11 research outputs found
A che gioco giochiamo? Tradurre Vitaï Lampada per riflettere su uno scarto culturale
No full textThe paper presents a possible translation of the poem "Vitaï Lampada", published by Sir Henry Newbolt, in 1897. His work can be inscribed in the genre of military-patriotic poetry, much in vogue in the nineties of the nineteenth century. The author reached in those years a high degree of popularity, for the tone and the character of his verses, but later fell out of favor - especially with the critics - coinciding with the decline of the imperialist ethic - with which Newbolt is inextricably associated - and with the awareness of the atrocity of the war, resulting from the impact of the first World War. Therefore, a timeworn text, the work of an author who is poorly considered and sometimes derided. Yet, because of the characteristics that make it an object of criticism and sometimes scorn, this poem - as the entire production of Newbolt - is worthy of attention because it is extremely representative of its era, of its mindset, of a cultural heritage that can be judged only when put in its proper context. The paper seeks to show how the translation of poetry can be an intellectual operation which requires a further degree of meta-textual reflection. The idiomatic phrase in the title of the paper - "What game are we playing?", inspired by the famous refrain, "Play up! Play up! and play the game! "- is intended as an invitation to reflect on the specificity of the semantic field of the verb" to play "in the ideological and historical moment in which Newbolt's poem was produced. From this consideration comes the need, for the translator, to bridge - or at least to reduce - the cultural gap between the British and the Italian concepts of "playing the game", especially with regard to the idea of "playing" a sport, and the value the Italians place to sports in terms of education or patriotic
Disabitare
No full textLa Mostra d’Oltremare che vediamo oggi è l’esito di un lungo processo di trasformazioni e stratificazioni, distruzioni e ricostruzioni, che arrivano a coincidere con la sua stessa storia. Le radici coloniali, i danneggiamenti bellici, le speculazioni sono altrettante tappe di un lungo e perdurante processo di rimozioni e censure, che sono state ogni volta il preludio a successive rinascite.
Ribaltando l’ordine gerarchico, questo universo pluralista apre la strada a un aggiornamento della disciplina, in cui l’assenza – materialmente predisposta – definisce nuovi confini del progetto. Diverso dal presente, il futuro della Mostra, per mezzo del progetto, non invoca una progressione, probabilmente impossibile, seppur concretamente pensabile. Tale futuro in cui le immagini verranno è composto da coesistenze, da nuovi dettagli in cui l’architettura si presenta quale dispositivo che tra le sue ragioni tiene «aperto l’imprevedibile»
Interaction between High-Level and Low-Level Image Analysis for Semantic Video Object Extraction
No full text The task of extracting a semantic video object is split into two subproblems, namely, object segmentation and region segmentation. Object segmentation relies on a priori assumptions, whereas region segmentation is data-driven and can be solved in an automatic manner. These two subproblems are not mutually independent, and they can benefit from interactions with each other. In this paper, a framework for such interaction is formulated. This representation scheme based on region segmentation and semantic segmentation is compatible with the view that image analysis and scene understanding problems can be decomposed into low-level and high-level tasks. Low-level tasks pertain to region-oriented processing, whereas the high-level tasks are closely related to object-level processing. This approach emulates the human visual system: what one “sees” in a scene depends on the scene itself (region segmentation) as well as on the cognitive task (semantic segmentation) at hand. The higher-level segmentation results in a partition corresponding to semantic video objects. Semantic video objects do not usually have invariant physical properties and the definition depends on the application. Hence, the definition incorporates complex domain-specific knowledge and is not easy to generalize. For the specific implementation used in this paper, motion is used as a clue to semantic information. In this framework, an automatic algorithm is presented for computing the semantic partition based on color change detection. The change detection strategy is designed to be immune to the sensor noise and local illumination variations. The lower-level segmentation identifies the partition corresponding to perceptually uniform regions. These regions are derived by clustering in an -dimensional feature space, composed of static as well as dynamic image attributes. We propose an interaction mechanism between the semantic and the region partitions which allows to cope with multiple simultaneous objects. Experimental results show that the proposed method extracts semantic video objects with high spatial accuracy and temporal coherence.</p
Malnutrition care in hospitalized pediatric inpatients: Comparison of perceptions and experiences across two pediatric academic health sciences centres.
