oxygen in water with MNBs were studied. The results show that the bubble diameter distribution is influenced by the surfactant concentration in the water. The existence of MNBs in pore water has no impact on the hydraulic conductivity of sand. Furthermore, the dissolved oxygen (DO) in water is greatly increased by the MNBs, which will predictably improve the aerobic bioremediation of groundwater. The results are meaningful and instructive in the further study of MNB research and applications in groundwater bioremediation.

Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective.

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a "model-based" (or goal-directed) system and a "model-free" (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.