From Paper Manual to AR Manual: Do We Still Need Text?

Abstract In this work, we proposed a method to reduce text in technical documentation, aiming at Augmented Reality manuals, where text must be reduced as much as possible. In fact, most of technical information is conveyed through other means such as CAD models, graphic signs, images, etc.. The method classifies technical instructions into two categories: instructions that can be presented with graphic symbols and instructions that should be presented with text. It is based on the analysis of the action verbs used in the instruction, and makes use of ASD Simplified Technical English (STE) for remaining text instructions and let them easier to translate into other languages

Building an ecologically valid facial expression database – Behind the scenes

Artificial Intelligence (AI) algorithms, together with a general increased computational performance, allow nowadays exploring the use of Facial Expression Recognition (FER) as a method of recognizing human emotion through the use of neural networks. The interest in facial emotion and expression recognition in real-life situations is one of the current cutting-edge research challenges. In this context, the creation of an ecologically valid facial expression database is crucial. To this aim, a controlled experiment has been designed, in which thirty-five subjects aged 18–35 were asked to react spontaneously to a set of 48 validated images from two affective databases, IAPS and GAPED. According to the Self-Assessment Manikin, participants were asked to rate images on a 9-points visual scale on valence and arousal. Furthermore, they were asked to select one of the six Ekman’s basic emotions. During the experiment, an RGB-D camera was also used to record spontaneous facial expressions aroused in participants storing both the color and the depth frames to feed a Convolutional Neural Network (CNN) to perform FER. In every case, the prevalent emotion pointed out in the questionnaires matched with the expected emotion. CNN obtained a recognition rate of 75.02%, computed comparing the neural network results with the evaluations given by a human observer. These preliminary results have confirmed that this experimental setting is an effective starting point for building an ecologically valid database

Indoor mobile mapping system and crowd simulation to support school reopening because of covid-19: a case study

Occupancy analyses represent a crucial topic for building performance. At present, this is even true because of the pandemic emergency due to SARS-CoV-2 and the need to support the functional analysis of building spaces in relation to social distancing rules. Moreover, the need to assess the suitability of spaces in high occupancy buildings as the educational ones, for which occupancy evaluations result pivotal to ensure the safety of the end-users in their daily activities, is a priority. The proposed paper investigates the steps that are needed to secure a safe re-opening of an educational building. A case study has been selected as a test site to analyse the re-opening steps as required by Italian protocols and regulations. This analysis supported the school director of a 2-to-10 year old school and its team in the decision-making process that led to the safe school re-opening. Available plants and elevations of the building were collected and a fast digital survey was carried out using the mobile laser scanner technology (iMMS - Indoor Mobile Mapping System) in order to acquire three-dimensional geometries and digital photographic documentation of the spaces. A crowd simulation software (i.e. Oasys MassMotion) was implemented to analyse end-users flows; the social distance parameter was set in its proximity modelling tools in order to check the compliance of spaces and circulation paths to the social distancing protocols. Contextually to the analysis of users flows, a plan of hourly air changes to maintain a high quality of the environments has been defined

Impacts of Climate Change on SOC Dynamic and Crop Yield of Italian Rainfed Wheat-Maize Cropping Systems Managed with Conventional or Conservation Tillage Practices

There is still uncertainty on the ability of conservation tillage (i.e., reduced- RT and no till - NT) in contributing to the resilience of cropping systems to climate change pressures (Powlson et al 2016). RT or NT can improve soil physical and biological proprieties thus increasing water holding capacity and fertility, stabilizing soil structure and enhancing soil biodiversity and functions. They are also frequently proposed as mitigation practices as they can contribute to increase soil organic carbon (SOC) compared to conventional moldboard ploughing practices (Gonzalez-Sanchezet al., 2012). However, SOC increase occurs mostly in the upper soil layer but not always in the deeper profile (Haddaway et al., 2016) where SOC measurements are less frequently measured. In this study, we used data obtained from long term field experiments(LTE) coupled with three crop simulation models in order to assess the long-term effects of different tillage management practices on crop yield and on changes in SOC stocks in both superficial (0-20cm) and deeper layers (20-50cm) in Mediterranean rainfed cereal cropping systems at current and future climate scenarios

Modelling different cropping systems

Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations

A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces

IRS2 TEAM:Alfredo Rodríguez(1), Ignacio J. Lorite(3), Fulu Tao(4), Nina Pirttioja(5), Stefan Fronzek(5), Taru Palosuo(4), Timothy R. Carter(5), Marco Bindi(2), Jukka G Höhn(4), Kurt Christian Kersebaum(6), Miroslav Trnka(7,8),Holger Hoffmann(9), Piotr Baranowski(10), Samuel Buis(11), Davide Cammarano(12), Yi Chen(13,4), Paola Deligios(14), Petr Hlavinka(7,8), Frantisek Jurecka(7,8), Jaromir Krzyszczak(10), Marcos Lana(6), Julien Minet(15), Manuel Montesino(16), Claas Nendel(6), John Porter(16), Jaime Recio(1), Françoise Ruget(11), Alberto Sanz(1), Zacharias Steinmetz(17,18), Pierre Stratonovitch(19), Iwan Supit(20), Domenico Ventrella(21), Allard de Wit(20) and Reimund P. Rötter(4).An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces . International Crop Modelling Symposiu

Probabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean

Effective adaptation of agricultural systems to climate change has to: Consider local specificities; provide sound and practical information and deal with the uncertainty We present a methodology for assessing different aspects of adaptation. Our study case is adaptation of winter wheat in the Mediterranean