New data on geochemical features, fluid mode, age and potential ore content of granitoids of Isherim anticlinorium (North Ural)

Granitoids of Ishirim anticlinorium which is one of the major Precambrian structures of the North Urals, are poorly studied by modern geochemical and isotope-geochronological methods that led to the existence of different points of view on formation conditions and age of these rocks. The authors performed a study of the composition of rocks from three massifs - Vels, Moiva and Pos’mak, by chemical analysis and ICP-MS; age determination on zircons by the methods of LA-ICP-MS and SHRIMP, as well as the study of the composition of rock-forming and accessory minerals using microprobe SX-100, which allowed us to obtain fundamentally new data about the age, fluid regime of formation and potential ore content of granitoids. It is shown that the granitoids were probably formed in environments of active continental margin and orogen; the first has the Ediacaran (567.2-558 Ma), the second - Cambrian (530.3-511.1 Ma) age. Discrete intervals of the formation and a fairly significant geochemical differences of Precambrian and Paleozoic granites, allow to attribute them to different complexes - the Ediacaran Moiva complex and Cambrian Vels complex. The complexes are different in composition of fluids which change over time from substantially chlorine to fluorine. With more ancient (Ediacaran) granitoids of Moiva massif can be associated gold-bearing Mo-W mineralization, and with Cambrian granites - rare-metal mineralization (W, Nb, Ta, REE)

Review of deep learning approaches in solving rock fragmentation problems

One of the most significant challenges of the mining industry is resource yield estimation from visual data. An example would be identification of the rock chunk distribution parameters in an open pit. Solution of this task allows one to estimate blasting quality and other parameters of open-pit mining. This task is of the utmost importance, as it is critical to achieving optimal operational efficiency, reducing costs and maximizing profits in the mining industry. The mentioned task is known as rock fragmentation estimation and is typically tackled using computer vision techniques like instance segmentation or semantic segmentation. These problems are often solved using deep learning convolutional neural networks. One of the key requirements for an industrial application is often the need for real-time operation. Fast computation and accurate results are required for practical tasks. Thus, the efficient utilization of computing power to process high-resolution images and large datasets is essential. Our survey is focused on the recent advancements in rock fragmentation, blast quality estimation, particle size distribution estimation and other related tasks. We consider most of the recent results in this field applied to open-pit, conveyor belts and other types of work conditions. Most of the reviewed papers cover the period of 2018-2023. However, the most significant of the older publications are also considered. A review of publications reveals their specificity, promising trends and best practices in this field. To place the rock fragmentation problems in a broader context and propose future research topics, we also discuss state-of-the-art achievements in real-time computer vision and parallel implementations of neural networks

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Abstract Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m² and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology