The role of ephemeris and GPSs distribution in high resolution satellite images modeling.

The geometric resolution of the images coming from the new generation satellites is almost competitive with that found in the traditional aerial photograms. The aim of this work is to define the role of satellite ephemerides and to optimise the number andvdistribution of Ground Control Points (GCPs) for the image registration. A zone of the Campania region in Italy has been used as a test area: it is characterized by a mountainous area without constructions and by flat land areas densely inhabited. The images that have been used relate to different epochs and different satellites such as Spot5, Ikonos2 and QuickBird. Spot5 images have also been used to generate automatic Digital Elevation Models (DEMs). The assessment of DEM precision has been carried out by comparison with raster DEMs from cartography. The GCPs coordinates have been obtained from a GPS network made up of almost hundred and some of them are used as Check Points (CPs). Different tests have been performed, either varying the number of GCPs or hypothesizing the presence or absence of satellite ephemeris. Some numerical evaluations confirm that the use of the satellite ephemeris greatly reduces the amount of residuals on the CPs; in the case of Spot5 images this improvement becomes even more evident

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

High-speed analysis of nuclear emulsion films with the use of dry objective lenses

The extensive use of nuclear emulsions as precise tracking detectors in experimental physics has been made possible due to recent advances in the production of novel emulsion films and to the development of automatic scanning devices. The scanning speed of such systems has exceeded the level of 20 cm2 of emulsion surface per hour. High-speed automatic scanning systems, such as those developed by the OPERA Collaboration, are able to reconstruct particle tracks in nuclear emulsions with excellent accuracy. However, the high-magnification oil immersion objectives used in these systems assume deposition and removal of oil onto and from the emulsion films. This is a major technological obstacle in the automatization of the emulsion feeding to the microscope, as required for large scale use as in the case of the OPERA neutrino oscillation experiment. In order to overcome this problem, an innovative technique of nuclear emulsion films scanning with the use of dry objective lenses has been developed and successfully applied to the experiment

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 $e/\pi$ 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

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

Analysis of the RLMS Adaptive Beamforming Algorithm Implemented with Finite Precision

This paper studies the influence of the use of finite wordlength on the operation of the RLMS adaptive beamformingalgorithm. The convergence behavior of RLMS, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of nine bits is sufficient for the RLMS algorithm to achieve performance close to that provided by full precision. The performance measures used include residual MSE, rate of convergence, error vector magnitude (EVM), and beam pattern. Based on all these measures, it is shown that the RLMS algorithm outperforms other earlier algorithms, such as least mean square (LMS), recursive least square (RLS), modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS)