Towards net-zero emissions in the EU energy system by 2050

This report presents a comparison of 8 scenarios achieving more than 50% reduction of greenhouse gas emissions by 2030 compared to 1990, and 16 scenarios aiming at climate neutrality by 2050, similar with the ambitions of the “European Green Deal”. This report summarises insights into similar and diverging elements of the scenarios on how the EU energy system may change by 2030 and by 2050, compared to today. The wealth of information, stemming from how different organisations see the EU energy system to evolve within their own scenario context, can provide useful input to EU climate and energy strategies.JRC.C.7-Knowledge for the Energy Unio

Addressing flexibility in energy system models

The present report summarises the discussions and conclusions of the international workshop on "Addressing flexibility in energy system models" held on December 4 and 5 2014 at the premises of the JRC Institute for Energy and Transport in Petten. Around 40 energy modelling experts and researchers from universities, research centres, the power industry, international organisations, and the European Commission (DGs ENER and JRC) met to present and discuss their views on the modelling of flexibility issues, the linkage of energy system models and sector-detailed energy models, the integration of high shares of variable renewable energy sources, and the representation of flexibility needs in power system models. The discussions took into account modelling and data-related methodological aspects, with their limitations and uncertainties, as well as possible alternatives to be implemented within energy system models.JRC.F.6-Energy Technology Policy Outloo

JRC-EU-TIMES 2017 Upgrade: Buildings and heating & cooling technologies

The present report describes two main upgrades that have been made to the JRC-EU-TIMES model during the year 2017: • An improvement of the description of residential and non-residential buildings • An update of data and a new representation for heating &cooling and heat distribution technologies The model updates have been validated through tests with the JRC-EU-TIMES model and with stylised models allowing isolating the observed effect of the changed model input. The updates performed greatly improve the ability of the JRC-EU-TIMES model to perform studies options for the decarbonisation of the heating and cooling sector.JRC.C.7-Knowledge for the Energy Unio

Deployment Scenarios for Low Carbon Energy Technologies

This report provides an outlook for a set of Low Carbon Energy Technologies as well as background on how JRC-EU-TIMES baseline and decarbonisation scenarios are derived. The results help inform decision makers on the technology choices through which the EU can meet its climate and energy goals under different global energy scenarios. The report also provides background for the technology specific results in the technology and market reports produced under the same AA.JRC.C.7-Knowledge for the Energy Unio

The JRC-EU-TIMES model - Assessing the long-term role of the SET Plan Energy technologies

The JRC-EU-TIMES model is one of the models currently pursued in the JRC under the auspices of the JRC Modelling Taskforce. The model has been developed over the last years in a combined effort of two of the JRC Institutes, IPTS and IET. The JRC-EU-TIMES model is designed for analysing the role of energy technologies and their innovation for meeting Europe's energy and climate change related policy objectives. It models technologies uptake and deployment and their interaction with the energy infrastructure including storage options in an energy systems perspective. It is a relevant tool to support impact assessment studies in the energy policy field that require quantitative modelling at an energy system level with a high technology detail. This report aims at providing an overview on the JRC-EU-TIMES model main data inputs and major assumptions. Furthermore, it describes a number of model outputs from exemplary runs in order to display how the model reacts to different scenarios. The scenarios described in this report do not represent a quantified view of the European Commission on the future EU energy mix.JRC.F.6-Energy systems evaluatio

Symptomatic and Subclinical Infection with Rotavirus P[8]G9, Rural Ecuador

Prevalence of this genotype is increasing

Clean energy technologies in coal regions

This report presents a concise overview of the role that clean energy technologies can play for the identified regions in the path to their transition from coal mining activity under a low carbon energy consumption and production lence. The focus is on power generation technologies from wind, solar photovoltaics (free standing and roof-top), bioenergy, geothermal sources, as well as on coal-fired power plants with carbon capture. We also address energy demand technologies and specifically assess the potential for energy efficiency refurbishments in buildings. Energy storage is dealt with presenting activities relevant to batteries, to give an insight on planned or ongoing activities within coal regions. The report summarises the main findings across regions, complemented by one detailed fact sheet per region. Estimates on the renewable energy and clean energy technology potential in each region are presented. We also assess the potential of technologies in terms of investments needs and the impact this could have on job creation and regional economic development. Renewable and clean energy technology options can be an alternative to the continuation of the current model for economic development, power generation and job creation in each region, in line with EU’s climate and energy targets.JRC.C.7-Knowledge for the Energy Unio

Impact of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients: A nationwide study in Spain

Objective To assess the effect of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients in Spain. Settings The initial flood of COVID-19 patients overwhelmed an unprepared healthcare system. Different measures were taken to deal with this overburden. The effect of these measures on neurosurgical patients, as well as the effect of COVID-19 itself, has not been thoroughly studied. Participants This was a multicentre, nationwide, observational retrospective study of patients who underwent any neurosurgical operation from March to July 2020. Interventions An exploratory factorial analysis was performed to select the most relevant variables of the sample. Primary and secondary outcome measures Univariate and multivariate analyses were performed to identify independent predictors of mortality and postoperative SARS-CoV-2 infection. Results Sixteen hospitals registered 1677 operated patients. The overall mortality was 6.4%, and 2.9% (44 patients) suffered a perioperative SARS-CoV-2 infection. Of those infections, 24 were diagnosed postoperatively. Age (OR 1.05), perioperative SARS-CoV-2 infection (OR 4.7), community COVID-19 incidence (cases/10 5 people/week) (OR 1.006), postoperative neurological worsening (OR 5.9), postoperative need for airway support (OR 5.38), ASA grade =3 (OR 2.5) and preoperative GCS 3-8 (OR 2.82) were independently associated with mortality. For SARS-CoV-2 postoperative infection, screening swab test <72 hours preoperatively (OR 0.76), community COVID-19 incidence (cases/10 5 people/week) (OR 1.011), preoperative cognitive impairment (OR 2.784), postoperative sepsis (OR 3.807) and an absence of postoperative complications (OR 0.188) were independently associated. Conclusions Perioperative SARS-CoV-2 infection in neurosurgical patients was associated with an increase in mortality by almost fivefold. Community COVID-19 incidence (cases/10 5 people/week) was a statistically independent predictor of mortality. Trial registration number CEIM 20/217

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio