Measuring and interpreting CO2 fluxes at regional scale: the case of the Apennines, Italy

Tectonically active regions are often characterized by large amounts of carbon dioxide degassing, and estimation of the total CO2 discharged to the atmosphere from tectonic structures, hydrothermal systems and inactive volcanic areas is crucial for the definition of present-day global Earth degassing. The carbon balance of regional aquifers is a powerful tool to quantify the diffuse degassing of deep inorganic carbon sources because the method integrates the CO2 flux over large areas. Its application to peninsular Italy shows that the region is characterized by specific CO2 fluxes higher than the baseline determined for the geothermal regions of the world, and that the amount of endogenous CO2 discharged through diffuse regional degassing (c. 2.1 × 1011 mol a−1) is the major component of the geological CO2 budget of Italy, definitely prevailing over the CO2 discharged by Italian active volcanoes and volcanoes with hydrothermal activity. Furthermore, the positive correlation between geothermal heat and deep CO2 dissolved in the groundwater of central Italy suggests that (1) the geothermal heat is transported into the aquifers by the same hot CO2-rich fluids causing the Italian CO2 anomaly and (2) the advective heat flow is the dominant form of heat transfer of the region. Supplementary material: The location, flow rate, extent of the hydrogeological basin, chemical and isotopic analyses of the 160 springs considered in this study, and the results of the carbon mass balance are reported in a table available at https://doi.org/10.6084/m9.figshare.c.423702

QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum

Function-as-a-Service (FaaS) paradigm is increasingly attractive to bring the benefits of serverless computing to the edge of the network, besides traditional Cloud data centers. However, FaaS adoption in the emerging Cloud-to-Edge Continuum is challenging, mostly due to geographical distribution and heterogeneous resource availability. This emerging landscape calls for effective strategies to trade off low latency at the edge of the network with Cloud resource richness, taking into account the needs of different functions and users. In this paper, we present QoS-aware offloading policies for serverless functions running in the Cloud-to-Edge continuum. We consider heterogeneous functions and service classes, and aim to maximize utility given a monetary budget for resource usage. Specifically, we introduce a two-level approach, where (i) FaaS nodes rely on a randomized policy to schedule every incoming request according to a set of probability values, and (ii) periodically, a linear programming model is solved to determine the probabilities to use for scheduling. We show by extensive simulation that our approach outperforms alternative approaches in terms of generated utility across multiple scenarios. Moreover, we demonstrate that our solution is computationally efficient and can be adopted in large-scale systems. We also demonstrate the functionality of our approach through a proof-of-concept experiment on an open-source FaaS framework

Chemical weathering and consumption of atmospheric carbon dioxide in the Alpine region

To determine the CO2 consumption due to chemical weathering in the Alps, water samples from the 32 main Alpine rivers were collected and analysed in two periods, spring 2011 and winter 2011/2012. Most of the river waters are characterized by a bicarbonate earth-alkaline composition with some samples showing a clear enrich-ment in sulphates and other samples showing a slight enrichment in alkaline metals. The amount of total dissolved solids (TDS) ranges between 96 and 551 mg/L. Considering the major ion composition and the Sr isotopic composition of water samples, coherently with the geological setting of the study area, three major reservoirs of dissolved load have been recognized: carbonates, evaporites and silicates. Based on a chemical mass balance, the flux of dissolved solids, and the flux of carbon dioxide consumed by chemical weathering have been computed for each basin and for the entire study area. Results show that the flux of dissolved solids, ranges from 8 × 103 to 411 × 103 kg km−2 y−1, with an average value of 127 × 103 kg km−2 y−1, while the flux of carbon dioxide consumed by chemical weathering in the short-term (b1 Ma) is 5.03 × 105 mol km−2 y−1 1 on average. Since part of the CO2 is returned to the atmosphere through carbonate precipitation and reverse weathering once river water reaches the ocean, the CO2 removed from the atmosphere/soil system in the long-term (N1 Ma) is much smaller than the CO2 consumed in the short-term and according to our calculations amounts to 2.01 × 104 mol km−2 y−1 on average. This value is almost certainly a minimum estimate of the total amount of CO2 fixed by weathering on the long-term because in our calculations we assumed that all the alkaline metals deriving from rock weathering in the continents are rapidly involved in the process of reverse weathering in the oceans, while there are still large uncertainties on the magnitude and significance of this process. The values of CO2 flux consumed by weathering are strongly correlated with runoff while other potential controlling factors show only weak correlations or no correlation. Our estimation of the CO2 consumed by weathering in the Alpine basins is in the same order of magnitude, but higher than the world average and is consistent with previ-ous estimations made in river basins with similar climatic conditions and similar latitudes

FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum

The widespread adoption of Artificial Intelligence applications to analyze data generated by Internet of Things sensors leads to the development of the edge computing paradigm. Deploying applications at the periphery of the network effectively addresses cost and latency concerns associated with cloud computing. However, it generates a highly distributed environment with heterogeneous devices, opening the challenges of how to select resources and place application components. Starting from a state-of-the-art design-time tool, we present in this paper a novel framework based on Reinforcement Learning, named FIGARO (reinForcement learnInG mAnagement acRoss the computing cOntinuum). It handles the runtime adaptation of a computing continuum environment, dealing with the variability of the incoming load and service times. To reduce the training time, we exploit the design-time knowledge, achieving a significant reduction in the violations of the response time constraint

