Notes

Readiness to have vaccinated in opposition to Covid-19 along with attitudes toward vaccine generally speaking.
Semiconductor saturable absorber mirrors (SESAMs) have enabled a wide variety of modelocked laser systems, which makes measuring their nonlinear properties an important step in laser design. Here, we demonstrate complete characterization of SESAMs using an equivalent time sampling apparatus. The light source is a free-running dual-comb laser, which produces a pair of sub-150-fs modelocked laser outputs at 1051 nm from a single cavity. The average pulse repetition rate is 80.1 MHz, and the full time window is scanned at 240 Hz. Cross-correlation between the beams is used to calibrate the time axis of the measurements, and we use a non-collinear pump-probe geometry on the sample. The measurements enable fast and robust determination of all the nonlinear reflectivity and recovery time parameters of the devices from a single setup, and show good agreement with conventional nonlinear reflectivity measurements. We compare measurements to a rate equation model, showing good agreement up to high pulse fluence values and revealing that the samples tested exhibit a slightly slower recovery at higher fluence values. Lastly, we examine the polarization dependence of the reflectivity, revealing a reduced rollover if cross-polarized beams are used or if the sample is oriented optimally around the beam axis.The pandemic caused by the COVID-19 virus affects the world widely and heavily. When examining the CT, X-ray, and ultrasound images, radiologists must first determine whether there are signs of COVID-19 in the images. That is, COVID-19/Healthy detection is made. The second determination is the separation of pneumonia caused by the COVID-19 virus and pneumonia caused by a bacteria or virus other than COVID-19. This distinction is key in determining the treatment and isolation procedure to be applied to the patient. In this study, which aims to diagnose COVID-19 early using X-ray images, automatic two-class classification was carried out in four different titles COVID-19/Healthy, COVID-19 Pneumonia/Bacterial Pneumonia, COVID-19 Pneumonia/Viral Pneumonia, and COVID-19 Pneumonia/Other Pneumonia. For this study, 3405 COVID-19, 2780 Bacterial Pneumonia, 1493 Viral Pneumonia, and 1989 Healthy images obtained by combining eight different data sets with open access were used. In the study, besides using the original Xt the 3-D CNN architecture can be an important alternative to achieve a high classification result.Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.Convolutional neural networks (CNN) are widely used in computer vision and medical image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly for downsampling the feature maps by aggregating features from local regions. Pooling can help CNN to learn invariant features and reduce computational complexity. Although the max and the average pooling are the widely used ones, various other pooling techniques are also proposed for different purposes, which include techniques to reduce overfitting, to capture higher-order information such as correlation between features, to capture spatial or structural information, etc. As not all of these pooling techniques are well-explored for medical image analysis, this paper provides a comprehensive review of various pooling techniques proposed in the literature of computer vision and medical image analysis. In addition, an extensive set of experiments are conducted to compare a selected set of pooling techniques on two different medical image classification problems, namely HEp-2 cells and diabetic retinopathy image classification. Experiments suggest that the most appropriate pooling mechanism for a particular classification task is related to the scale of the class-specific features with respect to the image size. https://www.selleckchem.com/products/brefeldin-a.html As this is the first work focusing on pooling techniques for the application of medical image analysis, we believe that this review and the comparative study will provide a guideline to the choice of pooling mechanisms for various medical image analysis tasks. In addition, by carefully choosing the pooling operations with the standard ResNet architecture, we show new state-of-the-art results on both HEp-2 cells and diabetic retinopathy image datasets.Burn injuries can decrease the quality of life of a patient tremendously, because of esthetic reasons and because of contractions that result from them. In severe case, skin contraction takes place at such a large extent that joint mobility of a patient is significantly inhibited. In these cases, one refers to a contracture. In order to predict the evolution of post-wounding skin, several mathematical model frameworks have been set up. These frameworks are based on complicated systems of partial differential equations that need finite element-like discretizations for the approximation of the solution. Since these computational frameworks can be expensive in terms of computation time and resources, we study the applicability of neural networks to reproduce the finite element results. Our neural network is able to simulate the evolution of skin in terms of contraction for over one year. The simulations are based on 25 input parameters that are characteristic for the patient and the injury. One of such input parameters is the stiffness of the skin. The neural network results have yielded an average goodness of fit ( R 2 ) of 0.9928 (± 0.0013). Further, a tremendous speed-up of 19354X was obtained with the neural network. We illustrate the applicability by an online medical App that takes into account the age of the patient and the length of the burn.
