Comparisons of soil suction induced by evapotranspiration and transpiration of S. <i>heptaphylla</i>

For a given evapotranspiration (ETr), both soil evaporation and plant transpiration (Tr) would induce soil suction. However, the relative contribution of these two processes to the amount of suction induced is not clear. The objective of this study is to quantify ETr- and Tr-induced suction by a selected tree species, Scheffllera heptaphylla, in silty sand. The relative contribution of transpiration and evaporation to the responses of suction is then explored based on observed differences in Tr- and ETr-induced suction. In total, 12 test boxes were used for testing: 10 for vegetated soil with different values of leaf area index (LAI) and root area index (RAI), while two were for bare soil as references. Each box was exposed to identical atmospheric conditions controlled in a plant room for monitoring suction responses over a week. Due to the additional effects of soil evaporation, ETr-induced suction could be 3%–47% higher than Tr-induced suction, depending on LAI. The significance of evaporation reduced substantially when LAI was higher, as relatively less radiant energy fell on the soil surface for evaporation. For a given LAI, the effects of evaporation were less significant at deeper depths within the root zone. The effects of RAI associated with root-water uptake upon transpiration were the dominant process of ETr affecting the suction responses.</jats:p

The Radon Monitoring System in Daya Bay Reactor Neutrino Experiment

We developed a highly sensitive, reliable and portable automatic system (H$^{3}$) to monitor the radon concentration of the underground experimental halls of the Daya Bay Reactor Neutrino Experiment. H$^{3}$ is able to measure radon concentration with a statistical error less than 10\% in a 1-hour measurement of dehumidified air (R.H. 5\% at 25$^{\circ}$C) with radon concentration as low as 50 Bq/m$^{3}$. This is achieved by using a large radon progeny collection chamber, semiconductor $\alpha$-particle detector with high energy resolution, improved electronics and software. The integrated radon monitoring system is highly customizable to operate in different run modes at scheduled times and can be controlled remotely to sample radon in ambient air or in water from the water pools where the antineutrino detectors are being housed. The radon monitoring system has been running in the three experimental halls of the Daya Bay Reactor Neutrino Experiment since November 2013

Stochastic Reinforcement Learning

In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation. Such stochastic elements are often numerous and cannot be known in advance, and they have a tendency to obscure the underlying rewards and punishments patterns. Indeed, if stochastic elements were absent, the same outcome would occur every time and the learning problems involved could be greatly simplified. In addition, in most practical situations, the cost of an observation to receive either a reward or punishment can be significant, and one would wish to arrive at the correct learning conclusion by incurring minimum cost. In this paper, we present a stochastic approach to reinforcement learning which explicitly models the variability present in the learning environment and the cost of observation. Criteria and rules for learning success are quantitatively analyzed, and probabilities of exceeding the observation cost bounds are also obtained.Comment: AIKE 201