Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Studies of climate change impacts on agricultural land use generally consider sets of climates combined with fixed socio-economic scenarios, making it impossible to compare the impact of specific factors within these scenario sets. Analysis of the impact of specific scenario factors is extremely difficult due to prohibitively long run-times of the complex models. This study produces and combines metamodels of crop and forest yields and farm profit, derived from previously developed very complex models, to enable prediction of European land use under any set of climate and socio-economic data. Land use is predicted based on the profitability of the alternatives on every soil within every 10' grid across the EU. A clustering procedure reduces 23,871 grids with 20+ soils per grid to 6,714 clusters of common soil and climate. Combined these reduce runtime 100 thousand-fold. Profit thresholds define land as intensive agriculture (arable or grassland), extensive agriculture or managed forest, or finally unmanaged forest or abandoned land. The demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. An iteration adjusts prices to meet these constraints. A range of measures are derived at 10' grid-level such as diversity as well as overall EU production. There are many ways to utilise this ability to do rapidWhat-If analysis of both impact and adaptations. The paper illustrates using two of the 5 different GCMs (CSMK3, HADGEM with contrasting precipitation and temperature) and two of the 4 different socio-economic scenarios ("We are the world", "Should I stay or should I go" which have contrasting demands for land), exploring these using two of the 13 scenario parameters (crop breeding for yield and population) . In the first scenario, population can be increased by a large amount showing that food security is far from vulnerable. In the second scenario increasing crop yield shows that it improves the food security problem

Coronary Microvascular Angina: A State-of-the-Art Review

Up to 60-70% of patients, undergoing invasive coronary angiography due to angina and demonstrable myocardial ischemia with provocative tests, do not have any obstructive coronary disease. Coronary microvascular angina due to a dysfunction of the coronary microcirculation is the underlying cause in almost 50% of these patients, associated with a bad prognosis and poor quality of life. In recent years, progress has been made in the diagnosis and management of this condition. The aim of this review is to provide an insight into current knowledge of this condition, from current diagnostic methods to the latest treatments.Copyright © 2022 Spione, Arevalos, Gabani, Sabaté and Brugaletta

Sig-Wasserstein GANs for Time Series Generation

Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity time-series generators, the SigWGAN, by combining continuous-time stochastic models with the newly proposed signature W1 metric. The former are the Logsig-RNN models based on the stochastic differential equations, whereas the latter originates from the universal and principled mathematical features to characterize the measure induced by time series. SigWGAN allows turning computationally challenging GAN min-max problem into supervised learning while generating high fidelity samples. We validate the proposed model on both synthetic data generated by popular quantitative risk models and empirical financial data. Codes are available at https://github.com/SigCGANs/Sig-WassersteinGANs.gi

Sig-Wasserstein GANs for Time Series Generation

Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity time-series generators, the SigWGAN, by combining continuous-time stochastic models with the newly proposed signature W1 metric. The former are the Logsig-RNN models based on the stochastic differential equations, whereas the latter originates from the universal and principled mathematical features to characterize the measure induced by time series. SigWGAN allows turning computationally challenging GAN min-max problem into supervised learning while generating high fidelity samples. We validate the proposed model on both synthetic data generated by popular quantitative risk models and empirical financial data. Codes are available at https://github.com/SigCGANs/Sig-WassersteinGANs.gi

Pulse processing routines for neutron time-of-flight data

A pulse shape analysis framework is described, which was developed for n_TOF-Phase3, the third phase in the operation of the n_TOF facility at CERN. The most notable feature of this new framework is the adoption of generic pulse shape analysis routines, characterized by a minimal number of explicit assumptions about the nature of pulses. The aim of these routines is to be applicable to a wide variety of detectors, thus facilitating the introduction of the new detectors or types of detectors into the analysis framework. The operational details of the routines are suited to the specific requirements of particular detectors by adjusting the set of external input parameters. Pulse recognition, baseline calculation and the pulse shape fitting procedure are described. Special emphasis is put on their computational efficiency, since the most basic implementations of these conceptually simple methods are often computationally inefficient.Comment: 13 pages, 10 figures, 5 table

Intravenous Statin Administration During Myocardial Infarction Compared With Oral Post-Infarct Administration

Beyond lipid-lowering, statins exert cardioprotective effects. High-dose statin treatment seems to reduce cardiovascular complications in high-risk patients. The ideal timing and administration regime remain unknown. This study compared the cardioprotective effects of intravenous statin administration during myocardial infarction (MI) with oral administration immediately post-MI. Hypercholesterolemic pigs underwent MI induction (90 min of ischemia) and were kept for 42 days. Animals were distributed in 3 arms (A): A1 received an intravenous bolus of atorvastatin during MI; A2 received an intravenous bolus of vehicle during MI; and A3 received oral atorvastatin within 2 h post-MI. A1 and A3 remained on daily oral atorvastatin for the following 42 days. Cardiac magnetic resonance analysis (days 3 and 42 post-MI) and molecular/histological studies were performed. At day 3, A1 showed a 10% reduction in infarct size compared with A3 and A2 and a 50% increase in myocardial salvage. At day 42, both A1 and A3 showed a significant decrease in scar size versus A2; however, A1 showed a further 24% reduction versus A3. Functional analyses revealed improved systolic performance in A1 compared with A2 and less wall motion abnormalities in the jeopardized myocardium versus both groups at day 42. A1 showed enhanced collagen content and AMP-activated protein kinase activation in the scar, increased vessel density in the penumbra, higher tumor necrosis factor α plasma levels and lower peripheral blood mononuclear cell activation versus both groups. Intravenous administration of atorvastatin during MI limits cardiac damage, improves cardiac function, and mitigates remodeling to a larger extent than when administered orally shortly after reperfusion. This therapeutic approach deserves to be investigated in ST-segment elevation MI patients