No full textMalnutrition affects up to 1 in 3 Canadian children admitted to hospital. Awareness among pediatric healthcare providers (HCP) of the prevalence and impacts of hospitalized malnutrition is critical for optimal management. The purpose of this study was to determine perceptions of malnutrition among pediatric HCP across two major academic health sciences centres, and to determine how the use of a standardized pediatric nutritional screening tool at one institution affects responses. Between 2020-2022, 192 HCP representing nursing, dietetics, medicine and other allied health were surveyed across McMaster Children’s Hospital (MCH) and The Hospital for Sick Children (SK). 38% of respondents from both centres perceived rates of malnutrition between approximately 1 in 3 patients. Perceptions of the need for nutritional screening, assessment, and management were similar between centres. All respondents identified the need for better communication of hospitalized malnutrition status to community providers at discharge, and resource limitations affecting nutritional management of pediatric inpatients. This study represents the largest and most diverse survey of inpatient pediatric HCP to date. We demonstrate high rates of baseline knowledge of hospital malnutrition, ongoing resource challenges, and the need for a systematic approach to pediatric nutritional management.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
Report for dedicated JPSS VIIRS Ocean Color Calibration/Validation Cruise
No full text60 pages, 26 figures, 3 tables, 3 appendixes, supporting files http://doi.org/10.7289/V52B8W0ZThe NOAA/STAR ocean color team is focused on “end-to-end” production of high quality satellite ocean color products. In situ validation of satellite data is essential to produce the high quality, “fit for purpose” remotely sensed ocean color products that are required and expected by all NOAA line offices, as well as by external (both applied and research) users. In addition to serving the needs of its diverse users within the U.S., NOAA has an ever increasing role in supporting the international ocean color community and is actively engaged in the International Ocean-Colour Coordinating Group (IOCCG). The IOCCG, along with the Committee on Earth Observation Satellites (CEOS) Ocean Colour Radiometry Virtual Constellation (OCR-VC), is developing the International Network for Sensor Inter-comparison and Uncertainty assessment for Ocean Color Radiometry (INSITU-OCR). The INSITU-OCR has identified, amongst other issues, the crucial need for sustained in situ observations for product validation, with longterm measurement programs established and maintained beyond any individual mission.
NOAA has been actively working to support this goal for some time. Dennis Clark of NOAA, in collaboration with community colleagues, began collecting in situ observations for mission validation activities starting with the launch of the Coastal Zone Color Scanner (CZCS; the first ocean color sensor) in 1978. NOAA/STAR scientists continued in situ data collection activities throughout all other ocean color satellite missions. More recently, the NOAA/STAR Ocean Color Team has been making in situ validation measurements continually since the launch in fall 2011 of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) platform, part of the U.S. Joint Polar Satellite System (JPSS) program. NOAA ship time for the purpose of ocean color validation, however, had never been allocated until the cruise described herein. [...]Peer reviewe
Dedicated JPSS VIIRS Ocean Color Calibration/Validation Cruise
No full textThe NOAA/STAR ocean color team is focused on “end-to-end” production of high quality satellite ocean color products. In situ validation of satellite data is essential to produce the high quality, “fit for purpose” remotely sensed ocean color products that are required and expected by all NOAA line offices, as well as by external (both applied and research) users. In addition to serving the needs of its diverse users within
the U.S., NOAA has an ever increasing role in supporting the international ocean color community and is actively engaged in the International Ocean-Colour Coordinating Group (IOCCG). The IOCCG, along with the Committee on Earth Observation Satellites (CEOS) Ocean Colour Radiometry Virtual Constellation (OCR-VC), is developing the International Network for Sensor Inter-comparison and Uncertainty assessment for Ocean Color Radiometry (INSITU-OCR). The INSITU-OCR has identified, amongst other issues, the crucial need for sustained in situ observations for product validation, with longterm measurement programs established and maintained beyond any individual mission.
