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Fc-conjugated C-type lectin receptors: instruments with regard to comprehension number pathogen interactions.
A clear understanding of community response to government decisions is crucial for policy makers and health officials during the COVID-19 pandemic. In this study, we document the determinants of implementation and compliance with stay-at-home orders in the USA, focusing on trust and social capital. Using cell phone data measuring changes in non-essential trips and average distance traveled, we find that mobility decreases significantly more in high-trust counties than in low-trust counties after the stay-at-home orders are implemented, with larger effects for more stringent orders. We also provide evidence that the estimated effect on post-order compliance is especially large for confidence in the press and governmental institutions, and relatively smaller for confidence in medicine and in science.Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The "official" approach adopted by public institutions to estimate the "excess mortality" during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in "ordinary" years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.
The online version contains supplementary material available at 10.1007/s00148-021-00857-y.
The online version contains supplementary material available at 10.1007/s00148-021-00857-y.Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.The Western homicide drop is a known fact, but the reasons behind the drop have so far mainly focused on macro explanations. In this study, we argue that to understand the homicide drop, it is necessary to first explore whether the drop is general or specific. We do this by examining the subtypes of homicide together with perpetrator and victim demographic characteristics. This study seeks to describe the nature and scope of homicidal violence in the period 1992-2016 in the Netherlands, disaggregating by subtype of homicide, and perpetrator and victim gender constellation and age. In doing so, we make use of the Dutch Homicide Monitor. Findings show that the Dutch homicide drop is significantly related to homicides resulting from disputes and robberies and intimate partner homicides. The gender constellation and age distribution in all homicide types are further explored. This study highlights the importance of disaggregating data by subtype in unravelling the homicide drop.Forecasting accurate Value-at-Risk (VaR) estimations is a crucial task in applied financial risk management. Even though there have been significant advances in the field of financial econometrics, many crises have been documented throughout the world in the last decades. An explanation for this discrepancy is that many contemporary models are too complex and cannot be easily understood and implemented in the financial industry (Fama in Financ Anal J 5175-80, 1995; Ross in AIMR conference proceedings, vol. 1993, no. 6, pp. 11-15, Association for Investment Management and Research, 1993). In order to bridge this theory-practice gap, we present a computational method based on the leverage effect. This method allows us to focus on financial theory and remove complexity. Examining the US stock market (2000-2020), we provide empirical evidence that our newly suggested approach, which uses only the most appropriate observation period, significantly increases the accuracy of the Conventional Delta Normal VaR model and generates VaR estimations which are as accurate as those of advanced econometric models, such as GARCH(1,1).In this paper, we have considered a deterministic epidemic model with logistic growth rate of the susceptible population, non-monotone incidence rate, nonlinear treatment function with impact of limited hospital beds and performed control strategies. The existence and stability of equilibria as well as persistence and extinction of the infection have been studied here. Cpd 20m order We have investigated different types of bifurcations, namely Transcritical bifurcation, Backward bifurcation, Saddle-node bifurcation and Hopf bifurcation, at different equilibrium points under some parametric restrictions. Numerical simulation for each of the above-defined bifurcations shows the complex dynamical phenomenon of the infectious disease. Furthermore, optimal control strategies are performed using Pontryagin's maximum principle and strategies of controls are studied for two infectious diseases. Lastly using efficiency analysis we have found the effective control strategies for both cases.
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