Social Sensing of Floods in the UK

"Social sensing" is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes `relevance' filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.Comment: 24 pages, 6 figure

N released from organic amendments is affected by soil management history

A ryegrass bioassay was conducted to investigate the effect of soil management history on nitrogen mineralisation from composted manure and pelleted poultry manure. Soils were used from 2 field experiments comparing conventional and organic/low input management systems. When composted manure was added, soils which had received high rates of composted FYM under biodynamic management released a greater amount of nitrogen for plant uptake than those with a history of mineral or fresh manure fertilisation, suggesting that biological preconditioning may result in greater efficiency of composted FYM as a nitrogen source for plants. “Native” N mineralisation was found to be related to total soil N content

Effect of organic, low-input and conventional production systems on yield and diseases in winter barley

The effect of organic, low-input and conventional management practices on barley yield and disease incidence was assessed in field trials over two years. Conventional fertility management (based on mineral fertiliser applications) and conventional crop protection (based on chemosynthetic pesticides) significantly increased the yield of winter barley as compared to organic fertility and crop protection regimes. Severity of leaf blotch (Rhynchosporium secalis) was highest under organic fertility and crop protection management and was correlated inversely with yield. For mildew (Erysiphe graminis), an interaction between fertility management and crop protection was detected. Conventional crop protection reduced severity of the disease, only under conventional fertility management. Under organic fertility management, incidence of mildew was low and application of synthetic pesticides in “low input” production systems had no significant effect on disease severity

Linking vision and motion for self-supervised object-centric perception

Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples perception from the downstream driving task and could harm generalization. In this work we adapt a self-supervised object-centric vision model to perform object decomposition using only RGB video and the pose of the vehicle as inputs. We demonstrate that our method obtains promising results on the Waymo Open perception dataset. While object mask quality lags behind supervised methods or alternatives that use more privileged information, we find that our model is capable of learning a representation that fuses multiple camera viewpoints over time and successfully tracks many vehicles and pedestrians in the dataset. Code for our model is available at https://github.com/wayveai/SOCS.Comment: Presented at the CVPR 2023 Vision-Centric Autonomous Driving worksho

A Unified Nanopublication Model for Effective and User-Friendly Access to the Elements of Scientific Publishing

Scientific publishing is the means by which we communicate and share scientific knowledge, but this process currently often lacks transparency and machine-interpretable representations. Scientific articles are published in long coarse-grained text with complicated structures, and they are optimized for human readers and not for automated means of organization and access. Peer reviewing is the main method of quality assessment, but these peer reviews are nowadays rarely published and their own complicated structure and linking to the respective articles is not accessible. In order to address these problems and to better align scientific publishing with the principles of the Web and Linked Data, we propose here an approach to use nanopublications as a unifying model to represent in a semantic way the elements of publications, their assessments, as well as the involved processes, actors, and provenance in general. To evaluate our approach, we present a dataset of 627 nanopublications representing an interlinked network of the elements of articles (such as individual paragraphs) and their reviews (such as individual review comments). Focusing on the specific scenario of editors performing a meta-review, we introduce seven competency questions and show how they can be executed as SPARQL queries. We then present a prototype of a user interface for that scenario that shows different views on the set of review comments provided for a given manuscript, and we show in a user study that editors find the interface useful to answer their competency questions. In summary, we demonstrate that a unified and semantic publication model based on nanopublications can make scientific communication more effective and user-friendly

Effect of Climate, Crop Protection, and Fertilization on Disease Severity, Growth, and Grain Yield Parameters of Faba Beans (<em>Vicia faba</em> L.) in Northern Britain: Results from the Long-Term NFSC Trials

\ua9 2024 by the authors. Faba beans are one of the most suitable grain legume crop for colder, maritime climates. However, there is limited information on the effect of changing from conventional to organic production methods and potential impacts of global warming on the health and performance of faba bean crops in Northern Europe. We therefore assessed the performance of faba beans grown with contrasting crop protection (with and without pesticides) and fertilization (with and without P and K fertilizer input) regimes used in organic and conventional production in seven growing seasons. Conventional crop protection and fertilization regimes had no effect on foliar disease severity, but resulted in small, but significant increases in faba bean yields. The overall yield gap between organic and conventional production regimes was relatively small (~10%), but there was substantial variation in yields between growing seasons/years. Redundancy analysis (RDA) showed that climate explanatory variables/drivers explained the largest proportion of the variation in crop performance and identified strong positive associations between (i) temperature and both straw and grain yield and (ii) precipitation and foliar disease severity. However, RDA also identified crop protection and variety as significant explanatory variables for faba bean performance. The relatively small effect of using P and K fertilizers on yields and the lack of a measurable effect of fungicide applications on foliar disease severity indicate that the use of these inputs in conventional faba beans may not be economical. Results also suggest that the yield gap between organic and conventional faba bean production is significant, but smaller than for other field crops

Organising multi-dimensional biological image information: The BioImage Database

Nowadays it is possible to unravel complex information at all levels of cellular organization by obtaining multi-dimensional image information. at the macromolecular level, three-dimensional (3D) electron microscopy, together with other techniques, is able to reach resolutions at the nanometer or subnanometer level. The information is delivered in the form of 3D volumes containing samples of a given function, for example, the electron density distribution within a given macromolecule. The same situation happens at the cellular level with the new forms of light microscopy, particularly confocal microscopy, all of which produce biological 3D volume information. Furthermore, it is possible to record sequences of images over time (videos), as well as sequences of volumes, bringing key information on the dynamics of living biological systems. It is in this context that work on bioimage started two years ago, and that its first version is now presented here. In essence, Bioimage is a database specifically designed to contain multi-dimensional images, perform queries and interactively work with the resulting multi-dimensional information on the World Wide Web, as well as accomplish the required cross-database links. Two sister home pages of bioimage can be accessed at http://www.bioimage.org and http://www-embl.bioimage.or