Oscillations and neuronal synchronization in epilepsy: an approach based on oscillation theory and statistical mechanics.

openIn questo lavoro si propone di studiare i processi di sincronizzazione neuronale dal punto di vista dei sistemi dinamici, in particolare, della teoria delle oscillazioni. Si può dimostrare che esistono oscillazioni macroscopiche nel sistema talamocorticale dei topi epilettici. Questo fatto permette di modellare gli attacchi epilettici come processi di sincronizzazione di uno o due oscillatori auto-sostenuti, i cui parametri vengono ricavati dalle funzioni di risposta di fase ottenute sperimentalmente. Si osservano anche le cosiddette lingue di Arnold e i plateau di sincronizzazione, caratteristici della risposta di fase dei processi con un ciclo limite. Inoltre, utilizzando metodi della fisica statistica e la teoria di informazione, si ricava un rapporto fra la sincronizzazione e la quantità di informazione contenuta nelle rette funzionali del cervello. Si osserva che questa quantità di informazione è massima a livelli intermedi di sincronizzazione, in stati normali di veglia, e molto più bassa durante gli attacchi epiletticiIn this work we propose to study the neuronal synchronization processes from the point of view of the dynamical systems, in particular of the oscillations theory. It can be demonstrated that there are macroscopic oscillations in the thalamocortical network in epileptic rats. So we are able to model the epileptic seizures as synchronization processes of one or two self-sustained oscillator, whose parameters are extracted from the phase response functions obtained experimentally. We observe also the Arnold tongues and the synchronization plateau that are typical pf the phase response processes with a limit cycle. Moreover, using statistical physics and information theory methods, we obtain a relation between synchronization and quantity of information contained in the brain functional lines. This quantity of information has a peak at intermediate synchronization levels, as in conscious awareness states, and it is lower during epileptic seizures

Modelling soil organic carbon changes under different maize cropping scenarios for cellulosic ethanol in Europe

The utilization of crop residues in the production of second generation biofuels has the potential to boost the bioenergy sector without affecting food commodity prices. However, policies leading to large-scale biomass removal should carefully balance the consequences, both environmental and in terms of emissions, on soil organic carbon (SOC) stocks depletion. Using a recently developed simulation platform, SOC changes were estimated at European level (EU + candidate and potential candidate countries) under two scenarios of low (R30) and high (R90) maize stover removal for cellulosic ethanol production (i.e. 30% and 90% of stover removal, respectively). Additionally, mitigation practices for SOC preservation, namely the introduction of a ryegrass cover crop (R90_C) and biodigestate return to soil (R90_B), were explored under the highest rate of stover removal. The results showed that 15.3 to 50.6 Mt yr-1 of stover (dry matter) would be potentially available for ethanol production under the lower and high removal rates considered. However, large-scale exploitation of maize residues will lead to a SOC depletion corresponding to 39.7 – 135.4 Mt CO2 eq. by 2020 (under R30 and R90, respectively) with greater losses in the long-term. In particular, every tonne of C residue converted to bioethanol was predicted to have an additional impact on SOC loss almost ranging from 0.2-0.5 CO2 eq ha-1 yr-1, considering a continuous biofuel scenario by 2050. The mitigation practices evaluated could more than halve SOC losses compared to R90, but not totally offsetting the negative soil C balance. There is a pressing need to design policies at EU level for optimum maize biofuel cultivations that will preserve the current SOC stock or even generate C credits.JRC.H.5-Land Resources Managemen

Calcolo strutturale di un'opera complementare alla linea tranviaria di mestre

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HPCML: A Modeling Language Dedicated to High-Performance Scientific Computing

International audienceTremendous computational resources are required to compute complex physical simulations. Unfortunately computers able to provide such computational power are difficult to program, especially since the rise of heterogeneous hardware architectures. This makes it particularly challenging to exploit efficiently and sustainably supercomputers resources. We think that model-driven engineering can help us tame the complexity of high-performance scientific computing software development by separating the different concerns such as mathematics, parallelism, or validation. The principles of our approach, named MDE4HPC, stem from this idea. In this paper, we describe the High-Performance Computing Modeling Language (HPCML), a domain-specific modeling language at the center of this approach

A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a Quantitative Spatially Distributed Modelling Approach

Field measurements and observations have shown that wind erosion is a threat for numerous arable lands in the European Union (EU). Wind erosion affects both the semi-arid areas of the Mediterranean region as well as the temperate climate areas of the northern European countries. Yet, there is still a lack of knowledge, which limits the understanding about where, when and how heavily wind erosion is affecting European arable lands. Currently, the challenge is to integrate the insights gained by recent pan-European assessments, local measurements, observations and field-scale model exercises into a new generation of regional-scale wind erosion models. This is an important step to make the complex matter of wind erosion dynamics more tangible for decision-makers and to support further research on a field-scale level. A geographic information system version of the Revised Wind Erosion Equation was developed to (i) move a step forward into the large-scale wind erosion modelling; (ii) evaluate the soil loss potential due to wind erosion in the arable land of the EU; and (iii) provide a tool useful to support field-based observations of wind erosion. The model was designed to predict the daily soil loss potential at a ca. 1 km2 spatial resolution. The average annual soil loss predicted by geographic information system Revised Wind Erosion Equation in the EU arable land totalled 0·53 Mg ha−1 y−1, with the second quantile and the fourth quantile equal to 0·3 and 1·9 Mg ha−1 y−1, respectively. The cross-validation shows a high consistency with local measurements reported in literature

