A general treatment of snow microstructure exemplified by an improved relation for thermal conductivity

Finding relevant microstructural parameters beyond density is a longstanding problem which hinders the formulation of accurate parameterizations of physical properties of snow. Towards a remedy, we address the effective thermal conductivity tensor of snow via anisotropic, second-order bounds. The bound provides an explicit expression for the thermal conductivity and predicts the relevance of a microstructural anisotropy parameter <i>Q</i>, which is given by an integral over the two-point correlation function and unambiguously defined for arbitrary snow structures. For validation we compiled a comprehensive data set of 167 snow samples. The set comprises individual samples of various snow types and entire time series of metamorphism experiments under isothermal and temperature gradient conditions. All samples were digitally reconstructed by micro-computed tomography to perform microstructure-based simulations of heat transport. The incorporation of anisotropy via <i>Q</i> considerably reduces the root mean square error over the usual density-based parameterization. The systematic quantification of anisotropy via the two-point correlation function suggests a generalizable route to incorporate microstructure into snowpack models. We indicate the inter-relation of the conductivity to other properties and outline a potential impact of <i>Q</i> on dielectric constant, permeability and adsorption rate of diffusing species in the pore space

Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modelling and machine learning techniques in wh

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen condition

Sketchnote Components, Design Space Dimensions, and Strategies for Effective Visual Note Taking

Sketchnoting is a form of visual note taking where people listen to, synthesize, and visualize ideas from a talk or other event using a combination of pictures, diagrams, and text. Little is known about the design space of this kind of visual note taking. With an eye towards informing the implementation of digital equivalents of sketchnoting, inking, and note taking, we introduce a classification of sketchnote styles and techniques, with a qualitative analysis of 103 sketchnotes, and situated in context with six semi-structured follow up interviews. Our findings distill core sketchnote components (content, layout, structuring elements, and visual styling) and dimensions of the sketchnote design space, classifying levels of conciseness, illustration, structure, personification, cohesion, and craftsmanship. We unpack strategies to address particular note taking challenges, for example dealing with constraints of live drawings, and discuss relevance for future digital inking tools, such as recomposition, styling, and design suggestions

On Embeddability of Buses in Point Sets

Set membership of points in the plane can be visualized by connecting corresponding points via graphical features, like paths, trees, polygons, ellipses. In this paper we study the \emph{bus embeddability problem} (BEP): given a set of colored points we ask whether there exists a planar realization with one horizontal straight-line segment per color, called bus, such that all points with the same color are connected with vertical line segments to their bus. We present an ILP and an FPT algorithm for the general problem. For restricted versions of this problem, such as when the relative order of buses is predefined, or when a bus must be placed above all its points, we provide efficient algorithms. We show that another restricted version of the problem can be solved using 2-stack pushall sorting. On the negative side we prove the NP-completeness of a special case of BEP.Comment: 19 pages, 9 figures, conference version at GD 201

Radiometric calibration of ‘Commercial off the shelf’ cameras for UAV-based high-resolution temporal phenotyping of reflectance and NDVI

Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are 13 common metrics used for measuring traits of interest in crop phenotyping. However traditional 14 measurements of these indices are often influenced by multiple confounding factors such as canopy 15 cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to 16 Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, 17 however incomplete methods for radiometric calibration into reflectance units limits how the data 18 can be applied to phenotyping. In this study, we assess the applicability of very high spatial 19 resolution (1cm) UAV-based imagery taken with commercial off the shelf (COTS) digital cameras 20 for both deriving calibrated reflectance imagery, and isolating vegetation canopy reflectance from 21 that of the underlying soil. We present new methods for successfully normalising the imagery for 22 exposure and solar irradiance effects, generating multispectral (RGB-NIR) orthomosaics of our 23 target field based wheat crop trial. Validation against measurements from a ground spectrometer 24 showed good results for reflectance (R2 ≥ 0.6) and NDVI (R2 ≥ 0.88). Application of imagery collected 25 through the growing season and masked using the Excess Green Red index was used to assess the 26 impact of canopy cover on NDVI measurements. Results showed the impact of canopy cover 27 artificially reducing plot NDVI values in the early season, where canopy development is low

Impact of body mass index on post-thyroidectomy morbidity

BACKGROUND: The impact of obesity on total thyroidectomy (TT) morbidity (recurrent laryngeal nerve palsy and hypocalcaemia) remains largely unknown. METHODS: In a prospective study (NCT01551914), patients were divided into five groups according to their body mass index (BMI): underweight, normal weight, overweight, obese, and severely obese. Preoperative and postoperative serum calcium was measured. Recurrent laryngeal nerve (RLN) function was evaluated before discharge, and if abnormal, at 6 months. RESULTS: In total 1310 patients were included. Baseline characteristics were similar across BMI groups except for age and sex. Postoperative hypocalcaemia was more frequent in underweight compared to obese patients but the difference was not statistically significant in multivariate analysis. There was no difference between groups in terms of definitive hypocalcaemia, transient and definitive RLN palsy, and postoperative pain. CONCLUSION: Obesity does not increase intraoperative and postoperative morbidity of TT, despite a longer duration of the procedure

AirConstellations: In-Air Device Formations for Cross-Device Interaction via Multiple Spatially-Aware Armatures

AirConstellations supports a unique semi-fixed style of cross-device interactions via multiple self-spatially-aware armatures to which users can easily attach (or detach) tablets and other devices. In particular, AirConstellations affords highly flexible and dynamic device formations where the users can bring multiple devices together in-air - with 2-5 armatures poseable in 7DoF within the same workspace - to suit the demands of their current task, social situation, app scenario, or mobility needs. This affords an interaction metaphor where relative orientation, proximity, attaching (or detaching) devices, and continuous movement into and out of ad-hoc ensembles can drive context-sensitive interactions. Yet all devices remain self-stable in useful configurations even when released in mid-air. We explore flexible physical arrangement, feedforward of transition options, and layering of devices in-air across a variety of multi-device app scenarios. These include video conferencing with flexible arrangement of the person-space of multiple remote participants around a shared task-space, layered and tiled device formations with overview+detail and shared-to-personal transitions, and flexible composition of UI panels and tool palettes across devices for productivity applications. A preliminary interview study highlights user reactions to AirConstellations, such as for minimally disruptive device formations, easier physical transitions, and balancing "seeing and being seen"in remote work

Identification of Traits Underpinning Good Breadmaking Performance of Wheat Grown with Reduced Nitrogen Fertilisation

Background: Nitrogen fertiliser is the major input and cost for wheat production, being required to support the development of the canopy to maximise yield and for the synthesis of the gluten proteins that are necessary for breadmaking. Consequently, current high-yielding cultivars require the use of nitrogen fertilisation levels above the yield optimum to achieve the grain protein content needed for breadmaking. This study aimed to reduce this requirement by identifying traits that allow the use of lower levels of nitrogen fertiliser to produce wheat for breadmaking. Results: A range of commercial wheat genotypes (cultivars) were grown in multiple field trials (six sites over 3 years) in the UK with optimal (200 kg Ha-1) and suboptimal (150 kg Ha-1) application of nitrogen. Bulked grain samples from four sites per year were milled and white flours were baked using three types of breadmaking process. This identified five cultivars that consistently exhibited good breadmaking quality when grown with the lower nitrogen application. Chemical and biochemical analyses showed that the five cultivars were characterised by exhibiting grain protein deviation (GPD) and high dough elasticity. Conclusions: It is possible to develop novel types of wheat that exhibit good breadmaking quality by selecting for GPD and high dough strengt

OptiJ: Open-source optical projection tomography of large organ samples

The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples