Ludovic Legré - verso

Legré, Ludovic (1838-1904). Titolo manoscritto sul recto, dov'è riportata la data di pubblicazione della foto (nov. 1901). Montata su cartoncino 105 x 62 mm. 1 fotografia : albumina ; 88 x 59 mm. Vai alla scheda bibliografica: https://galileodiscovery.unipd.it/discovery/fulldisplay?context=L&vid=39UPD_INST:VU1&search_scope=MyInst_and_CI&tab=Everything&docid=alma99001512624020604

Ludovic Legré - recto

Botanico: Legré, Ludovic (1838-1904). Titolo manoscritto sul recto, dov'è riportata la data di pubblicazione della foto (nov. 1901). Montata su cartoncino 105 x 62 mm. 1 fotografia : albumina ; 88 x 59 mm. Vai alla scheda bibliografica: https://galileodiscovery.unipd.it/discovery/fulldisplay?context=L&vid=39UPD_INST:VU1&search_scope=MyInst_and_CI&tab=Everything&docid=alma99001512624020604

Towards Improved Datacenter Assessment: Review and Framework Proposition

Our growing needs for social networks, cloud storage and more recently machine learning have fueled the increasing demand for datacenters (DC). It is estimated that by 2030, in the US alone, datacenter power consumption could more than double from 2022 [1]. In Europe, the energy consumption is expected to rise by 28%, from 77TWh to 99TWh [2]. This surge, coupled with the increasing scrutiny imposed on Information and Communication Technology (ICT) from stakeholders, regulators and competition, regarding environmental impacts, and to stay on the path of net-zero, has spotlighted datacenters as key contributors to these environmental concerns. Consequently, companies have set major milestones for the next decades in terms of renewables, energy consumption and water use. This paper aims to shed light on the imperative necessity of revisiting and evaluating various metrics and methodologies used to gauge the impact of datacenters, extending beyond merely assessing sustainability factors. More in detail, we focus on four paramount criteria which encompass the whole datacenter lifecycle and its direct and indirect impacts: environmental impact, economic performance, ecosystem integration and external influence. These are usually evaluated through three types of analysis: single indicators, lifecycle analysis and multi-criteria assessment, all of which are analyzed here

Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning

INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning

Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

International audienceThe present article is a contribution to the CLARIS WorkPackage "Climate and Agriculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions. © Springer Science + Business Media B.V. 2009