Towards 'Pt-free' Anion-Exchange Membrane Fuel Cells: Fe-Sn Carbon Nitride-Graphene 'Core-Shell' Electrocatalysts for the Oxygen Reduction Reaction

We report on the development of two new Pt-free electrocatalysts (ECs) for the oxygen reduction reaction (ORR) based on graphene nanoplatelets (GNPs). We designed the ECs with a core-shell morphology, where a GNP core support is covered by a carbon nitride (CN) shell. The proposed ECs present ORR active sites that are not associated to nanoparticles of metal/alloy/oxide, but are instead based on Fe and Sn sub-nanometric clusters bound in coordination nests formed by carbon and nitrogen ligands of the CN shell. The performance and reaction mechanism of the ECs in the ORR are evaluated in an alkaline medium by cyclic voltammetry with the thin-film rotating ring-disk approach and confirmed by measurements on gas-diffusion electrodes. The proposed GNP-supported ECs present an ORR overpotential of only ca. 70 mV higher with respect to a conventional Pt/C reference EC including a XC-72R carbon black support. These results make the reported ECs very promising for application in anion-exchange membrane fuel cells. Moreover, our methodology provides an example of a general synthesis protocol for the development of new Pt-free ECs for the ORR having ample room for further performance improvement beyond the state of the art

Are well-studied marine biodiversity hotspots still blackspots for animal barcoding?

Marine biodiversity underpins ecosystem health and societal well-being. Preservation of biodiversity hotspots is a global challenge. Molecular tools, like DNA barcoding and metabarcoding, hold great potential for biodiversity monitoring, possibly outperforming more traditional taxonomic methods. However, metabarcoding-based biodiversity assessments are limited by the availability of sequences in barcoding reference databases; a lack thereof results in high percentages of unassigned sequences. In this study we (i) present the current status of known vs. barcoded marine species at a global scale based on online taxonomic and genetic databases; and (ii) compare the current status with data from ten years ago. Then we analyzed occurrence data of marine animal species from five Large Marine Ecosystems (LMEs) classified as biodiversity hotspots, to identify any consistent disparities in COI barcoding coverage between geographic regions and at phylum level. Barcoding coverage varied among LMEs (from 36.8% to 62.4% COI-barcoded species) and phyla (from 4.8% to 74.7% COI-barcoded species), with Porifera, Bryozoa and Platyhelminthes being highly underrepresented, compared to Chordata, Arthropoda and Mollusca. We demonstrate that although barcoded marine species increased from 9.5% to 14.2% since the last assessment in 2011, about 15,000 (corresponding to 7.8% increase) new species were described from 2011 to 2021. The next ten years will thus be crucial to enroll concrete collaborative measures and long term initiatives (e.g., Horizon 2030, Ocean Decade) to populate barcoding libraries for the marine realm.the Department of Biological, Geological and Environmental Sciences (BiGeA) of the University of Bologna (UniBo). The CoMBoMed initiative was supported by the European Marine Research Network (EUROMARINE Network), the Inter-Departmental Research Centre for Environmental Sciences (CIRSA – UniBo), the Cultural Heritage Department (DBC - UniBo, https://beniculturali.unibo.it/it), the Fondazione Flaminia and the ERANet Mar-Tera Project SEAMoBB (Solutions for sEmi-Automated Monitoring of Benthic Biodiversity).Peer reviewe

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5–11 December, to 17.5% (25/143 samples) in the week 12–18, to 65.9% (89/135 samples) in the week 19–25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased fromone in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5-11 December, to 17.5% (25/143 samples) in the week 12-18, to 65.9% (89/135 samples) in the week 19-25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool

Machine Learning in a Policy Support System for Smart Tourism Management

In the last few years, the Emilia-Romagna region, in Italy, has seen a significant growth in the tourism economy, due to an increasing number of Italian and foreigner visitors. This has highlighted the need of a strong synergy between tourist facilities and local administrations. In this context, Smart City solutions and Machine Learning (ML) can play an important role to analyse the amount of data generated in this sector. This paper presents part of the work done within the ongoing POLIS-EYE project, targeted at the development of a Policy Support System (PSS) and related intelligent services for an optimized management of the Smart City in the specific domain of tourism in this region. Several results obtained from the application of supervised and unsupervised ML techniques show the effectiveness in the prediction of the tourist flow in different scenarios, e.g., towards regional museums and big events. The integration of these results in the PSS architecture will allow a smart management of the territory on behalf of the administration and will be replicable outside the region

A Machine Learning Pipeline to Analyse Multispectral and Hyperspectral Images: Full/Regular Research Paper (CSCI-RTHI)

Machine Learning is a branch of Artificial Intelligence with the goal of learning patterns from data. These techniques fall into two big categories: supervised and unsupervised learning. The former classify data based on a given set of examples whose classification is known (hence the name supervised), while the latter try to group the data without knowing a priori the possible classes. Neural Networks and clustering algorithms are two of the most prominent examples of the two aforementioned categories. In this paper, we describe a machine learning pipeline to analyse multispectral and hyperspectral images. Our approach first adopts neural networks to identify relevant pixels and then applies a clustering algorithm to group the pixels according to two different criteria, namely intensity and variation of intensity

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications

Deep Aggregations of the Polychaete <i>Amage adspersa</i> (Grube, 1863) in the Ionian Sea (Central Mediterranean Sea) as Revealed via ROV Observations

Many sessile and tube-dwelling polychaetes can act as ecosystem engineers, influencing the physical–chemical and biological characteristics of their habitats, increasing structural complexity. Thus, they are considered structuring species. In summer of 2021, in southern Sicily (Ionian Sea), benthic assemblages dominated by Ampharetidae Amage adspersa were discovered via an ROV survey at a depth range between 166 and 236 m on muddy horizontal seafloor. Large aggregations of this species (up to 297.2 tubes m−2), whose tubes are formed from Posidonia oceanica debris, occurred alternately with tube-free areas. The area was characterized by the sporadic presence of vulnerable sea pens Funiculina quadrangularis (up to 0.08 col. m−2) and Virgularia mirabilis (up to 0.16 col. m−2), and it was possible to detect signs of trawling as well the presence of marine litter (up to 24.0 items 100 m−2). The habitat description, distribution, and density of the tubes of A. adspersa were assessed via imaging analysis. In addition, morphological diagnostic analyses were carried out on some sampled specimens and on their tubes. The acquired data shed new light on how polychaetes can exploit the dead tissues of P. oceanica, contributing to highlight interactions between benthic fauna and seagrass detritus in the marine environment and their ecological role in enhancing the spatial heterogeneity of soft areas of the Mediterranean seafloor