Drone-Based Cattle Detection Using Deep Neural Networks

© 2021, Springer Nature Switzerland AG. Cattle form an important source of farming in many countries. In literature, several attempts have been conducted to detect farm animals for different applications and purposes. However, these approaches have been based on detecting animals from images captured from ground level and most approaches use traditional machine learning approaches for their automated detection. In this modern era, Drones facilitate accessing images in challenging environments and scanning large-scale areas with minimum time, which enables many new applications to be established. Considering the fact that drones typically are flown at high altitude to facilitate coverage of large areas within a short time, the captured object size tend to be small and hence this significantly challenges the possible use of traditional machine learning algorithms for object detection. This research proposes a novel methodology to detect cattle in farms established in desert areas using Deep Neural Networks. We propose to detect animals based on a ‘group-of-animals’ concept and associated features in which different group sizes and animal density distribution are used. Two state-of-the-art Convolutional Neural Network (CNN) architectures, SSD-500 and YOLO V-3, are effectively configured, trained and used for the purpose and their performance efficiencies are compared. The results demonstrate the capability of the two generated CNN models to detect groups-of-animals in which the highest accuracy recorded was when using SSD-500 giving a F-score of 0.93, accuracy of 0.89 and mAP rate of 84.7

Behavioural responses to SARS-CoV-2 antibody testing in England: REACT-2 study

Background: This study assesses the behavioural responses to SARS-CoV-2 antibody test results as part of the REal-time Assessment of Community Transmission-2 (REACT-2) research programme, a large community-based surveillance study of antibody prevalence in England. Methods: A follow-up survey was conducted six weeks after the SARS-CoV-2 antibody test. The follow-up survey included 4500 people with a positive result and 4039 with a negative result. Reported changes in behaviour were assessed using difference-in-differences models. A nested interview study was conducted with 40 people to explore how they thought through their behavioural decisions. Results: While respondents reduced their protective behaviours over the six weeks, we did not find evidence that positive test results changed participant behaviour trajectories in relation to the number of contacts the respondents had, for leaving the house to go to work, or for leaving the house to socialise in a personal place. The qualitative findings supported these results. Most people did not think that they had changed their behaviours because of their test results, however they did allude to some changes in their attitudes and perceptions around risk, susceptibility, and potential severity of symptoms. Conclusions: We found limited evidence that knowing your antibody status leads to behaviour change in the context of a research study. While this finding should not be generalised to widespread self-testing in other contexts, it is reassuring given the importance of large prevalence studies, and the practicalities of doing these at scale using self-testing with lateral flow immunoassay (LFIA)

Impact of the COVID-19 pandemic on admission rates for, and management of, acute coronary syndromes in England

Background Several countries affected by the COVID-19 pandemic have reported a substantial drop in the number of patients attending the emergency department with acute coronary syndromes and a reduced number of cardiac procedures. We aimed to understand the scale, nature, and duration of changes to admissions for different types of acute coronary syndrome in England and to evaluate whether in-hospital management of patients has been affected as a result of the COVID-19 pandemic. Methods We analysed data on hospital admissions in England for types of acute coronary syndrome from Jan 1, 2019, to May 24, 2020, that were recorded in the Secondary Uses Service Admitted Patient Care database. Admissions were classified as ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), myocardial infarction of unknown type, or other acute coronary syndromes (including unstable angina). We identified revascularisation procedures undertaken during these admissions (ie, coronary angiography without percutaneous coronary intervention [PCI], PCI, and coronary artery bypass graft surgery). We calculated the numbers of weekly admissions and procedures undertaken; percentage reductions in weekly admissions and across subgroups were also calculated, with 95% CIs. Findings Hospital admissions for acute coronary syndrome declined from mid-February, 2020, falling from a 2019 baseline rate of 3017 admissions per week to 1813 per week by the end of March, 2020, a reduction of 40% (95% CI 37–43). This decline was partly reversed during April and May, 2020, such that by the last week of May, 2020, there were 2522 admissions, representing a 16% (95% CI 13–20) reduction from baseline. During the period of declining admissions, there were reductions in the numbers of admissions for all types of acute coronary syndrome, including both STEMI and NSTEMI, but relative and absolute reductions were larger for NSTEMI, with 1267 admissions per week in 2019 and 733 per week by the end of March, 2020, a percent reduction of 42% (95% CI 38–46). In parallel, reductions were recorded in the number of PCI procedures for patients with both STEMI (438 PCI procedures per week in 2019 vs 346 by the end of March, 2020; percent reduction 21%, 95% CI 12–29) and NSTEMI (383 PCI procedures per week in 2019 vs 240 by the end of March, 2020; percent reduction 37%, 29–45). The median length of stay among patients with acute coronary syndrome fell from 4 days (IQR 2–9) in 2019 to 3 days (1–5) by the end of March, 2020. Interpretation Compared with the weekly average in 2019, there was a substantial reduction in the weekly numbers of patients with acute coronary syndrome who were admitted to hospital in England by the end of March, 2020, which had been partly reversed by the end of May, 2020. The reduced number of admissions during this period is likely to have resulted in increases in out-of-hospital deaths and long-term complications of myocardial infarction and missed opportunities to offer secondary prevention treatment for patients with coronary heart disease. The full extent of the effect of COVID-19 on the management of patients with acute coronary syndrome will continue to be assessed by updating these analyses

The early mathematical education of Ada Lovelace

Ada, Countess of Lovelace, is remembered for a paper published in 1843, which translated and considerably extended an article about the unbuilt Analytical Engine, a general-purpose computer designed by the mathematician and inventor Charles Babbage. Her substantial appendices, nearly twice the length of the original work, contain an account of the principles of the machine, along with a table often described as ‘the first computer program’. In this paper we look at Lovelace's education before 1840, which encompassed older traditions of practical geometry; newer textbooks influenced by continental approaches; wide reading; and a fascination with machinery. We also challenge judgements by Dorothy Stein and by Doron Swade of Lovelace's mathematical knowledge and skills before 1840, which have impacted later scholarly and popular discourse