Carbon budget and national gross domestic product in the framework of the Paris Climate Agreement

Abstract In 2015 an unprecedented effort was made in Paris by the countries adhering to the United Nations Framework Convention on Climate Change to decrease the CO2 emissions due to the close relationships of greenhouse gases with global warming. Under the previous Kyoto Protocol, only advanced countries were committed to reduce greenhouse emissions while under the Paris Climate Agreement all countries were committed to fight against global warming. The urgency of real action has been prompted by extreme events like bushfires, heatwaves, and the ongoing pandemic. Given the strong commitments, it looks interesting, seen that all countries are involved, to verify if any sustainability pattern is evident. Our approach is encouraging, as the downward emission trend shows a high increase in sustainability between 2027 and 2037. Without exacerbating the climate discussion, we used the national gross domestic product (hereafter: GDP) as environmental indicator to propose for the first time an allometric ranking of countries that need to change drastically their energy policy to meet their climate commitments. Any sustainability downturn in one country, especially if advanced, might rationally bring concern about the actual prospects of other countries which are all committed to the Paris Climate Agreement. But the departure from the annual allometric model GDP−CO2 may be much greater than can be accounted for by statistical expectations, as for Ukraine, Uzbekistan and Turkmenistan that are entering a sustainable condition where their CO2 emissions will be lower than they would have been without the Paris Climate Agreement

Sedation and weaning from mechanical ventilation: time for ‘best practice’ to catch up with new realities?

Delivery of sedation in anticipation of weaning of adult patients from prolonged mechanical ventilation is an arena of critical care medicine where opinion-based practice is currently hard to avoid because robust evidence is lacking. We offer some views on this subject, hoping to stimulate debate among colleagues

Measuring the shape. Performance evaluation of a photogrammetry improvement applied to the Neanderthal skull Saccopastore 1

Several digital technologies are nowadays developed and applied to the study of the human fossil record. Here, we present a low-cost hardware implementation of the digital acquisition via photogrammetry, applied to a specimen of paleoanthropological interest: the Neanderthal skull Saccopastore 1. Such implementation has the purpose to semi-automatize the procedures of digital acquisition, by the introduction of an automatically rotating platform users can easily build on their own with minimum costs. We provide all the technical specifications, mostly based on the Arduino UNO™ microcontroller technology, and evaluate the performance and the resolution of the acquisition by comparing it with the CT-scan of the same specimen through the calculation of their shape differences. In our opinion, the replication of the automatic rotating platform, described in this work, may contribute to the improvement of the digital acquisition processes and may represent, in addition, a useful and affordable tool for both research and dissemination

Diluted porcine surfactant lung lavages in children with severe ARDS

Acute respiratory distress syndrome (ARDS) is characterized by damage to the arteriolar-capillary endothelium and alveolar epithelium that leads to surfactant deficiency and atelectasis. Alveolar collapse and pulmonary edema will further induce surfactant inactivation. Surfactant supplementation has been suggested but results are unpredictable. Poor response may be due to inhibition of administered surfactant by plasma components filling the alveolar space, severity of lung injury, time of surfactant application and inadequate dose. We report the course of gas exchange and pulmonary mechanics after instillation of surfactant in 14 children (3 months-7 years) with severe ARDS, defined as an oxygenation index (OI) > 30 and a partial pressure of oxygen/ fraction of Inspired oxygen (PaO2/FiO2) <150 . We used a diluted concentration of Curosurf (8 mg/ml) divided into 4 aliquots, for a total dose of 25 mg/kg. An additional aliquot was used for bronchoalveolar lavage before surfactant treatment. All children showed a dramatic response to surfactant with rapid and progressive increase in compliance and improvement of all respiratory mechanics. Mechanical ventilation settings were rapidly reduced and gas exchange improved with a PaO2/FiO2 >200 for more than 12 hours. Diluted surfactant lung lavages were able to increase blood gas exchange in all our patients despite previously severe gas exchange impairment

Exogenous Surfactant Treatment in Children with ARDS

Since the Food and Drug Administration (FDA) approved exogenous surfactant in the early 90s for the treatment of neonates with Hyaline Membrane Disease (HMD), many studies have focused on enlarging its indications for others types of lung injuries and for other age groups. Although in the past 20 years no studies have shown clear results about the efficacy of exogenous surfactant treatment in paediatric Acute Respiratory Distress Syndrome (ARDS), many of them were able to point out and better define very important aspects of this treatment like dosage, timing, ways of administration and usage of different types of surfactant (natural and synthetic). In this review we retrace the development of studies looking at the role of exogenous surfactant treatment in paediatric ARDS

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of PDEs for multiple combinations of control parameters and initial conditions. Therefore, constructing efficient reduced order models (ROMs) that enable accurate but fast predictions, while retaining the dynamical characteristics of the physical phenomenon as parameters vary, is of paramount importance. In this work, a data-driven, non-intrusive framework which combines ROM construction with reduced dynamics identification, is presented. Starting from a limited amount of full order solutions, the proposed approach leverages autoencoder neural networks with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. This model can be queried to efficiently compute full-time solutions at new parameter instances, as well as directly fed to continuation algorithms. These aim at tracking the evolution of periodic steady-state responses as functions of system parameters, avoiding the computation of the transient phase, and allowing to detect instabilities and bifurcations. Featuring an explicit and parametrized modeling of the reduced dynamics, the proposed data-driven framework presents remarkable capabilities to generalize with respect to both time and parameters. Applications to structural mechanics and fluid dynamics problems illustrate the effectiveness and accuracy of the proposed method