Le politiche ICT e il know-how delle Regioni per lo sviluppo del Sistema federato di IDT regionali

Nell’ambito delle azioni promosse dal CISIS-CPSG riguardanti lo sviluppo delle Infrastrutture di Dati Territoriali (IDT), è stato svolto uno studio per comporre un quadro di riferimento conoscitivo, propedeutico alla definizione delle linee guida e dei contenuti tecnici “minimi comuni” per la formazione di IDT regionali di tipo federato su base interregionale, in un’ottica d’interoperabilità e di riuso dei dati. The standing Committee on GIS (CPSG) of the Interregional Centre for ICT, Geographic and Statistical Systems (CISIS) has carried out a knowledge framework useful to define the guidelines for the creation of regional federated SDI on an interregional basis. The study has taken into consideration European and National initiatives concerning ICT and GI policies, the SDI concept shift in respect of the geospatial data use changing and the expertise of the Regions on this matter, already acquired within inter-institutional National and European projects. The article shows the main findings of the study and provides a synthetic description of the methodological approach in order to organize and plan the next actions

GI2NK geographic information: Need to know towards a more demand-driven geospatial workforce education/training system

The paper presents GI-N2K (Geographic information: Need to Know), a European project aiming to improve the way in which future GI professionals are prepared for the labour market. Its main goal is twofold: updating the existing Body of Knowledge on the basis of the new technological developments and the European perspective, and realizing advanced tools to define curriculum, training opportunities and courses. This project focused on foundational research into the creation of a transformational, dynamic environment for pedagogy, knowledge construction, discourse, collaboration, and research in the domain of Geographic Information Science and Technology (GIS&T). After an initial integrated analysis of the demand for and supply of geospatial education and training, the revision of the BoK and the design of the Virtual Laboratory for the BoK (VirLaBok) are currently under investigation. In particular, the Consortium is now involved into the recognition of a proper revision strategy, in terms of content and its usability through an e-platform. The goal of this paper is to discuss preliminary results of this phase and illustrate problems due to a possible overlapping among different knowledge areas. Finally, a prototyping version of the ontology underlying the VirLaBok is presented

A Bottom-up Approach for the Identification of Requirements and ICT solutions for Environmental Information Sharing

European directives and policy communications have been launched with the objective of simplify the sharing of environmental data. The paper proposes a method for comparing heterogeneous information systems to provide input to the development of ICT solutions and best practices for a high level policy. The approach starts by referring to the priorities of the current initiatives that should be supported by a bottom-up collection of good practices and existing systems. High level models of the business processes that are supported by these systems are created to allow compari-son and giving, at the same time, a bottom-up vision on the policy. The ap-proach has been applied in the context of the European project NESIS to provide input for a proposal of ICT guidelines for SEIS initiative.JRC.H.6-Digital Earth and Reference Dat

A Bottom up Approach for the Identification of Requirements and ICT solutions for Environmental Information Sharing

European guidance and policy for the development of information infrastructures recommends that new data and information handling resources should be based on existing examples, as proposed by the European Interoperability Strategy (EIS1) and the Interoperability Solutions for European Public Administrations (ISA2). In addition, the recent Communication on a European Shared Environmental Information System (SEIS3) outlines the need to modernize and simplify the collection, exchange and use of data and information required for the design, implementation and monitoring of environmental policy. These policy drivers present challenges in the actual identification and comparison of often heterogeneous systems within the environmental information sharing domain; including the processes and resources which support the capture/discovery, processing, validation, analysis and dissemination of data/information about the environment. To address this challenge, we present a method to gather and analyze the components of environmental information systems that can contribute to the development of information infrastructures such as SEIS. Our approach is described as (principally) \u27bottom-up\u27: a community of practitioners propose candidate systems for analysis, illustrating what approaches are currently adopted to create, manage, use environmental data/information that aim to meet the goals/principles (or \u27top-down\u27 setting) of the SEIS policy arena, while also informing this policy\u27s development itself

Wildfire smoke reduces lake ecosystem metabolic rates unequally across a trophic gradient

Abstract Wildfire smoke covers entire continents, depositing aerosols and reducing solar radiation fluxes to millions of freshwater ecosystems, yet little is known about impacts on lakes. Here, we quantified trends in the spatial extent of smoke cover in California, USA, and assessed responses of gross primary production and ecosystem respiration to smoke in 10 lakes spanning a gradient in water clarity and nutrient concentrations. From 2006 − 2022, the maximum extent of medium or high-density smoke occurring between June-October increased by 300,000 km2. In the three smokiest years (2018, 2020, 2021), lakes experienced 23 − 45 medium or high-density smoke days, characterized by 20% lower shortwave radiation fluxes and five-fold higher atmospheric fine particulate matter concentrations. Ecosystem respiration generally declined during smoke cover, especially in low-nutrient, cold lakes, whereas responses of primary production were more variable. Lake attributes and seasonal timing of wildfires will mediate the effects of smoke on lakes

A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project

BACKGROUND: Identifying patients at higher risk of healthcare-associated infections (HAIs) in intensive care unit (ICU) represents a major challenge for public health. Machine learning could improve patient risk stratification and lead to targeted infection prevention and control interventions.AIM: To evaluate the performance of the Simplified Acute Physiology Score (SAPS) II for HAIs risk prediction in ICUs, using both traditional statistical and machine learning approaches.METHODS: We used data of 7827 patients from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" project. The Support Vector Machines (SVM) algorithm was applied to classify patients according to sex, patient origin, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II at admission, presence of invasive devices, trauma, impaired immunity, antibiotic therapy in 48 hours before ICU admission.FINDINGS: The performance of SAPS II for predicting the risk of HAIs provides a ROC (Receiver Operating Characteristics) curve with an AUC (Area Under the Curve) of 0.612 (p<0.001) and an accuracy of 56%. Considering SAPS II along with other characteristics at ICU admission, we found an accuracy of the SVM classifier of 88% and an AUC of 0.90 (p<0.001) for the test set. In line, the predictive ability was lower when considering the same SVM model but removing the SAPS II variable (accuracy= 78% and AUC= 0.66).CONCLUSIONS: Our study suggested the SVM model as a tool to early predict patients at higher risk of HAI at ICU admission