The evolution and future of influenza pandemic preparedness

The influenza virus is a global threat to human health causing unpredictable yet recurring pandemics, the last four emerging over the course of a hundred years. As our knowledge of influenza virus evolution, distribution, and transmission has increased, paths to pandemic preparedness have become apparent. In the 1950s, the World Health Organization (WHO) established a global influenza surveillance network that is now composed of institutions in 122 member states. This and other surveillance networks monitor circulating influenza strains in humans and animal reservoirs and are primed to detect influenza strains with pandemic potential. Both the United States Centers for Disease Control and Prevention and the WHO have also developed pandemic risk assessment tools that evaluate specific aspects of emerging influenza strains to develop a systematic process of determining research and funding priorities according to the risk of emergence and potential impact. Here, we review the history of influenza pandemic preparedness and the current state of preparedness, and we propose additional measures for improvement. We also comment on the intersection between the influenza pandemic preparedness network and the current SARS-CoV-2 crisis. We must continually evaluate and revise our risk assessment and pandemic preparedness plans and incorporate new information gathered from research and global crises.Subject terms: Influenza virus, Infectious diseases     

Rehabilitation of the Gauss-Jordan algorithm

Summary In this paper a Gauss-Jordan algorithm with column interchanges is presented and analysed. We show that, in contrast with Gaussian elimination, the Gauss-Jordan algorithm has essentially differing properties when using column interchanges instead of row interchanges for improving the numerical stability. For solutions obtained by Gauss-Jordan with column interchanges, a more satisfactory bound for the residual norm can be given. The analysis gives theoretical evidence that the algorithm yields numerical solutions as good as those obtained by Gaussian elimination and that, in most practical situations, the residuals are equally small. This is confirmed by numerical experiments. Moreover, timing experiments on a Cyber 205 vector computer show that the algorithm presented has good vectorisation properties.     

Investigation of organic impurities adsorbed on and incorporated into electroplated copper layers

At room temperature electroplated copper layers exhibit changes in resistivity, residual stress, and microstructure. This process, known as self-annealing, is intimately linked to the release of organic impurities, which stem from the incorporation of organic additives into the Cu layer in the course of the electroplating process. The behavior of these impurities during self-annealing, represented by the carbon content, could be detected by analytical radio frequency glow discharge optical emission spectrometry (GD-OES) and carrier gas hot extraction (CGHE). The precondition of a quantitative determination is a surface cleaning procedure to remove adsorbed organics from the copper surface. It was observed that at first almost all impurities have to leave the Cu metallization before an accelerated abnormal grain growth can start. The small amount of remaining organic species after self-annealing could be quantified by both examination techniques, GD-OES and CGHE.     

Uran im Quellengebiete von Franzensbad

Ohne Zusammenfassung     

Improving Construction for Connected Dominating Set with Steiner Tree in Wireless Sensor Networks

The connected dominating set plays an important role in ad hoc wireless networking. Many constructions for approximating the minimum connected dominating set have been proposed in the literature. In this paper, we propose a new one with Steiner tree, which produces approximation solution within a factor of 6.8 from optimal. This approximation algorithm can also be implemented distributedly.