About one percent of giants[1] are detected to have anomalously high lithium (Li) abundances in their atmospheres, conflicting directly with the prediction of the standard stellar evolution models [2] , and making the production and evolution of Li more intriguing, not only in the sense of the Big Bang nucleosynthesis [3,4] or the Galactic medium [5] , but also the evolution of stars. [6,7,8] , yet the origins of Li-rich giants are still being debated. Here we report the discovery of the most Li-rich giant known to date, with a super-high Li abundance of 4.51. This rare phenomenon was snapshotted together with another short-term event that the star is experiencing its luminosity bump on the red giant branch. Such high Li abundance indicates that the star might be at the very beginning of its Li-rich phase, which provides a great opportunity to investigate the origin and evolution of Li in the Galaxy. A detailed nuclear simulation is presented with up-to-date reaction rates to recreate the Li enriching process in this star. Our results provide tight constraints on both observational and theoretical points of view, suggesting that low-mass giants can produce Li inside themselves to a super high level via 7 Be transportation during the red giant phase. Decades of efforts have been put into explaining why such outliers existLithium is too fragile to survive in deeper layers of a stellar atmosphere due to the high temperature. Thus the first dredge up (FDU) process can sharply dilute the surface Li abundance in red giants. That explains why the first discovery [9] of a Li-rich giant evoked great interests on exploring and understanding the Li-rich objects. However, only about 150 Li-rich giants have been found [1,10,11,12,13,14] in the past three decades, and ∼ 20 of them were found to be super Li-rich with Li abundances higher than 3.3. Considering the NLTE corrections, three [12,15,16] stars were found to be at a level of A(Li) > 4.0. Such rare objects could provide a great opportunity to reveal the nature of the phenomenon of Li-richness because high Li abundance cannot be maintained for a long time due to frequent convection activity. Taking advantage of the powerful ability for spectral collection with the Large Sky Area Multi-Object * sjr@nao.cas.cn † gzhao@nao.cas.cn 1 Fiber Spectroscopy Telescope (LAMOST), we have obtained a large sample of Li-rich candidates by measuring the equivalent width of the Li I line at λ = 6707.8 Å. One of our candidates, TYC 429-2097-1, has a super strong Li absorption line (see Fig. 1, panel a). We then made a follow-up high-resolution observation with the 2.4-m Automated Planet Finder Telescope (APF) located at Lick Observatory on June 23, 2015. The spectrum covers a wavelength range of 374 nm − 970 nm with a resolution of ∼ 80, 000. The total integration time was 1.5 hours and was divided into three single exposures (30 minutes each) for a better subtraction of cosmic-rays. The spectrum of TYC 429-2097-1 obtained from APF is presented in Fig. 1, panels (b) and (e), where the spec...

The dynamics of the spreading of the COVID-19 virus has similar features to turbulent flow, chaotic maps, and other non-linear systems: a small seed grows exponentially and eventually saturates. Like in the percolation model, the virus is most dangerous if the probability of transmission (or the bond probability p in the percolation model) is high. This suggests a relation with the population density, ρ s , which must be higher than a certain value (ρ s > 1,000 persons/km 2). A "seed' implanted in such populations grows vigorously and affects nearby places at distance x. Thus, the spreading is governed by the ratio ρ = ρ s /x. Assuming a power law dependence τ of the number of positives to the virus N + from ρ, we find τ = 0.55, 0.75, and 0.96 for South Korea, Italy, and China, respectively.

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A model based on chaotic maps and turbulent flows is applied to the spread of Coronavirus for each Italian region in order to obtain useful information and help to contrast it. We divide the regions into different risk categories and discuss anomalies. The worst cases are confined between the Appenine and the Alps mountain ranges but the situation seem to improve closer to the sea. The Veneto region gave the most efficient response so far and some of their resources could be diverted to other regions, in particular more tests to the Lombardia, Liguria, Piemonte, Marche and V. Aosta regions, which seem to be worst affected. We noticed worrying anomalies in the Lazio, Campania and Sicilia regions to be monitored. We stress that the number of fatalities we predicted on March 12 has been confirmed daily by the bulletins. This suggests a change of strategy in order to reduce such number maybe moving the weaker population (and negative to the virus test) to beach resorts, which should be empty presently. The ratio deceased/positives on April 4, 2020 is 5.4% worldwide, 12.3% in Italy, 1.4% in Germany, 2.7% in the USA, 10.3% in the UK and 4.1% in China. These large fluctuations should be investigated starting from the Italian regions, which show similar large fluctuations.

A model based on population growth, chaotic maps, and turbulent flows is applied to the spread of Coronavirus for each Italian region in order to obtain useful information and help to contrast it. We divide the regions into different risk categories and discuss anomalies. The worst cases are confined between the Appenine and the Alps mountain ranges but the situation seem to improve closer to the sea. The Veneto region gave the most efficient response so far and some of their resources could be diverted to other regions, in particular, more tests to the Lombardia, Liguria, Piemonte, Marche and V. Aosta regions, which seem to be worst affected. We noticed worrying anomalies in the Lazio, Campania and Sicilia regions to be monitored. We stress that the number of fatalities we predicted on March 12 has been confirmed daily by the bulletins. This suggests a change of strategy in order to reduce such number maybe moving the weaker population (and negative to the virus test) to beach resorts, which should be empty presently. The ratio deceased/positives on April 4, 2020 is 5.4% worldwide, 12.3% in Italy, 1.4% in Germany, 2.7% in the USA, 10.3% in the UK and 4.1% in China. These large fluctuations should be investigated starting from the Italian regions, which show similar large fluctuations.