A Method for Magma Viscosity Assessment by Lava Dome Morphology

Lava domes form when a highly viscous magma erupts on the surface. Several types of lava dome morphology can be distinguished depending on the flow rate and the rheology of magma: obelisks, lava lobes, and endogenic structures. The viscosity of magma nonlinearly depends on the volume fraction of crystals and temperature. Here we present an approach to magma viscosity estimation based on a comparison of observed and simulated morphological forms of lava domes. We consider a two-dimensional axisymmetric model of magma extrusion on the surface and lava dome evolution, and assume that the lava viscosity depends only on the volume fraction of crystals. The crystallization is associated with a growth of the liquidus temperature due to the volatile loss from the magma, and it is determined by the characteristic time of crystal content growth (CCGT) and the discharge rate. Lava domes are modeled using a finite-volume method implemented in Ansys Fluent software for various CCGTs and volcanic vent sizes. For a selected eruption duration a set of morphological shapes of domes (shapes of the interface between lava dome and air) is obtained. Lava dome shapes modeled this way are compared with the observed shape of the lava dome (synthesized in the study by a random modification of one of the calculated shapes). To estimate magma viscosity, the deviation between the observed dome shape and the simulated dome shapes is assessed by three functionals: the symmetric difference, the peak signal-to-noise ratio, and the structural similarity index measure. These functionals are often used in the computer vision and in image processing. Although each functional allows to determine the best fit between the modeled and observed shapes of lava dome, the functional based on the structural similarity index measure performs it better. The viscosity of the observed dome can be then approximated by the viscosity of the modeled dome, which shape fits best the shape of the observed dome. This approach can be extended to three-dimensional case studies to restore the conditions of natural lava dome growth

IUGG in the 21st century

The International Union of Geodesy and Geophysics (IUGG) has vigorously responded to a number of the natural, scientific, and technological challenges and driving forces that have marked the 21st century thus far. This paper reviews the actions of the Union that were precipitated by disasters caused by natural hazard events, climatic and environmental changes, and important scientific advances, as well as the opportunities to support International Years and other cooperative programs. This period has also given rise to a number of structural changes within the Union. IUGG added an eighth association, the International Association of Cryospheric Sciences, and inaugurated the new categories of affiliate and honorary memberships, introduced new grants, science education, and recognition programs, and formed new Union commissions on climatic and environmental change, data and information, planetary sciences, and a working group on history. Electronic communication was welcomed as a cultural norm. Overall, the development of the scientific landscape in the 21st century and a healthy future for the Union requires emphasis on fundamental Earth and space sciences as well as on transdisciplinary science to resolve urgent problems of society. IUGG will continue to evolve throughout the coming decades in step with the changing world of science and its international organizations, by responding to challenging problems as they arise.</p

Geodynamics, seismicity, and seismic hazards of the Caucasus

Being a part of ongoing continental collision between the Arabian and Eurasian plates, the Caucasus region is a remarkable site of moderate to strong seismicity, where devastating earthquakes caused significant losses of lives and livelihood. In this article, we survey geology and geodynamics of the Caucasus and its surroundings; magmatism and heat flow; active tectonics and tectonic stresses caused by the collision and shortening; gravity and density models; and overview recent geodetic studies related to regional movements. The tectonic development of the Caucasus region in the Mesozoic-Cenozoic times as well as the underlying dynamics controlling its development are complicated processes. It is clear that the collision is responsible for a topographic uplift / inversion and for the formation of the fold-and-thrust belts of the Greater and Lesser Caucasus. Tectonic deformations in the region is influenced by the wedge-shaped rigid Arabian block indenting into the relatively mobile region and producing near N-S compressional stress and seismicity in the Caucasus. Regional seismicity is analysed with an attention to sub-crustal seismicity under the northern foothills of the Greater Caucasus, which origin is unclear – whether the seismicity associated with a descending oceanic crust or thinned continental crust. Recent seismic tomography studies are in favour of the detachment of a lithospheric root beneath the Lesser and Greater Caucasus. The knowledge of geodynamics, seismicity, and stress regime in the Caucasus region assists in an assessment of seismic hazard and risk. We look finally at existing gaps in the current knowledge and identify the problems, which may improve our understanding of the regional evolution, active tectonics, geodynamics, shallow and deeper seismicity, and surface manifestations of the lithosphere dynamics. Among the gaps are those related to uncertainties in regional geodynamic and tectonic evolution (e.g., continental collision and associated shortening and exhumation, lithosphere structure, deformation and strain-stress partitioning) and to the lack of comprehensive datasets (e.g., regional seismic catalogues, seismic, gravity and geodetic surveys)

