Copernicus high-resolution layers for land cover classification in Italy

The high-resolution layers (HRLs) are land cover maps produced for the entire Italian territory (approximately 30 million hectares) in 2012 by the European Environment Agency, aimed at monitoring soil imperviousness and natural cover, such as forest, grassland, wetland, and water surface, with a high spatial resolution of 20 m. This study presents the methodologies developed for the production, verification, and enhancement of the HRLs in Italy. The innovative approach is mainly based on (a) the use of available reference data for the enhancement process, (b) the reduction of the manual work of operators by using a semi-automatic approach, and (c) the overall increase in the cost-efficiency in relation to the production and updating of land cover maps. The results show the reliability of these methodologies in assessing and enhancing the quality of the HRLs. Finally, an integration of the individual layers, represented by the HRLs, was performed in order to produce a National High-Resolution Land Cover ma

Arene ruthenium (II) complexes with phosphorous ligands as possible anticancer agents

Ruthenium(II) complexes of formula [Ru(?6-arene)Cl2(PTA)] (RAPTA) are potential anticancer drugs with notable antimetastatic and antiangiogenic activity, which are now pointing to clinical trials. Following the great interest aroused by these compounds, a variety of RAPTA derivatives, obtained by chloride substitution and/or containing functionalized arene ligands, and complexes resembling the RAPTA structure but bearing different phosphorous ligands have been synthesized and evaluated for their anticancer activity. An overview of all of these biologically relevant complexes will be given, with particular reference to the anticancer behaviour exhibited by the compounds and the possible relationship with structural aspects

Characterization of Some Stilbenoids Extracted from Two Cultivars of Lambrusco-Vitis vinifera Species: An Opportunity to Valorize Pruning Canes for a More Sustainable Viticulture

Pruning canes from grape vines are valuable byproducts that contain resveratrol and other health-boosting stilbenoids. This study aimed to assess the effect of roasting temperature on the stilbenoid content of vine canes by comparing two Vitis vinifera cultivars, Lambrusco Ancellotta and Salamino. Samples were collected during different phases of the vine plant cycle. One set was collected in September after the grape harvest and was air-dried and analyzed. A second set was obtained during vine pruning in February and evaluated immediately after collection. The main stilbenoid identified in each sample was resveratrol (similar to 100-2500 mg/kg), with significant levels of viniferin (similar to 100-600 mg/kg) and piceatannol (similar to 0-400 mg/kg). Their contents decreased with increasing roasting temperature and residence time on the plant. This study provides valuable insights into the use of vine canes in a novel and efficient manner, which could potentially benefit different industries. One potential use involves the roasted cane chips to accelerate the aging of vinegars and alcoholic beverages. This method is more efficient and cost-effective than traditional aging, which is slow and unfavorable from an industrial perspective. Furthermore, incorporating vine canes into maturation processes reduces viticulture waste and enhances the final products with health-promoting molecules, such as resveratrol

Exploring the Impact of Various Wooden Barrels on the Aromatic Profile of Aceto Balsamico Tradizionale di Modena by Means of Principal Component Analysis

The study examines the unique production process of Aceto Balsamico Tradizionale di Modena PDO (ABTM), emphasizing its complex phases and the impact of raw materials and artisanal skill on its flavor characteristics. Analytical tests focused on the volatile composition of vinegars from different wood barrels at different aging stages, using solid-phase micro-extraction (SPME) coupled with gas chromatography, either with mass spectrometry (GC/MS) or flame ionization detector (FID). Multivariate analysis, including principal component analysis (PCA), was employed to investigate the presence of peculiarities among the volatile profiles of samples of different barrel origin. The research focuses on characterizing the volatile composition of vinegars sourced from individual wood barrels, such as Cherry, Chestnut, Mulberry, Juniper, and Oak. Although it was not possible to identify molecules directly connected to the woody essence, some similarities emerged between vinegar samples from mulberry and cherry barrels and between those of juniper and oak. The former group is characterized by analytes with high molecular weights, such as furfural and esters, while the latter group shows more intense peaks for ethyl benzoate. Moreover, ethyl benzoate appears to predominantly influence samples from chestnut barrels. Due to the highly complex production process of ABTM, where each battery is influenced by several factors, this study’s findings are specific to the current experimental conditions

Early Permian vertebrate ichnofauna from South Alpine Region (Northern Italy): ichnosystematics, paleoecology and stratigraphic meaning

Studies on Early Permian tetrapod ichnofauna emphasized the scarcity of forms from Italian sites. A revision work on the entire collections revealed the presence of Hyloidichnus bifurcatus Gilmore, 1927 and Limnopus heterodactylus (King, 1845). The ichnoassociation now lists seven ichnogenera: Amphisauropus, Batrachichnus, Dromopus, Erpetopus, Hyloidichnus, Limnopus, Varanopus. These new data enlarge the ichnoceonosis, adding tracks of medium-size captorhinomorphs (Hyloidichnus) and temnospondyls (Limnopus) to the Italian ichnofauna, previously characterized by scarcity of predators and amphibians. Radiometric ages give a strong age constraint to the ichnoassociation (Early Kungurian), allowing useful correlations to contemporary successions all over the world. The main difference is the absence of Ichniotherium and Dimetropus, and this could have a stratigraphic or paleoenvironmental significance. The fauna is similar in the two main basins (Collio and Orobic Basins). It differs solely in the proportions between ichnotaxa, with a predominance of areoscelid traces (Dromopus) in the Collio Basin and of captorhinomorph traces (Erpetopus, Varanopus, Hyloidichnus) in the Orobic Basin. This datum could reflect slightly different environments, seasonal in the Collio Basin (alluvial plain) and more arid in the Orobic Basin (playa-like). The lack of some forms in smaller basins of the Athesian Volcanic Complex is probably due to a bias

Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks

Pedestrians and drivers are expected to safely navigate complex urban environments along with several non cooperating agents. Autonomous vehicles will soon replicate this capability. Each agent acquires a representation of the world from an egocentric perspective and must make decisions ensuring safety for itself and others. This requires to predict motion patterns of observed agents for a far enough future. In this paper we propose MANTRA, a model that exploits memory augmented networks to effectively predict multiple trajectories of other agents, observed from an egocentric perspective. Our model stores observations in memory and uses trained controllers to write meaningful pattern encodings and read trajectories that are most likely to occur in future. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on four datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns

MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction

Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not just rely on perceiving present location and motion of other agents. It requires instead to predict such variables in a far enough future. In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network. Our method learns past and future trajectory embeddings using recurrent neural networks and exploits an associative external memory to store and retrieve such embeddings. Trajectory prediction is then performed by decoding in-memory future encodings conditioned with the observed past. We incorporate scene knowledge in the decoding state by learning a CNN on top of semantic scene maps. Memory growth is limited by learning a writing controller based on the predictive capability of existing embeddings. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on three datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns.Comment: Accepted at CVPR2

SMEMO: Social Memory for Trajectory Forecasting

Effective modeling of human interactions is of utmost importance when forecasting behaviors such as future trajectories. Each individual, with its motion, influences surrounding agents since everyone obeys to social non-written rules such as collision avoidance or group following. In this paper we model such interactions, which constantly evolve through time, by looking at the problem from an algorithmic point of view, i.e. as a data manipulation task. We present a neural network based on an end-to-end trainable working memory, which acts as an external storage where information about each agent can be continuously written, updated and recalled. We show that our method is capable of learning explainable cause-effect relationships between motions of different agents, obtaining state-of-the-art results on multiple trajectory forecasting datasets