IL CONCORDATO PREVENTIVO CON CESSIONE DEI BENI: ASPETTI TEORICO-PRATICI

L'elaborato ha lo scopo di illustrare la fase esecutiva del concordato con cessione dei beni. Nel primo capitolo si individua la fattispecie oggetto delle disposizioni di cui all'art. 182 l.f. Nel secondo capitolo si descrivono gli organi della procedura, mentre nel terzo si affronta l'argomento chiave dell'elaborato ovvero l'analisi della liquidazione attuata in fase esecutiva di un concordato con cessione dei beni. La problematica è stata affrontata con un taglio teorico-pratico, in modo da evidenziare anche gli aspetti procedurali che caratterizzano l'intera fase esecutiva

Semantic mapping of decay and materials through Artificial Intelligence and integrated H-BIM management

International audienceNowadays, the digital documentation of architectural heritage necessarily requires the integration of different types of representation and the organization of information on different levels, in order to plan adequate restoration and conservation operations [1]. Semantic segmentation techniques relying on Artificial Intelligence are emerging in this field as privileged tools to appropriately organize, structure and classify the complex system of analytical and survey data related to an architectural object or site [2]–[4].In this contribution, semi-automatic classification methods are exploited in order to associate the different semantic and descriptive information to the raw outputs of the three-dimensional survey and H-BIM based representations are finally created to display the results. The case study on which the methodological approach is tested is a church located in Pisa (Italy), Chiesa del Carmine: the classification is performed on the liturgical decorative apparatus of the church, and the textured meshes of the altars are analyzed in order to characterize the state of preservation, and in particular the material and decay mapping of these objects.Taking as input data the raw 3D models derived from laser scanning or photogrammetry, a supervised Machine Learning (ML) algorithm is applied in order to read, classify and return different degrees of degradation and/or types of materials of the altars. In detail, starting from the 3D survey, orthophotos and UV maps are generated. On these, the classes of materials or the levels of the degradation are identified and annotated over a reduced portion, and this constitutes a set of samples on which the ML model is trained. The training data are also supported by the so-called features, i.e. radiometric or geometric characteristics that allow to distinguish one class from another [5].A predictive model (Random Forest) is trained on these data so as to foresee and map the classification of the entire dataset. Once this supervised classification is performed on the orthophoto or UV map, the results are projected onto the 3D by exploiting the projective relationships between images and model, and this allows to obtain an overall mesh model in which different colors correspond to different degrees of degradation or different types of material.This distinction into classes is preserved even at a final stage, when the classified mesh models derived from the segmentation process are inserted within specific H-BIM platforms, in a Scan-to-BIM application. To this end, portions of mesh having different material and degradation characteristics are imported into BIM platforms thanks to visual programming algorithms implemented in Rhino’s Grasshopper. This step ensures the autonomous management and informatization of each class derived from segmentation, and the semantic data can be more easily shared, retrieved, visualized and stored, also in view of the use of heritage models for augmented reality applications.The results obtained in terms of description and semantic mapping of the model and of traceability and retrieval of information in H-BIM environment suggest the extension of the proposed methodological approach to the study of ornamental apparatuses related to other churches of the Carmelite order

Semantic mapping of decay and materials through Artificial Intelligence and integrated H-BIM management

International audienceNowadays, the digital documentation of architectural heritage necessarily requires the integration of different types of representation and the organization of information on different levels, in order to plan adequate restoration and conservation operations [1]. Semantic segmentation techniques relying on Artificial Intelligence are emerging in this field as privileged tools to appropriately organize, structure and classify the complex system of analytical and survey data related to an architectural object or site [2]–[4].In this contribution, semi-automatic classification methods are exploited in order to associate the different semantic and descriptive information to the raw outputs of the three-dimensional survey and H-BIM based representations are finally created to display the results. The case study on which the methodological approach is tested is a church located in Pisa (Italy), Chiesa del Carmine: the classification is performed on the liturgical decorative apparatus of the church, and the textured meshes of the altars are analyzed in order to characterize the state of preservation, and in particular the material and decay mapping of these objects.Taking as input data the raw 3D models derived from laser scanning or photogrammetry, a supervised Machine Learning (ML) algorithm is applied in order to read, classify and return different degrees of degradation and/or types of materials of the altars. In detail, starting from the 3D survey, orthophotos and UV maps are generated. On these, the classes of materials or the levels of the degradation are identified and annotated over a reduced portion, and this constitutes a set of samples on which the ML model is trained. The training data are also supported by the so-called features, i.e. radiometric or geometric characteristics that allow to distinguish one class from another [5].A predictive model (Random Forest) is trained on these data so as to foresee and map the classification of the entire dataset. Once this supervised classification is performed on the orthophoto or UV map, the results are projected onto the 3D by exploiting the projective relationships between images and model, and this allows to obtain an overall mesh model in which different colors correspond to different degrees of degradation or different types of material.This distinction into classes is preserved even at a final stage, when the classified mesh models derived from the segmentation process are inserted within specific H-BIM platforms, in a Scan-to-BIM application. To this end, portions of mesh having different material and degradation characteristics are imported into BIM platforms thanks to visual programming algorithms implemented in Rhino’s Grasshopper. This step ensures the autonomous management and informatization of each class derived from segmentation, and the semantic data can be more easily shared, retrieved, visualized and stored, also in view of the use of heritage models for augmented reality applications.The results obtained in terms of description and semantic mapping of the model and of traceability and retrieval of information in H-BIM environment suggest the extension of the proposed methodological approach to the study of ornamental apparatuses related to other churches of the Carmelite order

Semantic Mapping of Architectural Heritage via Artificial Intelligence and H-BIM

Starting from the virtual photogrammetric 3D reconstruction, this work proposes a classification method, based on Artificial Intelligence, allowing to semi-automatically characterize the digital models of existing architectural heritage in terms of material mapping and/or decay condition. The obtained data, once classified, is used and transferred in BIM environment, so to favor the construction of informative models rich in analytical content. The proposed approach is described with reference to the significant case study of the Chiesa del Carmine in Pisa, for the study and restitution of the liturgical and decorative apparatus, as part of a large-scale research project, still underway, on the reconstruction of the tramezzo screens for the churches of the Mendicant orders

Magnetic Circular Dichroism Elucidates Molecular Interactions in Aggregated Chiral Organic Materials

Chiral materials formed by aggregated organic compounds play a fundamental role in chiral optoelectronics, photonics and spintronics. Nonetheless, a precise understanding of the molecular interactions involved remains an open problem. Here we introduce magnetic circular dichroism (MCD) as a new tool to elucidate molecular interactions and structural parameters of a supramolecular system. A detailed analysis of MCD together with electronic circular dichroism spectra combined to ab initio calculations unveils essential information on the geometry and energy levels of a self-assembled thin film made of a carbazole di-bithiophene chiral molecule. This approach can be extended to a generality of chiral organic materials and can help rationalizing the fundamental interactions leading to supramolecular order. This in turn could enable a better understanding of structure-property relationships, resulting in a more efficient material design.Magnetic circular dichroism gives access to the geometry and interactions leading to the supramolecular structure of a thin film of an organic chiral molecule. The technique presented here may be applied to elucidate the structures of aggregates of organic compounds.+imag

The initial phase of the 2021 Cumbre Vieja ridge eruption (Canary Islands): Products and dynamics controlling edifice growth and collapse

Tajogaite cone in the Cumbre Vieja ridge (La Palma, Canary Islands) erupted between 19 September and 13 December 2021. The tephra and lava sourced from the newly formed fissure rapidly built a pyroclastic cone. During the early days of eruption and after several small-scale landslides, the west flank of the edifice partially collapsed on 25 September, breaching the cone and emplacing a prominent raft-bearing lava flow. Our research combines direct observations, digital elevation models, thermal and visible imaging, and textural and compositional investigation of the explosive products to describe and characterize the edifice growth and collapse. The cone built over a steep slope (26°) and its failure occurred after an intense phase of lava fountaining (up to 30 m3 s−1) that produced rapid pyroclastic accumulation. We suggest that an increased magma supply, to an ascent rate of 0.30 m s−1, led to the rapid growth of the cone (at 2.4 × 106 m3 day−1). Simultaneously, the SW lava flow reactivated and formed a lava ‘seep’ that undercut the flank of the cone, triggering a lateral collapse via rotational rockslide that moved at minimum speeds of 34–70 m h−1. The lateral collapse formed a ~ 200 m wide scar, involving 5.5 × 106 m3 of material, and covered 1.17 km2 with decametric edifice portions and raft-bearing lava. The collapse produced a modest change in the vent geometry, but did not affect eruptive activity long term. A short pause in the eruption after the collapse may have been favored by rapid emptying of the shallower magma system, reducing ascent rates and increasing crystallization times. These results reveal the complex chain of events related to the growth and destruction of newly formed volcanic cones and highlight hazards when situated close to inhabited areas