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Portfolio-scale seismic fragility of RC bridge columns with series-distributed neural networks
No full textThis paper proposes a novel series-distributed artificial neural network framework for rapidly constructing seismic fragility curves of reinforced-concrete (RC) bridge columns at markedly reduced computational cost. Three coupled surrogate models are trained on datasets generated from nonlinear time-history and pushover analyses of RC piers with randomly sampled geometric and material properties subjected to hazard-consistent ground motions. The first network learns correlations among a reduced set of efficient ground-motion intensity measures (IMs), the second predicts drift demand from IMs and modelling parameters, and the third provides drift capacities for multiple damage states directly from capacity-curve information, thereby incorporating epistemic uncertainty in structural capacity. The trained surrogates are embedded in a Monte Carlo simulation scheme to estimate, in a largely non-parametric manner, the probability that drift demand exceeds capacity at each IM level. A case study on a portfolio of simply supported bridges in the Da Nang area, including selected bridges along National Highway 1A, demonstrates that the framework reproduces benchmark fragility curves from nonlinear analyses while achieving substantial reductions in analysis time. The results highlight systematic differences between rectangular and circular piers and quantify the impact of relaxing internal lognormal assumptions relative to traditional cloud-based fragility derivation. The proposed approach is implementation-ready, which relies on standard structural and ground-motion descriptors, delivers conventional fragility parameters, and is readily scalable to portfolio- and network-level seismic risk assessments and screening
Artificial Intelligence and Sustainable Tourism: An Integrated Model for Impact Assessment
No full textIn recent years, among the various potential applications of Artificial Intelligence (AI), its ability to serve as a useful tool for data implementation and analysis in various monitoring and impact assessment processes has become increasingly evident. This paper presents an example of AI being used as an integration and support tool for impact assessment in the context of sustainable tourism. In 2023, it is estimated that there were 1.286 billion international tourists worldwide. Specifically, Europe was the most visited region, largely due to domestic demand and travel from the United States. In 2013, the European Commission launched the European Tourism Indicator System (ETIS), consisting of a set of indicators designed to assess tourism sustainability, support destinations, and monitor and measure their performance. This research aims to define an evaluation model that considers these indicators through a weighted system. To determine the specific weight of each indicator and assess the usefulness of AI systems, the paper presents the results of a study aimed at identifying discrepancies and convergences in output data obtained in two ways:Using a traditional model, (stakeholders’ consultation).Through AI-based interrogation. Using a traditional model, (stakeholders’ consultation). Through AI-based interrogation. In the proposed case study, a questionnaire will be used and administered to stakeholders in the sector as well as to major AI software systems. The input data will be carefully structured to enable AI to function as a knowledgeable expert in the tourism sector
Valorizing Coffee Waste into N-, P-, O-Heteroatom-Doped Carbons for Metal-Free Electrocatalysis
No full textSpent coffee grounds were valorized as a renewable feedstock to prepare N-, P-, and O-codoped activated carbons via pyrolysis, targeting the development of metal-free electrocatalysts for the oxygen reduction reaction (ORR). Hexachlorocyclotriphosphazene (HCCP) was used as a doping agent, and the influence of different porosity activators such as NH4HCO3, KOH, and oxalic acid was studied. This work employs NH4HCO3 as a benign, metal-free activator and secondary nitrogen source compared against conventional porosity agents. Among the mild activators, NH4HCO3 simultaneously provided nitrogen and released NH3 and CO2, while oxalic acid generated a clean CO2 stream, both contributing to mesoporous structure formation. XPS confirmed the successful incorporation of N and P into the carbon matrix, with a high fraction of graphitic nitrogen enhancing the conductivity and catalytic activity. Raman spectroscopy, TEM, and XRD analyses revealed predominantly sp2-hybridized, turbostratic carbon, with higher structural order at 900 degrees C compared to 700 degrees C. The combined effect of pyrolysis temperature, HCCP, and NH4HCO3 produced mesoporous, nitrogen-rich graphitic carbon structures with promising ORR performance, demonstrating a green strategy that maximizes atom economy, minimizes energy input and waste, and transforms low-value biomass into high-performance metal-free electrocatalysts
