Search

The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r 2 = 0.76, r 2 = 0.87 and r 2 = 0.91, respectively for the Hojiblanca model, and r 2 = 0.79, r 2 = 0.80 and r 2 = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r 2 = 0.69 for B to r 2 = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards. [Display omitted] • Leaf nutrient content of olive trees is predicted from UAV multispectral data. • Multiple Instance Regression is applied and its benefits discussed. • Models provide good results for N, P and K in the Hojiblanca and Picual models. • The rest of nutrients also show acceptable results. • Results are promising for detecting spatial variability in fertilisation needs. [ABSTRACT FROM AUTHOR]

Objectives: The outbreak of the COVID-19 pandemic has brought commercial, social, and economic consequences in every country that has experienced substantial SARS-CoV-2 infection rates. The complete change in the environment that took place due to the outbreak of the pandemic can lead to stressful situations, especially among healthcare personnel. Material and methods: The research were conducted during the COVID-19 pandemic between the 27 March 2020 and the 20 April 2020. The research included 1984 employees of emergency medical systems in seven European countries. An internet-based questionnaire format was adopted for the study (ΩMc-Donald > 0.7). Results: The highest level of stress was experienced by personnel in the United Kingdom M = 4.03, and the lowest by Norwegian employees M = 2.89. High levels of stress were also experienced by nurses from Spain and Poland. Women actively working in the healthcare system during the pandemic experienced higher stress levels than men. Conclusions: Women working in European emergency medical systems are more vulnerable to work-related stress, while carrying out emergency medical procedures during the pandemic. Differences in the level of stress experienced while carrying out duties in pre-hospital conditions were only found among Spanish emergency medical system personnel. [ABSTRACT FROM AUTHOR]

Low energy x-ray intra-operative radiation therapy (IORT) is used mostly for breast cancer treatment with spherical applicators. X-ray IORT treatment delivered during surgery (ex: INTRABEAM®, Carl Zeiss) can benefit from accurate and fast dose prediction in a patient 3D volume. However, full Monte Carlo (MC) simulations are time-consuming and no commercial treatment planning system (TPS) was available for this treatment delivery technique. Therefore, the aim of this work is to develop a dose computation tool based on MC phase space information, which computes fast and accurate dose distributions for spherical and needle INTRABEAM® applicators. First, a database of monoenergetic phase-space (PHSP) files and depth dose profiles (DDPs) in water for each applicator is generated at factory and stored for on-site use. During commissioning of a given INTRABEAM® unit, the proposed fast and optimized phase-space (FOPS) generation process creates a phase-space at the exit of the applicator considered, by fitting the energy spectrum of the source to a combination of the monoenergetic precomputed phase-spaces, by means of a genetic algorithm, with simple experimental data of DDPs in water provided by the user. An in-house hybrid MC (HMC) algorithm which takes into account condensed history simulations of photoelectric, Rayleigh and Compton interactions for x-rays up to 1 MeV computes the dose from the optimized phase-space file. The whole process has been validated against radiochromic films in water as well as reference MC simulations performed with penEasy in heterogeneous phantoms. From the pre-computed monoenergetic PHSP files and DDPs, building the PHSP file optimized to a particular depth-dose curve in water only takes a few minutes in a single core (i7@2.5 GHz), for all the applicators considered in this work, and this needs to be done only when the x-ray source (XRS) is replaced. Once the phase-space file is ready, the HMC code is able to compute dose distributions within 10 min. For all the applicators, more than 95% of voxels from dose distributions computed with the FOPS+hybrid code agreed within 7%–0.5 mm with both reference MC simulations and measurements. The method proposed has been fully validated and it is now implemented into radiance (GMV SA, Spain), the first commercial IORT TPS. [ABSTRACT FROM AUTHOR]