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

Properties of a magnetic superconductor with weak magnetization - application to ErNi2B2CErNi_2B_2C

Using a Ginsburg-Landau free energy functional, we study the $H-T$ phase diagram of a weak magnetic superconductor, where the magnetization from the magnetic component is marginal in supporting a spontaneous vortex phase in absence of external magnetic field. In particular, the competition between the spiral state and spontaneous vortex phase is analysed. Our theory is applied to understand the magnetic properties of $ErNi_2B_2C$.Comment: 13 pages, 4 postscript figure

Ordering dynamics of the driven lattice gas model

The evolution of a two-dimensional driven lattice-gas model is studied on an L_x X L_y lattice. Scaling arguments and extensive numerical simulations are used to show that starting from random initial configuration the model evolves via two stages: (a) an early stage in which alternating stripes of particles and vacancies are formed along the direction y of the driving field, and (b) a stripe coarsening stage, in which the number of stripes is reduced and their average width increases. The number of stripes formed at the end of the first stage is shown to be a function of L_x/L_y^\phi, with \phi ~ 0.2. Thus, depending on this parameter, the resulting state could be either single or multi striped. In the second, stripe coarsening stage, the coarsening time is found to be proportional to L_y, becoming infinitely long in the thermodynamic limit. This implies that the multi striped state is thermodynamically stable. The results put previous studies of the model in a more general framework

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