Efficient hosting capacity computation using the bisection method for rapid grid capacity assessment
No full textThis paper introduces a novel approach for computing Hosting Capacity (HC) by employing the bisection method as an alternative to the traditional step method. The primary objective is to achieve nodal and global HC results with significantly reduced computation time while preserving the accuracy characteristic of the step generator method. This fast and reliable technique is particularly well-suited for the iterative calculations required by large energy utilities to determine the maximum grid capacity for integrating new loads and distributed energy resources. The proposed method leverages grid linearization, incorporating the concept of compensation admittance at each node. The validity and effectiveness of the bisection-based HC computation are demonstrated through a comparative analysis against classical step method results, confirming its accuracy and computational efficiency
Detachments detection at the ‘Grand Stairway’ in the Room 38 of the Domus Aurea using the PICUS system
No full textThe purpose of this research was the diagnosis of the detachments and the analysis of the subsequent stabilization intervention of antique roman plaster in Room 38 of the Domus Aurea . We carefully assessed the extent of the detachments in the fresco-decorated plaster before initiating the stabilization intervention. Two methods were implemented: manual auscultation and automatic scanning using the PICUS system. Both produced a map representing the defects prior and after the intervention. The comparison between the map obtained by the auscultation method and the PICUS map shows that they can be superimposed. The map obtained with the PICUS system highlights the most severe defects using a colorimetric scale, which is normally not used in a manual scan. The PICUS system has proven to be a valid support to the classical manual auscultation to prepare the map of the defects of antique, damaged cultural heritage
Towards Safer Freight Rail Shunting: Integrating MILP and ML Classification Models in a Risk Management Framework
No full textThis paper proposes a novel risk analysis framework for the optimization of rolling stock management in rail freight shunting operations. We challenge the direct application of Machine Learning (ML) as input for operational decision-making by employing Risk Assessment strategies to evaluate how ML predictions affect the decision-making process. Our approach integrates the ML model’s performance metrics into a Mixed-Integer Linear Programming (MILP) model for shunting operation. A comparative analysis based on real data from the Luxembourgish rail freight company CFL Multimodal across various destinations reveals that a risk assessment approach provides superior performance compared to the direct use of the ML input, reducing the analyzed KPIs. This study demonstrates that the use of a risk assessment framework helps mitigate potential for operational inefficiencies and unfeasibility inherent in ML-dependent models
Presupposition, assertion, and epistemic vigilance across development
No full textThis study investigates how linguistic framing—specifically, the use of presupposition versus assertion—affects the critical evaluation of incoming information across development. While presuppositions present content as taken for granted, assertions introduce information as new, leading to differing levels of epistemic scrutiny. Prior work has shown that adults are less likely to detect falsehoods when they are presupposed rather than asserted. This study explores whether similar framing effects operate in childhood, and whether children's sensitivity is modulated by Information Structure—specifically, topic–focus articulation. To this end, we tested three age groups (7-year-olds, 10-year-olds, and adults) using a truth-evaluation task involving short videos and spoken sentences that either asserted, focally presupposed, or topically presupposed false information. Results revealed that across all age groups, presuppositions increased the likelihood of accepting false statements as true, indicating their potential to mislead. However, this effect varied with age: compared to adults, the impact of topical presuppositions was especially pronounced in 10-year-olds, and this stronger effect was possibly present in 7-year-olds as well. The results have implications for theories of pragmatic development, linguistic models, and practices of epistemic vigilance, with practical relevance for understanding children's susceptibility to misleading or manipulative content
I TRIBUTI STATALI. L'IMPOSTA SUL REDDITO DELLE PERSONE FISICHE. LE CATEGORIE DI REDDITO. PARTE I, CAPITOLO I, SEZ. II
No full textANALISI DELLA DISCIPLINA GIURIDICA DELLE CATEGORIE DI REDDITO NEL'AMBITO DELL'IRPEF