Higher Institute on Territorial Systems for Innovation
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Digitalizzazione integrata e prospettive per la gestione del patrimonio industriale: il caso del Civico Museo Setificio Monti
Get PDFAdvances in surveying and digitization techniques have increased access to complex data about architecturalheritage. For historic industrial buildings, which feature diverse construction layers, specific materials, and intricate spaces,creating an information system capable of collecting, organizing, and providing access to this data is crucial forunderstanding, monitoring, and planning interventions. A 3D digital model of the building is ideal for gatheringmultidisciplinary data necessary for effective conservation. However, it is vital to explore simpler, maintainable solutionsthat follow open standards, can handle diverse data types, and are accessible to technicians, researchers, and museum staffwithout specialized training. This study proposes using point clouds as the core of an information system for the CivicMuseum of Setificio Monti in Abbadia Lariana, a prominent mid-19th-century silk-spinning mill. The system combineshistorical documentation, diagnostic surveys, and maintenance data to support conservation planning and make industrialheritage information easily accessible and interoperable
An Engineering-Based Methodology to Assess Alternative Options for Reusing Decommissioned Offshore Platforms
Get PDFIn the current context of the energy transition, the reuse of offshore oil and gas (O&G) structures that have reached the end of their operational life presents new engineering challenges. Many projects aim to adapt existing facilities for a range of alternative uses.
This paper outlines guidelines for identifying the most suitable conversion options aligned with the goals of the ongoing energy transition, focusing on the Italian offshore area. The
study promotes the reuse—instead of partial or full removal—of existing offshore platforms originally built for the exploitation of hydrocarbon reservoirs. From an engineering perspective, the project describes the development of guidelines based on an innovative methodology to identify new uses for both offshore oil and gas platforms and the depleted reservoirs, with a focus on safety and environmental impact. The guidelines identify the most suitable and effective conversion option for the platform – reservoir system under consideration. To ensure a realistic approach, the developed methodology allows one to identify the preferable conversion option even when some piece of information is missing or incomplete, as often happens in the early stages of a feasibility study. The screening process provides an associated level of uncertainty related to the degree of data incompleteness. The outcome is a complete evaluation procedure divided into five phases: definition of criteria; assignment of an importance scale to determine how critical each criterion is; connection of indices and weights to each criterion; and analysis of the relationships between them. The guidelines are implemented in a software tool that supports and simplifies the decision-making process. The results are very promising. The developed methodology and the related guidelines applied to a case study have proven to be an effective decision-support for analysts. The study shows that it is possible to identify the
most suitable conversion option from a technical, engineering, and operational point of view while also considering its environmental impact and safety implications
Thermal Data Optimization Through Uncertainty Reduction in Fatigue Limits Estimation: A TCM–ANN Framework for C45 Steel
Get PDFThe combination of both Passive Thermography and machine learning in materials science and engineering allows rapid progress in advanced fatigue analysis. Focusing on mechanical aspects, the combination of these approaches is capable of interpolating the fatigue resistance in diverse conditions with minimal data, when compared to the classical solution, in which analyses are conducted using statistical processes such as the Staircase Method. Even though the thermal increment and thermal area are crucial parameters for the fatigue limit analysis, the implementation of machine-learning interpolation improves data consistency and reduces variability in the fatigue limit estimation through Type-A repeatability uncertainty reduction. This way, the two-layer artificial neural network does not have any predefined form of functions; second, it maintains the inherent non-linear features of the data. The validation of the proposed approach was conducted for a C45 steel, and two different experimental campaigns were conducted using a resonant machine. At the end, the analysis of the fatigue limit was conducted by means of an interpolationassisted Two-Curve Method, starting from the classical thermal data evolution properly optimized with a machine-learning approach, achieving a more precise result in estimating the fatigue limit
Artificial intelligence in healthcare: Proposal for a new medico-legal methodology in medical liability
