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There is increasing evidence linking the mass-extinction event at the Cretaceous-Paleogene boundary to an asteroid impact near Chicxulub, Mexico. Here we use model simulations to explore the combined effect of sulfate aerosols, carbon dioxide and dust from the impact on the oceans and the marine biosphere in the immediate aftermath of the impact. We find a strong temperature decrease, a brief algal bloom caused by nutrients from both the deep ocean and the projectile, and moderate surface ocean acidification. Comparing the modeled longer-term post-impact warming and changes in carbon isotopes with empirical evidence points to a substantial release of carbon from the terrestrial biosphere. Overall, our results shed light on the decades to centuries after the Chicxulub impact which are difficult to resolve with proxy data.
Plain Language Summary The sudden disappearance of the dinosaurs and many other species during the end-Cretaceous mass extinction 66 million years ago marks one of the most profound events in the history of life on Earth. The impact of a large asteroid near Chicxulub, Mexico, is increasingly recognized as the trigger of this extinction, causing global darkness and a pronounced cooling. However, the links between the impact and the changes in the biosphere are not fully understood. Here, we investigate how life in the ocean reacts to the perturbations in the decades and centuries after the impact. We find a short-lived algal bloom caused by the upwelling of nutrients from the deep ocean and nutrient input from the impactor.
Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.
We consider the synchrotron emission from electrons out in the Galactic halo bubble region where the Fermi bubble structures reside.
Utilizing a simple analytical expression for the non-thermal electron distribution and a toy magnetic field model, we simulate polarized synchrotron emission maps at a frequency of 30 GHz.
Comparing these maps with the observational data, we obtain constraints on the parameters of our toy Galactic halo bubble magnetic field model.
Utilizing this parameter value range for the toy magnetic field model, we determine the corresponding range of arrival directions and suppression factors of ultra high energy cosmic rays (UHECRs) from potential local source locations.
We find that high levels of flux suppression (down to 2 per cent) and large deflection angles (>= 80 degrees) are possible for source locations whose line of sight pass through the Galactic halo bubble region.
We conclude that the magnetic field out in the Galactic halo bubble region can strongly dominate the level of deflection UHECRs experience whilst propagating from local sources to Earth.
Temperature impacts on hate speech online: evidence from 4 billion geolocated tweets from the USA
(2022)
Background - A link between weather and aggression in the offline world has been established across a variety of societal settings. Simultaneously, the rapid digitalisation of nearly every aspect of everyday life has led to a high frequency of interpersonal conflicts online. Hate speech online has become a prevalent problem that has been shown to aggravate mental health conditions, especially among young people and marginalised groups.
We examine the effect of temperature on the occurrence of hate speech on the social media platform Twitter and interpret the results in the context of the interlinkage between climate change, human behaviour, and mental health.
Methods - In this quantitative empirical study, we used a supervised machine learning approach to identify hate speech in a dataset containing around 4 billion geolocated tweets from 773 cities across the USA between May 1, 2014 and May 1, 2020.
We statistically evaluated the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models.
Findings - The prevalence of hate tweets was lowest at moderate temperatures (12 to 21?) and marked increases in the number of hate tweets were observed at hotter and colder temperatures, reaching up to 12middot5% (95% CI 8middot0-16middot5) for cold temperature extremes (-6 to -3?) and up to 22middot0% (95% CI 20middot5-23middot5) for hot temperature extremes (42 to 45?). Outside of the moderate temperature range, the hate tweets also increased as a proportion of total tweeting activity. The quasi-quadratic shape of the temperature-hate tweet curve was robust across varying climate zones, income quartiles, religious and political beliefs, and both city-level and state-level aggregations.
However, temperature ranges with the lowest prevalence of hate tweets were centred around the local temperature mean and the magnitude of the increases in hate tweets for hot and cold temperatures varied across the climate zones.
Interpretation - Our results highlight hate speech online as a potential channel through which temperature alters interpersonal conflict and societal aggression. We provide empirical evidence that hot and cold temperatures can aggravate aggressive tendencies online. The prevalence of the results across climatic and socioeconomic subgroups points to limitations in the ability of humans to adapt to temperature extremes.
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustness. Here, we clarify the link between degree distribution entropy and giant component robustness to node removal by showing that the former merely sets a lower bound to the latter for randomly configured networks when no other network characteristics are specified. Furthermore, we show that, for networks of fixed expected degree that follow degree distributions of the same form, the degree distribution entropy is not indicative of robustness. By contrast, we show that the remaining degree entropy and robustness have a positive monotonic relationship and give an analytic expression for the remaining degree entropy of the log-normal distribution. We also show that degree-degree correlations are not by themselves indicative of a network's robustness for real networks. We propose an adjustment to how mutual information is measured which better encapsulates structural properties related to robustness.
We discuss the coherent splitting and recombining of a nanoparticle in a mesoscopic "closed-loop" Stern-Gerlach interferometer in which the observable is the spin of a single impurity embedded in the particle.
This spin, when interacting with a pulsed magnetic gradient, generates the force on the particle.
We calculate the internal decoherence, which arises as the displaced impurity excites internal degrees of freedom (phonons) that may provide WelcherWeg information and preclude interference.
We estimate the constraints this decoherence channel puts on future interference experiments with massive objects. We find that for a wide range of masses, forces, and temperatures, phonons do not inhibit Stern-Gerlach interferometry with micro-scale objects.
However, phonons do constitute a fundamental limit on the splitting of larger macroscopic objects if the applied force induces phonons.
The radiation model is a parameter-free model of human mobility that has been applied primarily for short-distance moves, such as commuting. When applied to migration, it underestimates the number of long-range moves, such as between different US states. Here we show that it additionally suffers from a conceptual inconsistency that can have substantial numerical effects on long-distance moves.
We propose a modification of the radiation model that introduces a dependence on the angle between any two alternative potential destinations, accounting for the possibility that migrants may have preferences about the approximate direction of their move.
We demonstrate that this modification mitigates the conceptual inconsistency and improves the model fit to observational migration data, without introducing any fitting parameters.
Contour scanning and process gas type are process parameters typically considered achieving second order effects compared to first order factors such as laser power and scanning speed.
The present work highlights that contour scanning is crucial to ensure geometrical accuracy and thereby the high performance under uniaxial compression of complex Alloy 718 lattice structures.
Studies of X-ray computed tomography visualizations of as-built and compression-strained structures reveal the continuous and smooth bending and compression of the walls, and the earlier onset of internal contact appearance in the denser lattices printed with contour. In contrast, the effect of addition of He to the Ar process gas appears to have limited influence on the mechanical response of the lattices and their microstructure as characterized by electron backscattered diffraction.
However, the addition of He proved to significantly enhance the cooling rate and to reduce the amount of the generated spatters as evidenced by in situ monitoring of the process emissions, which is very promising for the process stability and powder reusability during laser powder bed fusion.
In this work, we investigate the photo-aquation reaction of the ferrocyanide anion with multi-edge picosecond soft X-ray spectroscopy.
Combining the information of the iron L-edge with nitrogen and oxygen K-edges, we carry out a complete characterization of the bonding channels in the [Fe(CN)(5)(H2O)](3-) photo-product.
We observe clear spectral signatures of covalent bonding between water and the metal, reflecting the mixing of the Fe d(z)(2) orbital with the 3a(1) and 4a(1) orbitals of H2O. Additional fingerprints related to the symmetry reduction and the resulting loss in orbital degeneracy are also reported.
The implications of the elucidated fingerprints in the context of future ultra-fast experiments are also discussed.