The influence of water motion on the growth rate of the kelp Laminaria hyperborea

The kelp Laminaria hyperborea is a dominant component of the subtidal nearshore ecosystem and is subjected to a heterogeneous wave and current climate. Water motion is known to influence physiological processes in macroalgae such as photosynthesis and nutrient uptake attributed to mass-transfer limitation. The study attempts to establish the effect of water motion on the growth rates of blades and elongation rates of the stipes of L. hyperborea at adjacent wave-exposed and wave-sheltered locations over a 12month period from field observations. The observations were supported by detailed physical and chemical measurements (light, temperature, seawater nutrient concentrations and hydrodynamics) and of tissue carbon and nitrogen concentrations together with δ¹³carbon. Despite a 30% difference in the root mean square of the velocity (Vel_rms) between the two survey locations, there was no evidence to suggest that water motion had any direct influence on the growth rates of either the blades or elongation of stipes of L. hyperborea. No significant differences were observed between either environmental or plant physiological variables between the sheltered and exposed locations. Using an integral velocity parameter (Vel_rms) the present study also highlighted the importance of the tidally induced current component of water flow in the subtidal zone. 

Non-contact under-mattress sleep monitoring

Sleep quality and duration are increasingly recognised as being important prognostic parameters in the assessment of an individual's health. However, reliable non-invasive long-term monitoring of sleep in a non-clinical setting remains a challenging problem. This paper describes the validation of a novel under mattress pressure sensing sleep monitoring modality that can be seamlessly integrated into existing home environments and provides a pervasive and distributed solution for monitoring long-term changes in sleep patterns and sleep disorders in adults. 410 minutes of concomitant Under Mattress Bed Sensor (UMBS) and strain gauge data were analysed from eight healthy adults lying passively. In this analysis, customised respirations rate detection algorithms yielded a mean difference of −0.12 breaths per five minutes and a mean percentage error (MPE) of 0.16% when the sensor was placed beneath the mattress. 1,491 minutes of UMBS and video data were recorded simultaneously from four participants in order to assess the movement detection efficacy of customised UMBS algorithms. These algorithms yielded accuracies, sensitivities and specificities of over 90% when compared to a video-based movement detection gold standard. A reduced data set (267 minutes) of wrist actigraphy, the gold standard ambulatory sleep monitor, was recorded. The UMBS was shown to outperform the movement detection ability of wrist actigraphy and has the added advantage of not requiring active subject participation.

Inference with multinomial data: why to weaken the prior strength

This paper considers inference from multinomial data and addresses the problem of choosing the strength of the Dirichlet prior under a mean-squared error criterion. We compare the Maxi-mum Likelihood Estimator (MLE) and the most commonly used Bayesian estimators obtained by assuming a prior Dirichlet distribution with non-informative prior parameters, that is, the parameters of the Dirichlet are equal and altogether sum up to the so called strength of the prior. Under this criterion, MLE becomes more preferable than the Bayesian estimators at the increase of the number of categories k of the multinomial, because non-informative Bayesian estimators induce a region where they are dominant that quickly shrinks with the increase of k. This can be avoided if the strength of the prior is not kept constant but decreased with the number of categories. We argue that the strength should decrease at least k times faster than usual estimators do.

Absolute Magnitudes of Pan-STARRS PS1 Asteroids

Absolute magnitude (H) of an asteroid is a fundamental parameter describing the size and the apparent brightness of the body. Because of its surface shape, properties and changing illumination, the brightness changes with the geometry and is described by the phase function governed by the slope parameter (G). Although many years have been spent on detailed observations of individual asteroids to provide H and G, vast majority of minor planets have H based on assumed G and due to the input photometry from multiple sources the errors of these values are unknown. We compute H of ~ 180 000 and G of few thousands asteroids observed with the Pan-STARRS PS1 telescope in well defined photometric systems. The mean photometric error is 0.04 mag. Because on average there are only 7 detections per asteroid in our sample, we employed a Monte Carlo (MC) technique to generate clones simulating all possible rotation periods, amplitudes and colors of detected asteroids. Known asteroid colors were taken from the SDSS database. We used debiased spin and amplitude distributions dependent on size, spectral class distributions of asteroids dependent on semi-major axis and starting values of G from previous works. H and G (G12 respectively) were derived by phase functions by Bowell et al. (1989) and Muinonen et al. (2010). We confirmed that there is a positive systematic offset between H based on PS1 asteroids and Minor Planet Center database up to -0.3 mag peaking at 14. Similar offset was first mentioned in the analysis of SDSS asteroids and was believed to be solved by weighting and normalizing magnitudes by observatory codes. MC shows that there is only a negligible difference between Bowell's and Muinonen's solution of H. However, Muinonen's phase function provides smaller errors on H. We also derived G and G12 for thousands of asteroids. For known spectral classes, slope parameters agree with the previous work in general, however, the standard deviation of G in our sample is twice as larger, most likely due to sparse phase curve sampling. In the near future we plan to complete the H and G determination for all PS1 asteroids (500,000) and publish H and G values online. This work was supported by NASA grant No. NNX12AR65G.

Performance assessment of cascade control loopswith non-Gaussian disturbances using entropy information

Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

Winter Is Coming: Seasonal Variation in Resting Metabolic Rate of the European Badger (Meles meles)

Resting metabolic rate (RMR) is a measure of the minimum energy requirements of an animal at rest, and can give an indication of the costs of somatic maintenance. We measured RMR of free-ranging European badgers (Meles meles) to determine whether differences were related to sex, age and season. Badgers were captured in live-traps and placed individually within a metabolic chamber maintained at 20 ± 1°C. Resting metabolic rate was determined using an open-circuit respirometry system. Season was significantly correlated with RMR, but no effects of age or sex were detected. Summer RMR values were significantly higher than winter values (mass-adjusted mean ± standard error: 2366 ± 70 kJ⋅d-1; 1845 ± 109 kJ⋅d-1, respectively), with the percentage difference being 24.7%. While under the influence of anaesthesia, RMR was estimated to be 25.5% lower than the combined average value before administration, and after recovery from anaesthesia. Resting metabolic rate during the autumn and winter was not significantly different to allometric predictions of basal metabolic rate for mustelid species weighing 1 kg or greater, but badgers measured in the summer had values that were higher than predicted. Results suggest that a seasonal reduction in RMR coincides with apparent reductions in physical activity and body temperature as part of the overwintering strategy ('winter lethargy') in badgers. This study contributes to an expanding dataset on the ecophysiology of medium-sized carnivores, and emphasises the importance of considering season when making predictions of metabolic rate.