Carbon degassing through karst hydrosystems of Greece

Estimation of CO2 degassing from active tectonic structures and regional hydrothermal systems is essential for the quantification of presentday Earth degassing [Frondini et al., 2019 and references therein]. Due to the high solubility of CO2 in water, great amounts of deep inorganic carbon can be dissolved, transported, and released from regional aquifers. By applying a massbalance approach [Chiodini et al., 2000], different sources of the dissolved CO2 can be discriminated. The main source of degassing in Greece is concentrated in hydrothermal and volcanic areas. However, deep CO2 from active tectonic areas has not yet been quantified. A key point of this research is to investigate the possible deep CO2 degassing through the big karst aquifers of Greece. From May 2016, 156 karst springs were sampled along the greatest part of the Hellenic region. To discriminate the different carbon sources, we analyzed the chemical and isotopic composition of total dissolved inorganic carbon (TDIC). Results yield TDIC values from 1.89 to 21.7 mmol/l and δ13CTDIC from 16.61 to 0.91 ‰. On this basis, karst springs are clustered into two groups: (a) low TDIC and δ13CTDIC values and (b) intermediate TDIC and δ13CTDIC values. The carbon of the first group derives from organic source and dissolution of carbonates; whilst the second group shows a possible carbon input from deep source. This geogenic carbon is mostly related to high heat flux areas, often near active or recent (Quaternary) volcanic systems

Discharge variations of springs induced by strong earthquakes: the case of the mw 6.5 Norcia event (Italy, October 30 th 2016)

Earthquakes, dynamic strain, groundwater circulation, structural permeability

A game-theoretic approach to computation offloading in mobile cloud computing

We consider a three-tier architecture for mobile and pervasive computing scenarios, consisting of a local tier ofmobile nodes, a middle tier (cloudlets) of nearby computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have practically infinite resources. This architecture has been proposed to get the benefits of computation offloading from mobile nodes to external servers while limiting the use of distant servers whose higher latency could negatively impact the user experience. For this architecture, we consider a usage scenario where no central authority exists and multiple non-cooperative mobile users share the limited computing resources of a close-by cloudlet and can selfishly decide to send their computations to any of the three tiers. We define a model to capture the users interaction and to investigate the effects of computation offloading on the users’ perceived performance. We formulate the problem as a generalized Nash equilibrium problem and show existence of an equilibrium.We present a distributed algorithm for the computation of an equilibrium which is tailored to the problem structure and is based on an in-depth analysis of the underlying equilibrium problem. Through numerical examples, we illustrate its behavior and the characteristics of the achieved equilibria

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

In recent years, the field of Deep Learning has seen many disruptive and impactful advancements. Given the increasing complexity of deep neural networks, the need for efficient hardware accelerators has become more and more pressing to design heterogeneous HPC platforms. The design of Deep Learning accelerators requires a multidisciplinary approach, combining expertise from several areas, spanning from computer architecture to approximate computing, computational models, and machine learning algorithms. Several methodologies and tools have been proposed to design accelerators for Deep Learning, including hardware-software co-design approaches, high-level synthesis methods, specific customized compilers, and methodologies for design space exploration, modeling, and simulation. These methodologies aim to maximize the exploitable parallelism and minimize data movement to achieve high performance and energy efficiency. This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field. In particular, this work complements the previous survey proposed by the same authors in [203], which focuses on Deep Learning hardware accelerators for heterogeneous HPC platforms

Particle profiling of EV‐lipoprotein mixtures by AFM nanomechanical imaging

The widely overlapping physicochemical properties of lipoproteins (LPs) and extracellular vesicles (EVs) represents one of the main obstacles for the isolation and characterization of these pervasive biogenic lipid nanoparticles. We herein present the application of an atomic force microscopy (AFM)-based quantitative morphometry assay to the rapid nanomechanical screening of mixed LPs and EVs samples. The method can determine the diameter and the mechanical stiffness of hundreds of individual nanometric objects within few hours. The obtained diameters are in quantitative accord with those measured via cryo-electron microscopy (cryo-EM); the assignment of specific nanomechanical readout to each object enables the simultaneous discrimination of co-isolated EVs and LPs even if they have overlapping size distributions. EVs and all classes of LPs are shown to be characterised by specific combinations of diameter and stiffness, thus making it possible to estimate their relative abundance in EV/LP mixed samples in terms of stoichiometric ratio, surface area and volume. As a side finding, we show how the mechanical behaviour of specific LP classes is correlated to distinctive structural features revealed by cryo-EM. The described approach is label-free, single-step and relatively quick to perform. Importantly, it can be used to analyse samples which prove very challenging to assess with several established techniques due to ensemble-averaging, low sensibility to small particles, or both, thus providing a very useful tool for quickly assessing the purity of EV/LP isolates including plasma- and serum-derived preparations