Archivio istituzionale della Ricerca - Università degli Studi di Parma
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L’INDEBITAMENTO NEL CALCIO È CONSEGUENZA DEI SUCCESSI SPORTIVI? UN’ANALISI DEL CAMPIONATO ITALIANO DI SERIE A
No full textConservazione del patrimonio costruito attraverso il BIM: studio di una strategia
No full textLa conservazione del patrimonio costruito passa anche, e non solo, attraverso la sua corretta manutenzione e gestione. L’impiego della metodologia BIM è di supporto in questo processo; infatti, la modellazione parametrica viene impiegata per edifici esistenti (spesso di proprietà e/o dati in gestione ad enti pubblici) che necessitano di una costante supervisione e gestione, approfondendo la sesta dimensione del BIM (Facility Management). Lo studio condotto mette al centro del processo di modellazione parametrica la gestione manutentiva dell’edificio preso in analisi, ponendo particolare attenzione alla gestione dello spazio (nel caso specifico di natura allestitiva/espositiva). Lo studio si incentra dunque sull’elaborazione di una metodologia procedurale replicabile messa a servizio delle Pubbliche Amministrazioni con lo scopo di fornire strumenti metodologici utili alla digitalizzazione del patrimonio pubblico esistente, finalizzati alla sua gestione materiale e funzionale
Use of NDSS to discriminate between Parmigiano Reggiano and Grana Padano PDO and their ripening times from grated cheese spectra
No full textThis study focuses on using spectral data recorded with two different Near-Infrared (NIR) instruments (a benchtop and a portable device) to differentiate between Parmigiano Reggiano (PR) and Grana Padano (GP) Protected Designation of Origin (PDO) cheeses and their ripening times. Key findings showed that NIR spectroscopy effectively discriminated between PR and GP, with spectral range differences linked to their chemical composition, including fat, protein, and carbohydrate content. Specifically, certain wavelength ranges (1375–1400 nm, 1205–1250 nm, and 1410–1440 nm) were identified as significant in distinguishing PDO labels, highlighting the roles of fat and protein content in the cheese classification. Spectral features are also distinguished between ripening times, with specific wavelength bands tied to biochemical modifications during maturation, such as changes in moisture, protein, and fat content. In terms of instrument performance, the benchtop device achieved high accuracy in PDO classification (up to 0.97 F1 score), particularly when using a first derivative pre-treatment. The portable device performances showed higher variability but performed flawlessly for PDO classification. While both instruments effectively classified cheeses of distinct ripening ages, they were less successful at detecting samples containing mixtures of different aged cheeses. The portable instrument showed better results when combining the visible spectrum (350–950 nm) with the NIR spectrum (950–1650 nm), capturing surface color changes alongside internal structural transformations related to aging. Overall, the study validates the potential of NIR spectroscopy, especially when combined with established preprocessing techniques, as a powerful non-destructive tool to authenticate specific cheese PDO and assess ripening stages
Euclid I. Overview of the Euclid mission
No full textThe current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance
Synthesis and conformational analysis of digestion resistant allergenic epitopes in Hermetia illucens arginine kinase
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Prediction of hop cone ripening through Internet of Things (IoT) and Machine Learning (ML) technologies
No full textHop (Humulus lupulus L.) cones ripening is characterized by a gradual increase of the valuable brewing metabolites, namely bitter acids and essential oils (EOs), thus making the identification of the optimal harvest time pivotal to obtain high quality yields and avoid economical losses. Cone ripeness is currently evaluated visually: Smart Agriculture (SA) technologies, including the Internet of Things (IoT) paradigm and Machine Learning (ML) models, are expected to have a significant impact on it. In this work, IoT devices are employed to collect data in the time period 2021–2023 at the “Azienda Agricola Ludovico Lucchi” hop testbed located in Campogalliano, Modena, Italy. Two ML-based algorithms are proposed to forecast the optimal harvesting period: the first relies on Multiple Linear Regression (MLR), while the second exploits Principal Component Regression (PCR). Finally, both algorithms classify ripening stages (namely: immature, mature, overripe) through a soft voting classifier. To this end, the identification of the optimal ripening time required the hop cones to be morphologically and chemically characterized (approximately) weekly for three growing seasons. Our results indicated that during the first half of September, there was a contraction in cone width and an increase in the EOs content, representing the optimal harvest maturity. Finally, the proposed ML models forecasted the optimal harvesting period for the 2024 season in the same days and this was confirmed in the reality. The correspondence between predictions and analytical results highlights the potential of integrated IoT and ML techniques to provide decision support for farmers and to improve agricultural operations
