A metapopulation model with Markovian landscape dynamics

We study a variant of Hanski's incidence function model that allows habitat patch characteristics to vary over time following a Markov process. The widely studied case where patches are classified as either suitable or unsuitable is included as a special case. For large metapopulations, we determine a recursion for the probability that a given habitat patch is occupied. This recursion enables us to clarify the role of landscape dynamics in the survival of a metapopulation. In particular, we show that landscape dynamics affects the persistence and equilibrium level of the metapopulation primarily through its effect on the distribution of a local population's life span.Comment: This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Kinetics and mechanism of formic acid decomposition on Ru(001)

The steady-state rate of decomposition of formic acid on Ru(001) has been measured as a function of surface temperature, parametric in the pressure of formic acid. The products of the decomposition reaction are C0_2, H_2, CO, and H_2)0, i.e., both dehydrogenation and dehydration occur on Ru (001). A similar product distribution has been observed on Ni(110), Ni(100), Ru(100), Fe(100), and Ni(111) surfaces; whereas only dehydrogenation to C0_2 and H_2 occurs on the Cu(100), Cu(110), and Pt(111) surfaces. Only reversible adsorption and desorption of formic acid is observed on the less reactive Ag(110) surface at low temperatures, whereas the more reactive Mo(100) surface is oxidized by formic acid at low temperatures with the products of this reaction being H_2, CO, and H_(2)O (Ref. 10). We report here the confirmation of earlier observations of the occurrence of both dehydrogenation and dehydration of formic acid on Ru(001), and more importantly, we provide a detailed mechanistic description of the steady-state decomposition reaction on this surface in terms of elementary steps

AN ANKLE SPRAIN RECOGNITION SYSTEM FOR IDENTIFYING ANKLE SPRAIN MOTION FROM OTHER NORMAL MOTION USING MOTION SENSOR

The purpose of this study was to develop an ankle sprain recognition system which identifies ankle sprain motions from other normal motions. Six healthy male subjects performed a total of 600 simulated ankle sprain motions and normal sports motions. Eight motion sensors were attached to cover the whole foot segment to monitor the linear velocity and angular accelerations of the segment. The data obtained from the motion sensor at the medial calcaneus selected to train up the Support Vector Machine (SVM). The trained SVM model was then verified by another 600 trials from other six healthy male subjects. Among the 300 sprain trials, 291 (97.0%) of them were identified correctly. However, there was still a 14.3% false alarm which normal trials being identified as sprain trails. In general, a good accuracy of 91.3% was achieved