Get PDFThe rapid integration of Artificial Intelligence (AI) into healthcare promises significant benefits but also raises unprecedented ethical, clinical, and legal challenges. Current medico-legal frameworks, primarily designed for human decision-making, are often inadequate to address liability issues arising from algorithmic errors or opaque "black box" models. This paper introduces a novel medico-legal methodology that combines proactive and reactive approaches to risk assessment, originally developed within European forensic medicine, and adapts it to the context of AI in healthcare. By systematically analyzing data collection, dataset validation, error identification, and causal reconstruction, the proposed framework provides a structured path for evaluating medical liability when AI systems are involved. This dual approach not only supports clinicians, developers, and policymakers in preventing harm, but also establishes a robust forensic tool for liability assessment. The methodology offers a step toward internationally applicable standards for addressing the medico-legal implications of AI in medicine
A versatile readout system for front-end ASICs with HLS-based hardware-accelerated processing capabilities
Get PDFThis paper presents a versatile readout system for particle detector front-end ASICs based on the AMD Zynq Ultrascale+ System-on-Chips. The system is suitable for both extensive laboratory characterization and for data acquisition at test beam facilities. Its software-level scripting of the test procedure reduces the firmware development effort, maximizing the system reusability among different DUTs. At the same time, the integration with High-Level Synthesis flows allows the deployment of real-time data processing algorithms in hardware, offloading the ARM processor. The system has been tested in different use cases, including the mixed-signal data acquisition from monolithic CMOS sensors for timing applications at test beam facilities and the readout of CMOS sensors for tracking and tomographic imaging
An explicitly solvable NLS model with discontinuous standing waves
No full textWe study the NLS Equation on the line with a point interaction given by the superposition of an attractive delta potential with a dipole interaction, in the cases of L2-subcritical and L2-critical nonlinearity. For a subcritical nonlinearity we prove the existence and the uniqueness of Ground States at any mass. If the mass exceeds an explicit threshold, then there exists a positive excited state too. For the critical nonlinearity we prove that Ground States exist only in a specific interval of masses, while in a different interval excited states exist. We provide the value of the optimal constant in the Gagliardo-Nirenberg estimate and describe in the dipole case the branches of the stationary states as the strength of the interaction varies. Since all stationary states are explicitly computed, ours is a solvable model involving a non-standard interplay of a nonlinearity with a point interaction
An algorithm for the estimation of the segmental Lebesgue constant
No full textThe main goal of this work is to provide an explicit algorithm for the estimation of the segmental Lebesgue constant, an extension of the nodal Lebesgue constant that arise, for instance, in histopolation problems. With the help of two simple but efficacious lemmas, we reverse the already known technology and sensibly speed up the numerical estimation of such quantities. Results are comparable with the known literature, although cpu time of the presented method is sensibly smaller. It is worth pointing out that the numerical approach is the only known for analyzing the majority of families of supports
The conjecture of carrying capacity in cancer: Thermodynamic implications
Get PDFIn this study, we explore Deisboeck and Z. Wang’s conjecture from 2007, which establishes a quantitative relationship between the spatial expansion of tumours and the volume constraints as well as functional variations of the surrounding host tissue. By integrating the physical properties of cells into this mechanistic framework, we provide a robust thermodynamic interpretation based on cellular internal energy. Our findings decisively characterise the conditions that facilitate coexistence between tumours and host organs at first, while also identifying the factors that eventually drive the competitive transformation of tumour cells through spatial dissemination-specifically, local invasion
into healthy tissue followed by distant metastasis. We underscore the critical necessity of vascularisation from an energetic perspective and highlight the existence of an ‘entropy threshold’ for cancer invasion, which mirrors a phase transition in physics, as revealed through the application of the second law of thermodynamics
Artificial Intelligence and Extended Reality for Communicating the Uses of Natural Fibers in Building Construction: State of the Art and New Proposals
No full textThis contribution presents a digital framework to promote and communicate the use of natural fibers in building construction, developed within the “Circular Design for Natural Fibers” (CD4NF) project. The research addresses the limited application of advanced digital tools in this emerging field by proposing an integrated workflow that synergizes Building Information Modeling (BIM), Artificial Intelligence (AI), and eXtended Reality (XR). The proposal’s core is a flexible and modular platform built upon a structured material filing system that catalogs detailed information on various natural fibers. This platform utilizes BIM as a central data repository. An AI system, based on a Retrieval-Augmented Generation (RAG) model, enables users to query this complex data using natural language. XR applications provide an interface for visualizing and interacting with the BIM models and their associated information in real-world contexts. This approach aims to optimize key processes from material research and design to construction, enhancing decision-making, interoperability, and communication. The system represents a step towards a more efficient, circular, and sustainable construction sector by facilitating the informed adoption of natural fiber-based materials