A Method for Magma Viscosity Assessment by Lava Dome Morphology

Abstract: Lava domes form when a highly viscous magma erupts on the surface. Several types of lava dome morphology can be distinguished depending on the flow rate and the rheology of magma: obelisks, lava lobes, and endogenic structures. The viscosity of magma nonlinearly depends on the volume fraction of crystals and temperature. Here we present an approach to magma viscosity estimation based on a comparison of observed and simulated morphological forms of lava domes. We consider a two-dimensional axisymmetric model of magma extrusion on the surface and lava dome evolution, and assume that the lava viscosity depends only on the volume fraction of crystals. The crystallization is associated with a growth of the liquidus temperature due to the volatile loss from the magma, and it is determined by the characteristic time of crystal content growth (CCGT) and the discharge rate. Lava domes are modeled using a finite-volume method implemented in Ansys Fluent software for various CCGTs and volcanic vent sizes. For a selected eruption duration a set of morphological shapes of domes (shapes of the interface between lava dome and air) is obtained. Lava dome shapes modeled this way are compared with the observed shape of the lava dome (synthesized in the study by a random modification of one of the calculated shapes). To estimate magma viscosity, the deviation between the observed dome shape and the simulated dome shapes is assessed by three functionals: the symmetric difference, the peak signal-to-noise ratio, and the structural similarity index measure. These functionals are often used in the computer vision and in image processing. Although each functional allows to determine the best fit between the modeled and observed shapes of lava dome, the functional based on the structural similarity index measure performs it better. The viscosity of the observed dome can be then approximated by the viscosity of the modeled dome, which shape fits best the shape of the observed dome. This approach can be extended to three-dimensional case studies to restore the conditions of natural lava dome growth. © 2021, The Author(s).We are grateful to two anonymous reviewers for their constructive comments. Numerical experiments were carried out on the Uran computing cluster (Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences, Yekaterinburg). The work was supported by the Russian Science Foundation (project no. 19-17-00027)

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future

Longitudinal Molecular Trajectories of Diffuse Glioma in Adults

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear ¹² . Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of difuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specifc gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at diferent rates across the glioma subtypes, and hypermutation was not associated with diferences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

Disposable sensors in diagnostics, food and environmental monitoring

Disposable sensors are low‐cost and easy‐to‐use sensing devices intended for short‐term or rapid single‐point measurements. The growing demand for fast, accessible, and reliable information in a vastly connected world makes disposable sensors increasingly important. The areas of application for such devices are numerous, ranging from pharmaceutical, agricultural, environmental, forensic, and food sciences to wearables and clinical diagnostics, especially in resource‐limited settings. The capabilities of disposable sensors can extend beyond measuring traditional physical quantities (for example, temperature or pressure); they can provide critical chemical and biological information (chemo‐ and biosensors) that can be digitized and made available to users and centralized/decentralized facilities for data storage, remotely. These features could pave the way for new classes of low‐cost systems for health, food, and environmental monitoring that can democratize sensing across the globe. Here, a brief insight into the materials and basics of sensors (methods of transduction, molecular recognition, and amplification) is provided followed by a comprehensive and critical overview of the disposable sensors currently used for medical diagnostics, food, and environmental analysis. Finally, views on how the field of disposable sensing devices will continue its evolution are discussed, including the future trends, challenges, and opportunities