Recently, the NOAA/STAR Ocean Color Team has been making in situ validation measurements continually since the launch in fall 2011 of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) platform, part of the U.S. Joint Polar Satellite System (JPSS) program. NOAA ship time for the purpose of ocean color validation, however, had never been allocated until the cruise described herein. As the institutional lead for this cruise, NOAA/STAR invited external collaborators based on scientific
objectives and existing institutional collaborations. The invited collaborators are all acknowledged professionals in the ocean color remote sensing community. Most of the cruise principal investigators (PIs) are also PIs of the VIIRS Ocean Color Calibration and Validation (Cal/Val) team, including groups from Stennis Space Center/Naval Research Laboratory (SSC/NRL) and the University of Southern Mississippi (USM); City College of New York (CCNY); University of Massachusetts Boston (UMB); University of South Florida (USF); University of Miami (U. Miami); and, the National Institute of Standards and Technology (NIST). These Cal/Val PIs participated directly, sent qualified researchers
from their labs/groups, or else contributed specific instruments or equipment. Some of the cruise PIs are not part of the NOAA VIIRS Ocean Color Cal/Val team but were chosen to complement and augment the strengths of the Cal/Val team participants. Outside investigator groups included NASA Goddard Space Flight Center (NASA/GSFC), Lamont-Doherty Earth Observatory at Columbia University (LDEO), and the Joint Research Centre of the European Commission (JRC).
This report documents the November 2014 cruise off the U.S. East Coast aboard the NOAA Ship Nancy Foster. This cruise was the first dedicated ocean color validation cruise to be supported by the NOAA Office of Marine and Air Operations (OMAO). A second OMAO-supported cruise aboard the Nancy Foster is being planned for late 2015. We at NOAA/STAR are looking forward to continuing dedicated ocean color validation cruises, supported by OMAO on NOAA vessels, on an annual basis in support of JPSS VIIRS on SNPP, J-1, J-2 and other forthcoming satellite ocean color missions from the U.S as well as other countries. We also look forward to working with the U.S. and the international ocean community for improving our understanding of global ocean optical, biological, and biogeochemical properties.JRC.H.1 - Water Resource
Absorption coefficients by phytoplankton at the first eight Ocean Land Colour Instrument (OLCI) bands from a global in-situ collection of open ocean, coastal and inland surface waters matched to OLCI
No full textThis in situ data set of absorption coefficients by phytoplankton at the first eight Ocean Land Colour Imager (OLCI) bands (centred at 400 nm 412.5 nm, 442.5 nm, 490 nm, 510 nm, 560 nm, 620 nm, 665 nm, abbreviated as aph(400), aph(412), aph(443), aph(490), aph(510), aph(560), aph(620), and aph(665)) consists of different data sets gathered together from in situ measurements collected in open, coastal, and inland surface waters spread around the globe and covering the time from first data delivery by OLCI on S3A in May 2016 until November 2022 which were matched to Ocean Land Colour Imager on Sentinel-3A and -3B and used in the paper by Bracher et al. (2025). We only used the absorption coefficient data derived from measurements on discrete water samples to ensure a similar method procedure followed and a similar uncertainty. It includes publicly available data and newly collected, measured and analysed data sets from the Phytooptics group at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, PI: Astrid Bracher) and Hellenic Centre for Marine Research (HCMR, PI: Andrew C. Banks). This collection was matched that in situ data points had to fall within the 3x3 OLCI FR pixel box and a time window of + 12 hours which followed established community protocols (IOCCG 2018) and particularly EUMETSAT's OLCI matchup protocol (EUMETSAT 2022). Firstly, a pre-processing for quality control and a conversion of the considered in situ data to a common format following Valente et al. (2022) was performed. We flagged and disregarded the following data from the final quality-controlled data set which had (1) unrealistic or missing date or geographic coordinate fields, (2) poor quality (e.g., original flags) or method of observation that did not meet the criteria for the dataset (e.g., not defined in the community protocols (IOCCG 2018, 2019a, 2019b), and (3) spuriously high or low data. For the last item, the following limits were imposed: [0.0001–10] m−1 for aph(443). OLCI pixels were discarded when flagged with the recommended flags in (EUMETSAT 