455 research outputs found
Improving water use efficiency in vertical farming: Effects of growing systems, far-red radiation and planting density on lettuce cultivation
Vertical farms (VFs) are innovative urban production facilities consisting of multi-level indoor systems equipped with artificial lighting in which all the environmental conditions are controlled independently from the external climate. VFs are generally provided with a closed loop fertigation system to optimize the use of water and nu-trients. The objective of this study, performed within an experimental VF at the University of Bologna, was to quantify the water use efficiency (WUE, ratio between plant fresh weight and the volume of water used) for a lettuce (Lactuca sativa L.) growth cycle obtained in two different growing systems: an ebb-and-flow substrate culture and a high pressure aeroponic system. Considering the total water consumed (water used for irrigation and climate management), WUE of ebb-and-flow and aeroponics was 28.1 and 52.9 g L-1 H2O, respectively. During the growing cycle, the contribution generated by the recovery of internal air moisture from the heating, ventilation and air conditioning (HVAC) system, was quantified. Indeed, by recovering water from the dehu-midifier, water use decreases dramatically (by 67 %), while WUE increased by 206 %. Further improvement of WUE in the ebb-and-flow system was obtained through ameliorated crop management strategies, in particular, by increasing planting densities (e.g., 153, 270 and 733 plants m-2) and by optimizing the light spectrum used for plant growth (e.g., adjusting the amount of far-red radiation in the spectrum). Strategies for efficient use of water in high-tech urban indoor growing systems are therefore proposed
Search for spontaneous muon emission from lead nuclei
We describe a possible search for muonic radioactivity from lead nuclei using
the base elements ("bricks" composed by lead and nuclear emulsion sheets) of
the long-baseline OPERA neutrino experiment. We present the results of a Monte
Carlo simulation concerning the expected event topologies and estimates of the
background events. Using few bricks, we could reach a good sensitivity level.Comment: 12 pages, 4 figure
A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning
Objective: Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a
global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and
its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light
of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service
of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial
intelligence algorithms towards predictive hypotheses in tackling health emergencies.
Methods: Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare
institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geo-
spatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely
on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the fore-
cast of regional medical supplies, synergically enhancing geospatial and temporal dimensions.
Results: The overall performance of the regional risk colour code classifier yielded a high value of the macro-average
F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded
a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hos-
pitals of the Lazio region which admit about 90% of the region’s patients. The prediction of the medicinal purchases for the
regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%.
Conclusions: Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand
throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective
government decision-making, managing supply chain resources, and for informing tough policy decisions
Prospects for measuring the gravitational free-fall of antihydrogen with emulsion detectors
The main goal of the AEgIS experiment at CERN is to test the weak equivalence
principle for antimatter. AEgIS will measure the free-fall of an antihydrogen
beam traversing a moir\'e deflectometer. The goal is to determine the
gravitational acceleration g for antihydrogen with an initial relative accuracy
of 1% by using an emulsion detector combined with a silicon micro-strip
detector to measure the time of flight. Nuclear emulsions can measure the
annihilation vertex of antihydrogen atoms with a precision of about 1 - 2
microns r.m.s. We present here results for emulsion detectors operated in
vacuum using low energy antiprotons from the CERN antiproton decelerator. We
compare with Monte Carlo simulations, and discuss the impact on the AEgIS
project.Comment: 20 pages, 16 figures, 3 table
Procedure for short-lived particle detection in the OPERA experiment and its application to charm decays
The OPERA experiment, designed to perform the first observation of oscillations in appearance mode through the detection of
the leptons produced in charged current interactions, has
collected data from 2008 to 2012. In the present paper, the procedure developed
to detect particle decays, occurring over distances of the order of 1 mm
from the neutrino interaction point, is described in detail. The results of its
application to the search for charmed hadrons are then presented as a
validation of the methods for appearance detection
Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network
We have studied the performance of a new algorithm for electron/pion
separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion
films. The software for separation consists of two parts: a shower
reconstruction algorithm and a Neural Network that assigns to each
reconstructed shower the probability to be an electron or a pion. The
performance has been studied for the ECC of the OPERA experiment [1].
The separation algorithm has been optimized by using a detailed Monte
Carlo simulation of the ECC and tested on real data taken at CERN (pion beams)
and at DESY (electron beams). The algorithm allows to achieve a 90% electron
identification efficiency with a pion misidentification smaller than 1% for
energies higher than 2 GeV
Measurements of , , , and proton production in proton-carbon interactions at 31 GeV/ with the NA61/SHINE spectrometer at the CERN SPS
Measurements of hadron production in p+C interactions at 31 GeV/c are
performed using the NA61/ SHINE spectrometer at the CERN SPS. The analysis is
based on the full set of data collected in 2009 using a graphite target with a
thickness of 4% of a nuclear interaction length. Inelastic and production cross
sections as well as spectra of , , p, and are
measured with high precision. These measurements are essential for improved
calculations of the initial neutrino fluxes in the T2K long-baseline neutrino
oscillation experiment in Japan. A comparison of the NA61/SHINE measurements
with predictions of several hadroproduction models is presented.Comment: v1 corresponds to the preprint CERN-PH-EP-2015-278; v2 matches the
final published versio
NA61/SHINE facility at the CERN SPS: beams and detector system
NA61/SHINE (SPS Heavy Ion and Neutrino Experiment) is a multi-purpose
experimental facility to study hadron production in hadron-proton,
hadron-nucleus and nucleus-nucleus collisions at the CERN Super Proton
Synchrotron. It recorded the first physics data with hadron beams in 2009 and
with ion beams (secondary 7Be beams) in 2011.
NA61/SHINE has greatly profited from the long development of the CERN proton
and ion sources and the accelerator chain as well as the H2 beamline of the
CERN North Area. The latter has recently been modified to also serve as a
fragment separator as needed to produce the Be beams for NA61/SHINE. Numerous
components of the NA61/SHINE set-up were inherited from its predecessors, in
particular, the last one, the NA49 experiment. Important new detectors and
upgrades of the legacy equipment were introduced by the NA61/SHINE
Collaboration.
This paper describes the state of the NA61/SHINE facility - the beams and the
detector system - before the CERN Long Shutdown I, which started in March 2013
Measurement of negatively charged pion spectra in inelastic p+p interactions at = 20, 31, 40, 80 and 158 GeV/c
We present experimental results on inclusive spectra and mean multiplicities
of negatively charged pions produced in inelastic p+p interactions at incident
projectile momenta of 20, 31, 40, 80 and 158 GeV/c ( 6.3, 7.7,
8.8, 12.3 and 17.3 GeV, respectively). The measurements were performed using
the large acceptance NA61/SHINE hadron spectrometer at the CERN Super Proton
Synchrotron.
Two-dimensional spectra are determined in terms of rapidity and transverse
momentum. Their properties such as the width of rapidity distributions and the
inverse slope parameter of transverse mass spectra are extracted and their
collision energy dependences are presented. The results on inelastic p+p
interactions are compared with the corresponding data on central Pb+Pb
collisions measured by the NA49 experiment at the CERN SPS.
The results presented in this paper are part of the NA61/SHINE ion program
devoted to the study of the properties of the onset of deconfinement and search
for the critical point of strongly interacting matter. They are required for
interpretation of results on nucleus-nucleus and proton-nucleus collisions.Comment: Numerical results available at: https://edms.cern.ch/document/1314605
Updates in v3: Updated version, as accepted for publicatio
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