Ecology and conservation of Alseuosmia quercifolia (Alseuosmiaceae) in the Waikato region, New Zealand

The ecology of Alseuosmia quercifolia, a small endemic shrub, was investigated, focussing on its habitat requirements, population dynamics, phenology and reproductive biology, and conservation status. This species occurs most commonly in lowland native forests of the Waikato region of the North Island (north of latitude 38°05'S), but is also found in scattered populations to North Cape. In the Waikato region it typically occupies shady, well-drained, south or south-east facing lower slopes of hills and ranges at altitudes below 400 m. Population structures show considerable variation amongst seven study sites in the Waikato region, with disjunct size classes a reflection of the presence and abundance of introduced browsing mammals. It is a relatively short-lived (less than 50 years), slow-growing species with a fleshy fruit adapted to bird dispersal, but seed dispersal now appears to be primarily by gravity. Flowering occurs early in spring and is synchronous at both individual and population levels, occurring over a 5-week period, with peak flowering during the second and third weeks. While all populations set seed, reproductive output can be negatively affected by persistent browse and by rain during peak flowering. This species is vulnerable because it is highly palatable to introduced mammals and all plants in a population are within browse height. It has relatively narrow habitat specificity, localised distribution, and limited potential to extend its range. We suggest it fulfils the requirements of the category "declining", using the most recent classification of threatened and uncommon plants of New Zealand

Investigating the Andromeda Stream: II. Orbital Fits and Properties of the Progenitor

We construct test-particle orbits and simple N-body models that match the properties of the giant stellar stream observed to the south of M31, using the model of M31's potential derived in the companion paper by Geehan et al. (2006). We introduce a simple approximation to account for the difference in position between the stream and the orbit of the progenitor; this significantly affects the best-fitting orbits. The progenitor orbits we derive have orbital apocenter \sim 60 \kpc and pericenter \sim 3 \kpc, though these quantities vary somewhat with the current orbital phase of the progenitor which is as yet unknown. Our best combined fit to the stream and galaxy properties implies a mass within 125 kpc of M31 of (7.4 \pm 1.2) \times 10^{11} \Msun. Based on its length, width, luminosity, and velocity dispersion, we conclude that the stream originates from a progenitor satellite with mass M_s \sim 10^9 \Msun, and at most modest amounts of dark matter; the estimate of $M_s$ is again correlated with the phase of the progenitor. M31 displays a large number of faint features in its inner halo which may be progenitors or continuations of the stream. While the orbital fits are not constrained enough for us to conclusively identify the progenitor, we can identify several plausible candidates, of which a feature in the planetary nebula distribution found by Merrett et al. is the most plausible, and rule out several others. We make predictions for the kinematic properties of the successful candidates. These may aid in observational identification of the progenitor object, which would greatly constrain the allowed models of the stream.Comment: 17 pages, 10 color figures, 4 tables. Accepted by Monthly Notices; some minor revisions and corrected typo

Augmenting forearm crutches with wireless sensors for lower limb rehabilitation

Forearm crutches are frequently used in the rehabilitation of an injury to the lower limb. The recovery rate is improved if the patient correctly applies a certain fraction of their body weight (specified by a clinician) through the axis of the crutch, referred to as partial weight bearing (PWB). Incorrect weight bearing has been shown to result in an extended recovery period or even cause further damage to the limb. There is currently no minimally invasive tool for long-term monitoring of a patient's PWB in a home environment. This paper describes the research and development of an instrumented forearm crutch that has been developed to wirelessly and autonomously monitor a patient's weight bearing over the full period of their recovery, including its potential use in a home environment. A pair of standard forearm crutches are augmented with low-cost off-the-shelf wireless sensor nodes and electronic components to provide indicative measurements of the applied weight, crutch tilt and hand position on the grip. Data are wirelessly transmitted between crutches and to a remote computer (where they are processed and visualized in LabVIEW), and the patient receives biofeedback by means of an audible signal when they put too much or too little weight through the crutch. The initial results obtained highlight the capability of the instrumented crutch to support physiotherapists and patients in monitoring usage

Thermally-aware composite run-time CPU power models

Accurate and stable CPU power modelling is fundamental in modern system-on-chips (SoCs) for two main reasons: 1) they enable significant online energy savings by providing a run-time manager with reliable power consumption data for controlling CPU energy-saving techniques; 2) they can be used as accurate and trusted reference models for system design and exploration. We begin by showing the limitations in typical performance monitoring counter (PMC) based power modelling approaches and illustrate how an improved model formulation results in a more stable model that efficiently captures relationships between the input variables and the power consumption. Using this as a solid foundation, we present a methodology for adding thermal-awareness and analytically decomposing the power into its constituting parts. We develop and validate our methodology using data recorded from a quad-core ARM Cortex-A15 mobile CPU and we achieve an average prediction error of 3.7% across 39 diverse workloads, 8 Dynamic Voltage-Frequency Scaling (DVFS) levels and with a CPU temperature ranging from 31 degrees C to 91 degrees C. Moreover, we measure the effect of switching cores offline and decompose the existing power model to estimate the static power of each CPU and L2 cache, the dynamic power due to constant background (BG) switching, and the dynamic power caused by the activity of each CPU individually. Finally, we provide our model equations and software tools for implementing in a run-time manager or for using with an architectural simulator, such as gem5