2022), and the remaining matchups were only considered valid if more than 50% of satellite pixels were available at remote sensing reflectance centred at band 560 nm (Rrs(560), e.g., 5 out of 9 for the 3x3 criterion) per an in situ data point, and a coefficient of variation <0.2. Dedicated matchup software developed by EUMETSAT was used to ensure that the validation process followed the established guidelines, ThoMaS (the Tool to generate Matchups of OC products with S3 OLCI https://gitlab.eumetsat.int/eumetlab/oceans/ocean-science-studies/ThoMaS). In situ data from Valente22 (see details on data sets below) were already provided at the nominal OLCI band 443 nm. All other aph(λ) data were provided in hyperspectral resolution (1nm, 2nm or around 3.3 nm resolution). Following Zibordi et al. (2023), these hyperspectral absorption coefficients were transformed to the nominal OLCI bands by averaging over the specific bandwidth. The OLCI matchup data, based on their associated RRS data at the first eight OLCI bands, were assigned to the specific optical water classes (OWCs) according to the Mélin & Vantrepotte (2015) classification. This contains 17 OWCs which range from very turbid to (OWC 1) oligotrophic to very clear waters (OWC 17). The OWC is also delivered for each matchup point (if the assignment fails the field contains "NaN". We provide also for OLCI the standard deviation of the OLCI matchup data to a in situ data point within the 3x3 pixels. For the in situ data we provide the estimate of the uncertainty for each matchup point further described in Bracher et al. (2025)
Absorption coefficients by non-water components at the first eight Ocean Land Colour Imager bands from a global in-situ collection of open ocean, coastal and inland surface waters matched to OLCI
No full textThis in situ data set of absorption coefficients by non-water components at the first eight Ocean Land Colour Imager (OLCI) bands (centred at 400 nm 412.5 nm, 442.5 nm, 490 nm, 510 nm, 560 nm, 620 nm, 665 nm, abbreviated as anw(400), anw(412), anw(443), anw(490), anw(510), anw(560), anw(620), and anw(665)) consists of different data sets gathered together from measurements collected in open, coastal, and inland surface waters spread around the globe and covering the time from first data delivery by OLCI on S3A in May 2016 until November 2022 which were matched to Ocean Land Colour Imager on Sentinel-3A and -3B and used in the paper by Bracher et al. (2025). We only used coincident hyperspectral absorption coefficients by particulates and coloured dissolved organic matter or non-algal particulates, phytoplankton and coloured dissolved organic matter derived from measurements on discrete water samples to ensure a similar method procedure followed and a similar uncertainty. These coincident measurements were summed up to calculate anw(λ). The collection includes publicly available data and newly collected, measured and analysed data sets from the Phytooptics group at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, PI: Astrid Bracher) and Hellenic Centre for Marine Research (HCMR, PI: Andrew C. Banks). The data collection was matched that in situ data points had to fall within the 3x3 OLCI FR pixel box and a time window of + 12 hours which followed established community protocols (IOCCG 2018) and particularly EUMETSAT's OLCI matchup protocol (EUMETSAT 2022). Firstly, a pre-processing for quality control and a conversion of the considered in situ data to a common format following Valente et al. (2022) was performed. We flagged and disregarded the following data from the final quality-controlled data set which had (1) unrealistic or missing date or geographic coordinate fields, (2) poor quality (e.g., original flags) or method of observation that did not meet the criteria for the dataset (e.g., not defined in the community protocols (IOCCG 2018, 2019a, 2019b), and (3) spuriously high or low data. For the last item, the following limits were imposed: [0.0001–10] m−1 for anw(443). OLCI pixels were discarded when flagged with the recommended flags in (EUMETSAT 2022), and the remaining matchups were only considered valid if more than 50% of satellite pixels were available at remote sensing reflectance centred at band 560 nm (Rrs(560), e.g., 5 out of 9 for the 3x3 criterion) per an in situ data point, and a coefficient of variation <0.2. Dedicated matchup software developed by EUMETSAT was used to ensure that the validation process followed the established guidelines, ThoMaS (the Tool to generate Matchups of OC products with S3 OLCI https://gitlab.eumetsat.int/eumetlab/oceans/ocean-science-studies/ThoMaS). The anw(λ) data provided in hyperspectral resolution (1nm, 2nm or around 3.3 nm