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We present PolyHoop, a lightweight standalone C++ implementation of a mechanical model to simulate the dynamics of soft particles and cellular tissues in two dimensions. With only few geometrical and physical parameters, PolyHoop is capable of simulating a wide range of particulate soft matter systems: from biological cells and tissues to vesicles, bubbles, foams, emulsions, and other amorphous materials. The soft particles or cells are represented by continuously remodeling, non-convex, high-resolution polygons that can undergo growth, division, fusion, aggregation, and separation. With PolyHoop, a tissue or foam consisting of a million cells with high spatial resolution can be simulated on conventional laptop computers. Computer Physics Communications, 299 ISSN:0010-4655 ISSN:1879-2944

The Tokaj Mts. volcanism occurred in a thinning continental lithosphere regime at the final stage of the subduction process. Using high-precision zircon U-Pb dating, four major explosive eruption events were distinguished. Among them the 13.1 Ma Sátoraljaújhely and the 12.0 Ma Szerencs eruptions could have yielded large amount of volcanic material (possibly > 100 km3) and they were associated with caldera collapse as shown by the several hundred-metre-thick pyroclastic deposits and the long (>100 km) runout pyroclastic flow in case of the 13.1 Ma eruption. The 12.3 Ma Hegyköz and the 11.6 Ma Vizsoly eruptions were relatively smaller. The volcanic products can be readily distinguished by zircon and glass trace elements and trace element ratios, which can be used for fingerprinting and to correlate with distal deposits. The Rb, Ba, Sr content and strong negative Eu-anomaly of the glasses reflect extreme crystal fractionation, particularly for the Szerencs rhyolitic magma. The silicic volcanic products of the Tokaj Mts. show compositional similarities with the so-called ‘dry–reduced–hot’ rhyolite type consistent with an origin in an extensional environment, where the primary magmas were formed by near-adiabatic decompression melting in the mantle with subordinate fluid flux. In contrast, some of the older Bükkalja rhyolitic magmas evolved via more hydrous evolutionary paths, where amphibole played a role in the control of the trace element budget. The significant increase of zircon ε Hf values from −8.8 to + 0.2 in the rhyolitic pyroclastic rocks of Tokaj Mts. with time implies that mantle-derived magmas became more dominant. This can be explained by the specific tectonic setting, i.e. the final stage of subduction when the descending subducted slab became almost vertical, which exerted a pull in the upper lithosphere leading to thinning and accelerated subsidence as well as asthenospheric mantle flow just before the slab detachment. Gondwana Research, 130 ISSN:1342-937X ISSN:1878-0571

micrOMEGAs is a numerical code to compute dark matter (DM) observables in generic extensions of the Standard Model of particle physics. We present a new version of micrOMEGAs that includes a generalization of the Boltzmann equations governing the DM cosmic abundance evolution which can be solved to compute the relic density of N-component DM. The direct and indirect detection rates in such scenarios take into account the relative contribution of each component such that constraints on the combined signal of all DM components can be imposed. The co-scattering mechanism for DM production is also included, whereas the routines used to compute the relic density of feebly interacting particles have been improved in order to take into account the effect of thermal masses of t-channel particles. Finally, the tables for the DM self-annihilation - induced photon spectra have been extended down to DM masses of 110 MeV, and they now include annihilation channels into light mesons. 39 pages, 1 figure

Given the global biodiversity crisis, there is an urgent need for new tools to monitor populations of endangered marine megafauna, like sharks. To this end, Baited Remote Underwater Video Stations (BRUVS) stand as the most effective tools for estimating shark abundance, measured using the MaxN metric. However, a bottleneck exists in manually computing MaxN from extensive BRUVS video data. Although artificial intelligence methods are capable of solving this problem, their effectiveness is tested using AI metrics such as the F-measure, rather than ecologically informative metrics employed by ecologists, such as MaxN. In this study, we present both an automated and a semi-automated deep learning approach designed to produce the MaxN abundance metric for three distinct reef shark species: the grey reef shark (Carcharhinus amblyrhynchos), the blacktip reef shark (C. melanopterus), and the whitetip reef shark (Triaenodon obesus). Our approach was applied to one-hour baited underwater videos recorded in New Caledonia (South Pacific). Our fully automated model achieved F-measures of 0.85, 0.43, and 0.72 for the respective three species. It also generated MaxN abundance values that showed a high correlation with manually derived data for C. amblyrhynchos (R = 0.88). For the two other species, correlations were significant but weak (R = 0.35–0.44). Our semi-automated method significantly enhanced F-measures to 0.97, 0.86, and 0.82, resulting in high-quality MaxN abundance estimations while drastically reducing the video processing time. To our knowledge, we are the first to estimate MaxN with a deep-learning approach. In our discussion, we explore the implications of this novel tool and underscore its potential to produce innovative metrics for estimating fish abundance in videos, thereby addressing current limitations and paving the way for comprehensive ecological assessments.