Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease.

We use a spatially explicit, stochastic model to analyse the effectiveness of different scales of local control strategies in containing the long-term, multi-seasonal spread of a crop disease through a dynamically changing population of susceptible crops in which there is cryptic infection. The model distinguishes between susceptible, infested and symptomatic fields. It is motivated by rhizomania disease on sugar beet in the UK as an exemplar of a spatially structured and partially asymptomatic epidemic. Our results show the importance of matching the scales of local control strategies to prevent intensification and regional spread of disease with the inherent temporal and spatial scales of an epidemic. A simple field-scale containment strategy, whereby the susceptible crop is no longer grown on fields showing symptoms, fails for this system with cryptic infection because the locally applied control lags behind the epidemic. A farm-scale strategy, whereby growers respond to the disease status of neighbouring farms by transferring their quota for sugar beet to farmers in regions of reduced risk, succeeds. We conclude that a soil-borne pathogen such as rhizomania could be managed by movement of susceptible crops in the landscape using a strategy that matches the temporal and spatial scales of the epidemic and which take account of risk aversion among growers. We show some parallels and differences in effectiveness between a 'culling' strategy involving crop removal around emerging foci and the local deployment of partially resistant varieties that reduce amplification and transmission of inoculum. Some relationships between the control of plant and livestock diseases are briefly discussed.     

High Sensitivity,Wearable, Piezoresistive Pressure Sensors Based on Irregular Microhump Structures and Its Applications in Body Motion Sensing

A pressure sensor based on irregular microhump patterns has been proposed and developed. The devices show high sensitivity and broad operating pressure regime while comparing with regular micropattern devices. Finite element analysis (FEA) is utilized to confirm the sensing mechanism and predict the performance of the pressure sensor based on the microhump structures. Silicon carbide sandpaper is employed as the mold to develop polydimethylsiloxane (PDMS) microhump patterns with various sizes. The active layer of the piezoresistive pressure sensor is developed by spin coating PEDOT:PSS on top of the patterned PDMS. The devices show an averaged sensitivity as high as 851 kPa^?1, broad operating pressure range (20 kPa), low operating power (100 nW), and fast response speed (6.7 kHz). Owing to their flexible properties, the devices are applied to human body motion sensing and radial artery pulse. These flexible high sensitivity devices show great potential in the next generation of smart sensors for robotics, real‐time health monitoring, and biomedical applications.     

MINING SURPRISING PATTERNS AND THEIR EXPLANATIONS IN CLINICAL DATA

□ We present a new technique for interactively mining patterns and generating explanations by harnessing the expertise of domain experts. Key to the approach is the distinction between what is unexpected from the perspective of the computational data mining process and what is surprising to the domain experts and interesting relative to their needs. We demonstrate the potential of the approach for discovering patterns and generating rich explanations in a clinical domain. Discovering interesting facts in clinical data is a grand challenge, because medical practitioners and clinicians generally have exceptional knowledge in the problem domain in which they work, however, this knowledge is typically difficult to isolate computationally. To identify the desired surprising patterns, we formally record user knowledge and use that knowledge to filter and constrain the output from an objective data mining technique, with the user making the final judgement about whether a rule is surprising. Specifically, we introduce an unexpectedness algorithm based on association rule mining and Bayesian Networks and a ?-explanations technique for explanation generation to identify unexpected patterns. An implemented prototype is successfully demonstrated using a large clinical database recording incidence, prevalance, and outcome of dialysis and kidney transplant patients.     