The Tensor Algebra Compiler

Tensor and linear algebra is pervasive in data analytics and the physical sciences. Often the tensors, matrices or even vectors are sparse. Computing expressions involving a mix of sparse and dense tensors, matrices and vectors requires writing kernels for every operation and combination of formats of interest. The number of possibilities is infinite, which makes it impossible to write library code for all. This problem cries out for a compiler approach. This paper presents a new technique that compiles compound tensor algebra expressions combined with descriptions of tensor formats into efficient loops. The technique is evaluated in a prototype compiler called taco, demonstrating competitive performance to best-in-class hand-written codes for tensor and matrix operations

LUCAS 2018 - SOIL COMPONENT: Sampling Instructions for Surveyors

The European Commission launched a soil assessment component to the periodic LUCAS Land Use/Land Cover Area Frame Survey in 2009. Composite soil samples from 0-20-cm depth were taken, air-dried and sieved to 2 mm in order to analyse physical and chemical parameters of topsoil in 25 Member States (EU-27 except Bulgaria, Romania, Malta and Cyprus). The aim of the LUCAS Soil Component was to create a harmonised and comparable dataset of main properties of topsoil at the EU. The LUCAS Soil Component was extended to Bulgaria and Romania in 2012. Overall, ca. 22,000 soil samples were collected and analysed. All samples were analysed for percentage of coarse fragments, particle-size distribution, pH, organic carbon, carbonates, phosphorous, total nitrogen, extractable potassium, cation exchange capacity, multispectral properties and heavy metals. In 2015, the soil sampling was repeated in the same set of points of LUCAS 2009/2012 to monitor changes in topsoil physical and chemical parameters across the EU. The soil component was extended to points above elevations of 1000 m, which were not sampled in LUCAS 2009/2012. Furthermore, soil samples were taken in Albania, Bosnia-Herzegovina, Croatia, Macedonia, Montenegro, Serbia and Switzerland. The soil sampling was carried out following the instructions already used in LUCAS 2009/2012. Approximately 27,000 samples were collected and will be analysed during 2016 and 2017. In 2018, a new soil sampling campaign will be carried out within the LUCAS framework. Soil samples will be taken in repeated points of LUCAS 2009/2012 and LUCAS 2015. The novelty of the survey is that new physical, chemical and biological parameters will be analysed. Key parameters for evaluating soil quality, such as bulk density and soil biodiversity, will be analysed. These analyses require specific methods of soil sampling, preparation and storage of samples. Furthermore, field measurements such as the thickness of organic layer in peat soils, and visual assessment of signs of soil erosion will be carried out in 2018. This technical report compiles the instructions for collecting the various soil samples and for performing field measurements in the soil survey of 2018. These instructions will be used for all LUCAS surveyors, to create a comparable database of soil characteristics all over Europe.JRC.D.3-Land Resource

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)

In this paper, we are interested in the acceleration of numerical simulations. We focus on a hypersonic planetary reentry problem whose simulation involves coupling fluid dynamics and chemical reactions. Simulating chemical reactions takes most of the computational time but, on the other hand, cannot be avoided to obtain accurate predictions. We face a trade-off between cost-efficiency and accuracy: the simulation code has to be sufficiently efficient to be used in an operational context but accurate enough to predict the phenomenon faithfully. To tackle this trade-off, we design a hybrid simulation code coupling a traditional fluid dynamic solver with a neural network approximating the chemical reactions. We rely on their power in terms of accuracy and dimension reduction when applied in a big data context and on their efficiency stemming from their matrix-vector structure to achieve important acceleration factors ($\times 10$ to $\times 18.6$). This paper aims to explain how we design such cost-effective hybrid simulation codes in practice. Above all, we describe methodologies to ensure accuracy guarantees, allowing us to go beyond traditional surrogate modeling and to use these codes as references.Comment: Under revie

Effect of Good Agricultural and Environmental Conditions on erosion and soil organic carbon balance: A national case study

Since, the Common Agricultural Policies (CAP) reform in 2003, many efforts have been made at the European level to promote a more environmentally friendly agriculture. In order to oblige farmers to manage their land sustainably, the GAEC (Good Agricultural and Environmental Conditions) were introduced as part of the Cross Compliance mechanism. Among the standards indicated, the protection of soils against erosion and the maintenance of soil organic matter and soil structure were two pillars to protect and enhance the soil quality and functions. While Member States should specifically define the most appropriate management practices and verify their application, there is a substantial lack of knowledge about the effects of this policy on erosion prevention and soil organic carbon (SOC) change. In order to fill this gap, we coupled a high resolution erosion model based on Revised Universal Soil Loss Equation (RUSLE) with the CENTURY biogeochemical model, with the aim to incorporate the lateral carbon fluxes occurring with the sediment transportation. Three scenarios were simulated on the whole extent of arable land in Italy: (i) a baseline without the GAEC implementation; (ii) a current scenario considering a set of management related to GAEC and the corresponding area of application derived from land use and agricultural management statistics and (iii) a technical potential where GAEC standards are applied to the entire surface. The results show a 10.8% decrease, from 8.33 Mg ha −1 year −1 to 7.43 Mg ha −1 year −1 , in soil loss potential due to the adoption of the GAEC conservation practices. The technical potential scenario shows a 50.1% decrease in the soil loss potential (soil loss 4.1 Mg ha −1 year −1 ). The GAEC application resulted in overall SOC gains, with different rates depending on the hectares covered and the agroecosystem conditions. About 17% of the SOC change was attributable to avoided SOC transport by sediment erosion in the current scenario, while a potential gain up to 23.3 Mt of C by 2020 is predicted under the full GAEC application. These estimates provide a useful starting point to help the decision-makers in both ex-ante and ex-post policy evaluation while, scientifically, the way forward relies on linking biogeochemical and geomorphological processes occurring at landscape level and scaling those up to continental and global scales