resolution) were transformed to the nominal OLCI bands by averaging over the specific bandwidth, following Zibordi et al. (2023). The OLCI matchup data, based on their associated RRS data at the first eight OLCI bands, were assigned to the specific optical water classes (OWCs) according to the Mélin & Vantrepotte (2015) classification. This contains 17 OWCs which range from very turbid to (OWC 1) oligotrophic to very clear waters (OWC 17). The OWC is also delivered for each matchup point (if the assignment fails the field contains "NaN". We provide also for OLCI the standard deviation of the OLCI matchup data to a in situ data point within the 3x3 pixels. For the in situ data we provide the estimate of the uncertainty for each matchup point further described in Bracher et al. (2025)
Absorption coefficients by coloured detrital and dissolved organic matter from a global surface water in-situ collection at the first eight bands of and matched to the Ocean Land Colour Imager (OLCI)
No full textThis data set of absorption coefficients by coloured detrital and dissolved organic matter at the first eight Ocean Land Colour Imager (OLCI) bands (centred at 400 nm 412.5 nm, 442.5 nm, 490 nm, 510 nm, 560 nm, 620 nm, 665 nm, abbreviated as aCDM(400), aCDM(412), aCDM(443), aCDM(490), aCDM(510), aCDM(560), aCDM(620), and aCDM(665)) consists of different data sets gathered together in situ from measurements collected in open, coastal, and inland szrface waters spread around the globe and covering the time from first data delivery by OLCI on S3A in May 2016 until November 2022 which were matched to Ocean Land Colour Imager on Sentinel-3A and -3B and used in the paper by Bracher et al. (2025). We only used coincident hyperspectral absorption coefficients by non-algal particulates and coloured dissolved organic matter derived from measurements on discrete water samples to ensure a similar method procedure followed and a similar uncertainty. These coincident measurements were summed up to calculate aCDM(λ). The collection includes the matched OLCI aCDOM products and the publicly available data and newly collected, measured and analysed data sets from the Phytooptics group at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, PI: Astrid Bracher) and Hellenic Centre for Marine Research (HCMR, PI: Andrew C. Banks). The data collection was matched that in situ data points had to fall within the 3x3 OLCI FR pixel box and a time window of + 12 hours which followed established community protocols (IOCCG 2018) and particularly EUMETSAT's OLCI matchup protocol (EUMETSAT 2022). Firstly, a pre-processing for quality control and a conversion of the considered in situ data to a common format following Valente et al. (2022) was performed. We flagged and disregarded the following data from the final quality-controlled data set which had (1) unrealistic or missing date or geographic coordinate fields, (2) poor quality (e.g., original flags) or method of observation that did not meet the criteria for the dataset (e.g., not defined in the community protocols (IOCCG 2018, 2019a, 2019b), and (3) spuriously high or low data. For the last item, the following limits were imposed: [0.0001–10] m−1 for aCDM(443). OLCI pixels were discarded when flagged with the recommended flags in (EUMETSAT 2022), and the remaining matchups were only considered valid if more than 50% of satellite pixels were available at remote sensing reflectance centred at band 560 nm (Rrs(560), e.g., 5 out of 9 for the 3x3 criterion) per an in situ data point, and a coefficient of variation <0.2. Dedicated matchup software developed by EUMETSAT was used to ensure that the validation process followed the established guidelines, ThoMaS (the Tool to generate Matchups of OC products with S3 OLCI https://gitlab.eumetsat.int/eumetlab/oceans/ocean-science-studies/ThoMaS). The aCDM(λ) data provided in hyperspectral resolution (1nm, 2nm or around 3.3 nm resolution) were transformed to the nominal OLCI bands by averaging over the specific bandwidth, following Zibordi et al. (2023). The OLCI matchup data, based on their associated RRS data at the first eight OLCI bands, were assigned to the specific optical water classes (OWCs) according to the Mélin & Vantrepotte (2015) classification. This contains 17 OWCs which range from very turbid to (OWC 1) oligotrophic to very clear waters (OWC 17). The OWC is also delivered for each matchup point (if the assignment fails the field contains "NaN". We provide also for OLCI the standard deviation of the OLCI matchup data to a in situ data point within the 3x3 pixels. For the in situ data we provide the estimate of the uncertainty for each matchup point further described in Bracher et al. (2025)