3D Scene Reconstruction from Multiple Spherical Stereo Pairs

We propose a 3D environment modelling method using multiple pairs of high-resolution spherical images. Spherical images of a scene are captured using a rotating line scan camera. Reconstruction is based on stereo image pairs with a vertical displacement between camera views. A 3D mesh model for each pair of spherical images is reconstructed by stereo matching. For accurate surface reconstruction, we propose a PDE-based disparity estimation method which produces continuous depth fields with sharp depth discontinuities even in occluded and highly textured regions. A full environment model is constructed by fusion of partial reconstruction from spherical stereo pairs at multiple widely spaced locations. To avoid camera calibration steps for all camera locations, we calculate 3D rigid transforms between capture points using feature matching and register all meshes into a unified coordinate system. Finally a complete 3D model of the environment is generated by selecting the most reliable observations among overlapped surface measurements considering surface visibility, orientation and distance from the camera. We analyse the characteristics and behaviour of errors for spherical stereo imaging. Performance of the proposed algorithm is evaluated against ground-truth from the Middlebury stereo test bed and LIDAR scans. Results are also compared with conventional structure-from-motion algorithms. The final composite model is rendered from a wide range of viewpoints with high quality textures.     

GLL parse-tree generation

Backtracking techniques which are often used to extend recursive descent (RD) parsers can have explosive run-times and cannot deal with grammars with left recursion. GLL parsers are fully general, worst-case cubic parsers which have the recursive descent-like property that they are easy to write and to use for grammar debugging. They have the direct relationship with the grammar that an RD parser has. In this paper we give an algorithm for generating GLL parsers which build an SPPF representation of the derivations of the input, complementing our existing GLL recognition algorithm, and we show that such parsers and recognisers are worst-case cubic.     

Design of an integrated UWB antenna with dual band notch characteristics

A miniaturized couple-line-fed planar ultra-wideband (UWB) antenna is proposed, which has a dual band-notched characteristic as well as two integrated monopoles. Narrowband notches are generated at frequencies of 3.5 GHz and 5.5 GHz using independently controlled bent resonators, whereas the monopoles are designed for radiation at 900 MHz and 2.4 GHz. The proposed design is simulated with full wave solvers and verified with measurements. A good agreement is observed between the simulations and measurements for the antenna's return loss, gain and radiation pattern performances.     

Critical superparamagnetic/single-domain grain sizes in interacting magnetite particles: implications for magnetosome crystals

Magnetotactic bacteria contain chains of magnetically interacting crystals (magnetosome crystals), which they use for navigation (magnetotaxis). To improve magnetotaxis efficiency, the magnetosome crystals (usually magnetite or greigite in composition) should be magnetically stable single-domain (SSD) particles. Smaller single-domain particles become magnetically unstable owing to thermal fluctuations and are termed superparamagnetic (SP). Previous calculations for the SSD/SP threshold size or blocking volume did not include the contribution of magnetic interactions. In this study, the blocking volume has been calculated as a function of grain elongation and separation for chains of identical magnetite grains. The inclusion of magnetic interactions was found to decrease the blocking volume, thereby increasing the range of SSD behaviour. Combining the results with previously published calculations for the SSD to multidomain threshold size in chains of magnetite reveals that interactions significantly increase the SSD range. We argue that chains of interacting magnetosome crystals found in magnetotactic bacteria have used this effect to improve magnetotaxis.     

Parikh matrices and amiable words

Using the fact that the Parikh matrix mapping is not an injective mapping, the paper investigates some properties of the set of words with the same Parikh matrix; these words are called “amiable”. The presented results extend the results obtained in [A. Atanasiu, Binary amiable words, Int. J. Found. Comput. Sci. 18 (2) (2007) 387–400] for the binary case. In particular it is shown that all the words having the same Parikh matrix can be obtained one from another by applying only two types of transformations. Moreover, the mirrors of two amiable words are also amiable (thus forming a symmetrical class of words).     

Evolution of ion-induced ripple patterns on SiO2 surfaces

The evolution of nanoscale ripple patterns during sub-keV ion sputtering of thermally grown, fused and single crystalline SiO₂ surfaces has been investigated by means of atomic force microscopy. For all three materials, different dependencies of the ripple wavelength and the surface roughness on the ion fluence have been found. Within the Bradley-Harper model of pattern formation, the observed differences are consistent with different amounts of surface and near-surface mass transport by ion-enhanced viscous flow which might result from different surface energies of the SiO₂ specimens.