Tradizione manoscritta e citazioni epigrafiche di Ovidio. Una nota su Trist. 1, 3, 25 e Pont. 1, 2, 111 alla luce di alcuni confronti epigrafici

This paper discusses two verses of Ovid’s Tristia (1, 3, 25) and Epistulae ex Ponto (1, 2, 111), for which the manuscript tradition is discordant. These lines are quoted in three epigraphic documents: CLE 1339 = ICVR, I 3903, CLE 1979 = ICVR, VIII 23529 and CLE 1988 = CIL, VI 37965. How reliable are the quotations in the Latin inscriptions? Do they help to reassess the Ovidian text? The main purpose of this study is to answer such questions, with respect to these particular cases

First Case of Systemic Coronavirus Infection in a Domestic Ferret (Mustela putorius furo) in Peru.

A domestic ferret from Lima, Peru, died after ten days of non-specific clinical signs. Based on pathology, immunohistochemistry and molecular analysis, ferret systemic coronavirus (FRSCV)-associated disease was diagnosed for the first time in South America. This report highlights the potential spread of pathogens by the international pet trade

Nuovi fasti dei magistri fontani in un frustulo epigrafico opistografo dal piccolo Aventino

Edizione di un nuovo frammento epigrafico, opistografo, rinvenuto a Roma sul piccolo Aventino e contenente liste di personaggi identificati, in base a vari indizi, con magistri Fontani

Dispatcher3 D4.2 - Prototype package (first release) - User manual

This deliverable along with deliverable D4.1. Technical documentation first release consists of the release of the first prototype of Dispatcher3. The release consists of the binaries and Docker version of the prototype (sent to the Topic Manager). The first release prototype package consists of a set on individual machine learning models which can be executed using Jupyter notebooks. It also includes the integration of the outcome of some of these individual models into a visualisation which would be part of the advice generator to provide high-level information to the end users. All models described in the Deliverable D4.1 will be available and executable in this release. Data required to run the models (with some examples) are also provided. If data are public raw sample values are provided, otherwise pre-computed features are delivered so that the models can be run on individual flight examples. The prototypes can be run using local data (provided in the release) or with data stored in cloud storage (Amazon Web Services (AWS)). This deliverable serves as a manual for the execution of the first release prototype software

Dispatcher3 – Machine learning to support flight planning processes

This poster will present the final results of the Clean Sky 2 project Dispatcher3. Dispatcher3 focuses on the use of machine learning techniques to support flight operations prior departure with holding predictions, runway at arrival estimation and fuel deviations pre-departure to support the flight crew, and ATFM and reactionary delays on D-1 to support the duty manage

Adherence in Rheumatoid Arthritis patients assessed with a validated Italian version of the 5-item compliance questionnaire for rheumatology

OBJECTIVES: The 5-item Compliance Questionnaire for Rheumatology (CQR5) proved reliability and validity in respect of identification of patients likely to be high adherers (HAs) to anti-rheumatic treatment, or low adherers (LAs), i.e. taking<80% of their medications correctly. The objective of the study was to validate an Italian version of CQR5 (I-CQR5) in rheumatoid arthritis (RA) patients and to investigate factors associated with high adherence. METHODS: RA patients, undergoing treatment with ≥1 self-administered conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) or biological DMARD (bDMARD), were enrolled. The cross-cultural adaptation and validation of I-CQR5 followed standardised guidelines. I-CQR5 was completed by patients on one occasion. Data were subjected to factor analysis and Partial Credit model Parametrisation (PCM) to assess construct validity of I-CQR5. Analysis of factors associated with high adherence included demographic, social, clinical and treatment information. Factors achieving a p<0.10 in univariate analysis were included in multivariable analysis. RESULTS: Among 604 RA patients, 274 patients were included in the validation and 328 in the analysis of factors associated with adherence. Factor analysis and PCM confirmed the construct validity and consistency of I-CQR5. HAs were found to be 109 (35.2%) of the patients. bDMARD treatment and employment were found to be independently associated with high adherence: OR 2.88 (1.36-6.1), p=0.006 and OR 2.36 (1.21-4.62), p=0.012, respectively. CONCLUSIONS: Only one-third of RA patients were HAs according to I-CQR5. bDMARDs and employment status increased by almost 3-fold the likelihood of being highly adherent to the anti-rheumatic treatment.Peer reviewe

Dispatcher3 – Machine learning for efficient flight planning - Approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decision making processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general

Use of Modal Representation for the Supporting Structure in Model Based Fault Identification of Large Rotating Machinery: Part 2: Application to a Real Machine

Model-based techniques are often employed in diagnostics of rotating machines to locate and to evaluate the severity of a malfunction. The use of a reliable model can increase the accuracy of identification. Rigid supports or lumped mass pedestals are not always enough to account for foundation dynamics; a modal representation of the supports can improve the identification results. The method, discussed in the first part, is here validated using experimental data of a 320 MW steam turbogenerator. To the authors’ knowledge, this is the first case of fault identification on real data from a large machine, where the supporting structure is accounted for by means of a modal model