The debate about whether making a risky choice is based on a weighting and adding process has a long history and is still unresolved. To address this long-standing controversy, we developed a comparative paradigm. Participants' eye movements in 2 risky choice tasks that required participants to choose between risky options in single-play and multiple-play conditions were separately compared with those in a baseline task in which participants naturally performed a deliberate calculation following a weighting and adding process. The results showed that, when participants performed the multiple-play risky choice task, their eye movements were similar to those in the baseline task, suggesting that participants may use a weighting and adding process to make risky choices in multiple-play conditions. In contrast, participants' eye movements were different in the single-play risky choice task versus the baseline task, suggesting that participants were not likely to use a weighting and adding process to make risky choices in single-play conditions and were more likely to use a heuristic process. We concluded that an expectation-based index for predicting risk preferences is applicable in multiple-play conditions but not in single-play conditions, implying the need to improve current theories that postulate the use of a heuristic process.

The simulation results imply that the control limits should vary based on the particular patient population of interest in order to control the in-control performance of the risk-adjusted Bernoulli CUSUM method.

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Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

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Gibson (1966, 1979) and Lee (1976) have described the potential usefulness of optic-flow information for the control of locomotion. One variable that might be particularly important for an animal approaching a target is time-to-collision, which Lee argues is most efficiently specified by the tau margin (the inverse of the relative rate of expansion of the target image on the retina). In humans, most empirical studies of optic flow have required perceptual judgements or have examined catching/intercepting behaviours. In animals, most studies have been strictly observational. This is particularly true for mammals, where there has been no experimental work of any kind looking at the control of locomotion. The present experiment demonstrates that the Mongolian gerbil (Meriones unguiculatus) uses time-to-collision information to control deceleration as it runs towards a target. The development of this animal model will aid investigation of the neural circuitry underlying optic flow utilization in motor control.

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