Improved image recognition via synthetic plants using 3D modelling with stochastic variations

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics

A synthetic wheat l-system to accurately detect and visualise wheat head anomalies

Greater knowledge of wheat crop phenology and growth and improvements in measurement are beneficial to wheat agronomy and productivity. This is constrained by a lack of public plant datasets. Collecting plant data is expensive and time consuming and methods to augment this with synthetic data could address this issue. This paper describes a cost-effective and accurate Synthetic Wheat dataset which has been created by a novel L-system, based on technological advances in cameras and deep learning. The dataset images have been automatically created, categorised, masked and labelled, and used to successfully train a synthetic neural network. This network has been shown to accurately recognise wheat in pasture images taken from the Global Wheat dataset, which provides for the ongoing interest in the phenotyping of wheat characteristics around the world. The proven Mask R-CNN and Detectron2 frameworks have been used, and the created network is based on the public COCO format. The research question is “How can L-system knowledge be used to create an accurate synthetic wheat dataset and to make cost-effective wheat crop measurements?”

Improved image recognition via Synthetic Plants using 3D Modelling with Stochastic Variations

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural dataset hierarchy. The research demonstrates a significantly improved L-system that can more effectively and more accurately define and distinguish wheat crop characteristics

Relation of severe COVID-19 in Scotland to transmission-related factors and risk conditions eligible for shielding support:REACT-SCOT case-control study

Abstract Background Clinically vulnerable individuals have been advised to shield themselves during the COVID-19 epidemic. The objectives of this study were to investigate (1) the rate ratio of severe COVID-19 associated with eligibility for the shielding programme in Scotland across the first and second waves of the epidemic and (2) the relation of severe COVID-19 to transmission-related factors in those in shielding and the general population. Methods In a matched case-control design, all 178,578 diagnosed cases of COVID-19 in Scotland from 1 March 2020 to 18 February 2021 were matched for age, sex and primary care practice to 1,744,283 controls from the general population. This dataset (REACT-SCOT) was linked to the list of 212,702 individuals identified as eligible for shielding. Severe COVID-19 was defined as cases that entered critical care or were fatal. Rate ratios were estimated by conditional logistic regression. Results With those without risk conditions as reference category, the univariate rate ratio for severe COVID-19 was 3.21 (95% CI 3.01 to 3.41) in those with moderate risk conditions and 6.3 (95% CI 5.8 to 6.8) in those eligible for shielding. The highest rate was in solid organ transplant recipients: rate ratio 13.4 (95% CI 9.6 to 18.8). Risk of severe COVID-19 increased with the number of adults but decreased with the number of school-age children in the household. Severe COVID-19 was strongly associated with recent exposure to hospital (defined as 5 to 14 days before presentation date): rate ratio 12.3 (95% CI 11.5 to 13.2) overall. The population attributable risk fraction for recent exposure to hospital peaked at 50% in May 2020 and again at 65% in December 2020. Conclusions The effectiveness of shielding vulnerable individuals was limited by the inability to control transmission in hospital and from other adults in the household. Mitigating the impact of the epidemic requires control of nosocomial transmission

Mesons with Beauty and Charm: Spectroscopy

Applying knowledge of the interaction between heavy quarks derived from the study of $c\overline{c}$ and $b\overline{b}$ bound states, we calculate the spectrum of $c\overline{b}$ mesons. We compute transition rates for the electromagnetic and hadronic cascades that lead from excited states to the $^1\text{S}_0$ ground state, and briefly consider the prospects for experimental observation of the spectrum.Comment: 32 pages + 2 uuencoded PostScript figures Fermilab-Pub-94/032-

Committed global warming risks triggering multiple climate tipping points

Many scenarios for limiting global warming to 1.5°C assume planetary-scale carbon dioxide removal sufficient to exceed anthropogenic emissions, resulting in radiative forcing falling and temperatures stabilizing. However, such removal technology may prove unfeasible for technical, environmental, political, or economic reasons, resulting in continuing greenhouse gas emissions from hard-to-mitigate sectors. This may lead to constant concentration scenarios, where net anthropogenic emissions remain non-zero but small, and are roughly balanced by natural carbon sinks. Such a situation would keep atmospheric radiative forcing roughly constant. Fixed radiative forcing creates an equilibrium “committed” warming, captured in the concept of “equilibrium climate sensitivity.” This scenario is rarely analyzed as a potential extension to transient climate scenarios. Here, we aim to understand the planetary response to such fixed concentration commitments, with an emphasis on assessing the resulting likelihood of exceeding temperature thresholds that trigger climate tipping points. We explore transients followed by respective equilibrium committed warming initiated under low to high emission scenarios. We find that the likelihood of crossing the 1.5°C threshold and the 2.0°C threshold is 83% and 55%, respectively, if today's radiative forcing is maintained until achieving equilibrium global warming. Under the scenario that best matches current national commitments (RCP4.5), we estimate that in the transient stage, two tipping points will be crossed. If radiative forcing is then held fixed after the year 2100, a further six tipping point thresholds are crossed. Achieving a trajectory similar to RCP2.6 requires reaching net-zero emissions rapidly, which would greatly reduce the likelihood of tipping events