The online version contains supplementary material available at 10.1007/s00521-021-06772-3.
The online version contains supplementary material available at 10.1007/s00521-021-06772-3.Knowing how school reopenings affect the spread of COVID-19 is crucial when balancing children's right to schooling with contagion management. This paper considers the effects on COVID-19 testing prevalence and the positive test rate of reopening Norwegian schools after a 6-week closure aimed at reducing contagion. We estimate the effects of school reopening on teachers, parents and students using an event study/difference-in-differences design that incorporates comparison groups with minimal exposure to in-person schooling. We find no evidence that COVID-19 incidence increased following reopening among students, parents or teachers pooled across grade levels. We find some suggestive evidence that infection rates among upper secondary school teachers increased; however, the effects are small and transitory. At low levels of contagion, schools can safely be reopened when other social distancing policies remain in place.Using a novel panel survey of relatively poor urban Peruvian adolescents, we explore the link between educational aspirations and propensity to invest in the future. Aspirations comprise hope and agency. We find remarkably high educational aspirations, even among relatively poor individuals and adolescents who were exposed to negative shocks, suggesting high levels of resilience. We also find high occupational aspirations and aspirations to migrate. High-aspiration respondents were also more likely to invest in their education and avoid risky behaviors. These are associations as we do not have enough data to establish causality, although we were able to control for within-person traits. Aspirations are stable over time and positively associated with personality traits such as self-efficacy and life satisfaction, which help explain their persistence over time. Our findings complement those of other recent studies that highlight the role of personality traits in addition to cognitive skills in long-term educational, health, and socioeconomic outcomes.In this paper, we propose a new non-parametric test for equality of distributions. The test is based on the recently introduced measure of (niche) overlap and its rank-based estimator. As the estimator makes only one basic assumption on the underlying distribution, namely continuity, the test is universal applicable in contrast to many tests that are restricted to only specific scenarios. By construction, the new test is capable of detecting differences in location and scale. It thus complements the large class of rank-based tests that are constructed based on the non-parametric relative effect. In simulations this new test procedure obtained higher power and lower type I error compared to two common tests in several settings. The new procedure shows overall good performance. Together with its simplicity, this test can be used broadly.
The online version contains supplementary material available at 10.1007/s00362-021-01239-y.
The online version contains supplementary material available at 10.1007/s00362-021-01239-y.Heatwaves are extreme weather events that have become more frequent and intense in Europe over the past decades. Heatwaves are often coupled to droughts. The combination of them lead to severe ecological and socio-economic impacts. Heatwaves can self-amplify through internal climatic feedback that reduces local precipitation. Understanding the terrestrial sources of local precipitation during heatwaves might help identify mitigation strategies on land management and change that alleviate impacts. Moisture recycling of local water sources through evaporation allows a region to maintain precipitation in the same region or, by being transported by winds, in adjacent regions. To understand the role of terrestrial moisture sources for sustaining precipitation during heatwaves, we backtrack and analyse the precipitation sources of Northern, Western, and Southern sub-regions across Europe during 20 heatwave periods between 1979 and 2018 using the moisture tracking model Water Accounting Model-2layers (WAM-2layers). In Northern and Western Europe, we find that stabilizing anticyclonic patterns reduce the climatological westerly supply of moisture, mainly from the North Atlantic Ocean, and enhances the moisture flow from the eastern Euro-Asian continent and from within their own regions-suggesting over 10% shift of moisture supply from oceanic to terrestrial sources.
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