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The Tissint Martian meteorite is an unusual depleted olivine-phyric shergottite, reportedly sourced from a mantle-derived melt within a deep magma chamber. Here, we report major and trace element data for Tissint olivine and pyroxene, and use these data to provide new insights into the dynamics of the Tissint magma chamber. The presence of irregularly spaced oscillatory phosphorous (P)-rich bands in olivine, along with geochemical evidence indicative of a closed magmatic system, implies that the olivine grains were subject to solute trapping caused by vigorous crystal convection within the Tissint magma chamber. Calculated equilibration temperatures for the earliest crystallizing (antecrystic) olivine cores suggest a Tissint magma source temperature of 1680°C, and a local Martian mantle temperature of 1560°C during the late Amazonian-the latter being consistent with the ambient mantle temperature of Archean Earth. Mineral Chemistry as a Probe for Martian Magma Dynamics Paleo-heat flows deduced from lithospheric strength suggest that the "stagnant-lid" tectonic regime of Mars has enabled mantle heat retention for the majority of the planet's history (

The Marsili volcano is the largest known seamount in Europe, located in the Marsili Basin (Aeolian Arc, Tyrrhenian Sea, Italy). The Marsili seamount shows a high probability to generate a volcanogenic tsunami in the near future, and the coasts of Southern Italy could be affected by this event. We conducted a qualitative risk perception analysis by distributing a questionnaire at the population from three different regions that are surrounded by the Tyrrhenian Sea. Data from questionnaires were analyzed in order to understand the tsunami risk perception of the population. Our data were compared with a probabilistic tsunami scenario due to a Marsili seamount flank collapse. Results underlined the need for a proposed campaign that aimed at informing the Southern Italy population about tsunami risk and the phenomena that can potentially generate a tsunami event.

This study aims to analyze the comparative performance of pandemic dynamics prediction methods on the island of Java, based on data from March to May 2020 covering the provinces of DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java. The prediction uses Knowledge Growing System (KGS) and time series models, namely Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the Mean Absolute Percentage Error (MAPE) computational results, the EMA method produces a lower error rate than the SMA method with 47.94 % on average. The KGS prediction with a Degree of Certainty (DoC) produced a trend analysis that the pandemic dynamics in DKI Jakarta province will decrease gradually if the current policy is still implemented. Whereas in the other provinces, the KGS predicted the pandemic dynamics trends will still increase.