Using a human rights and feminist economist perspective, this article analyzes the emergency financial policies deployed by international financial institutions (IFIs)-in particular the IMF and the World Bank-to help countries in Latin America cope with the COVID-19 crisis. Looking at the macroeconomic and fiscal assumptions behind IMF loans to countries, it identifies clear signals that fiscal discipline and pro-market options will continue to be priorities as soon as the emergency has been overcome. The study explains how recent adjustment and austerity policies adopted by a number of countries have disproportionately affected women's human rights, reinforcing the invisibilization of gender inequalities in domestic and care work and in turn, making women even more vulnerable to the impact of the pandemic and resulting economic recession. It concludes that in order to properly consider the conditions of IFI loans, countries must evaluate the probable impact of these financial contracts on people's human rights, and in particular on gender equality.
  This study aims to estimate the risk of acquiring medical complication or death from COVID-19 infection in patients who were admitted for orthopaedic trauma surgery during the peak and plateau of pandemic. Unlike other recently published studies, where patient-cohort included a more morbid group and cancer surgeries, we report on a group of patients who had limb surgery and were more akin to elective orthopaedic surgery.
 
  The study included 214 patients who underwent orthopaedic trauma surgeries in the hospital between 12th March and 12th May-2020 when the pandemic was on the rise in the United Kingdom. Data was collected on demographic profile including comorbidities, ASA grade, COVID-19 testing, type of procedures and any readmissions, complications or mortality due to COVID-19.
 
  There were 7.9% readmissions and 52.9% of it was for respiratory complications. Only one patient had positive COVID-19 test during readmission. 30-day mortality for trauma surgeries was 0% if hip fractures were excluded and 2.8dst the recovery phase of the pandemic.We present results from observation, correlation and analysis of interferometric measurements between the three geodetic very long baseline interferometry (VLBI) stations at the Onsala Space Observatory. In total, 25 sessions were observed in 2019 and 2020, most of them 24 h long, all using X band only. These involved the legacy VLBI station ONSALA60 and the Onsala twin telescopes, ONSA13NE and ONSA13SW, two broadband stations for the next-generation geodetic VLBI global observing system (VGOS). We used two analysis packages ν Solve to pre-process the data and solve ambiguities, and ASCOT to solve for station positions, including modelling gravitational deformation of the radio telescopes and other significant effects. We obtained weighted root mean square post-fit residuals for each session on the order of 10-15 ps using group-delays and 2-5 ps using phase-delays. The best performance was achieved on the (rather short) baseline between the VGOS stations. As the main result of this work, we determined the coordinates of the Onsala twin telescopes in VTRF2020b with sub-millimetre precision. This new set of coordinates should be used from now on for scheduling, correlation, as a priori for data analyses, and for comparison with classical local-tie techniques. Finally, we find that positions estimated from phase-delays are offset ∼ + 3 mm in the up-component with respect to group-delays. Additional modelling of (elevation dependent) effects may contribute to the future understanding of this offset.Emotion is an instinctive or intuitive feeling as distinguished from reasoning or knowledge. It varies over time, since it is a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others. Since emotions vary over time, it is important to understand and analyze them appropriately. Existing works have mostly focused well on recognizing basic emotions from human faces. However, the emotion recognition from cartoon images has not been extensively covered. Therefore, in this paper, we present an integrated Deep Neural Network (DNN) approach that deals with recognizing emotions from cartoon images. Since state-of-works do not have large amount of data, we collected a dataset of size 8 K from two cartoon characters 'Tom' & 'Jerry' with four different emotions, namely happy, sad, angry, and surprise. The proposed integrated DNN approach, trained on a large dataset consisting of animations for both the characters (Tom and Jerry), correctly identifies the character, segments their face masks, and recognizes the consequent emotions with an accuracy score of 0.96. The approach utilizes Mask R-CNN for character detection and state-of-the-art deep learning models, namely ResNet-50, MobileNetV2, InceptionV3, and VGG 16 for emotion classification. In our study, to classify emotions, VGG 16 outperforms others with an accuracy of 96% and F1 score of 0.85. The proposed integrated DNN outperforms the state-of-the-art approaches.This paper analyzes a large-scale dataset of real-world Wi-Fi operating networks, collected from more than 9,000 access points (APs) for 1 year. The APs are distributed among more than 1,200 educational centers in the context of a nation-wide one-to-one computing program, being most of them primary and secondary schools. The data corresponds to RSSI measurements between APs used to build the conflict graphs for each school Wi-Fi network. We propose a simple embedding for the Wi-Fi network conflict graphs based on classical graph features, which proves to be useful to analyze the behavior of the wireless networks, showing a high discrimination power among the different school networks.  https://www.selleckchem.com/screening/chemical-library.html  Moreover, we discuss some practical applications of the embedding. In particular, it enables to study the Wi-Fi network dynamics at each school, analyzing the conflict graphs temporal variations through clustering techniques. The presented methodology allows us to successfully separate the most stable scenarios from those with more significant variability, which therefore require more technical resources to optimize the network.