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Cinzia Bearzot. Hoson dynaton. Studi su Tucidide e Senofonte
No full textHoson Dynaton raccoglie diciotto dei principali studi che Cinzia Bearzot ha dedicato a Tucidide e alle Elleniche di Senofonte, pubblicati nel corso del venticinquennio 2001-2024. Attraverso l’analisi di episodi o di problemi storici, di termini particolari o di figure storiche, le pagine dei vari contributi ricostruiscono le caratteristiche della storiografia tucididea e senofontea e offrono, nel contempo, una comprensione più approfondita del metodo di lavoro dei due storici. Il titolo è un omaggio a Tucidide e al suo metodo storiografico, accurato e scientifico «per quanto possibile», nella consapevolezza che l’obiettivo dello storico è il vero (aletheia), ma che nondimeno la sua ricostruzione si muove nell’ambito del possibile e del probabile
Techniques for Sensitivity Analysis in Severe Accidents: A Comparative Study
No full textAlong the Management and Uncertainty of Severe Accidents (MUSA) project, attention was paid to the unfolding of uncertainty analysis when dealing with severe accident (SA) scenarios. While the quantification of the uncertainty linked to SA simulations’ results was the main focus of the project, some efforts were also addressed to the identification of the variables being the root of it. To this end, a complementary sensitivity analysis was deemed to be of high importance. Following this path, the present paper reports the advancements made in the attempt to enhance and optimize the sensitivity analysis process. More commonly used sensitivity analysis techniques, such as correlation coefficients or simple regression, are complemented by more advanced techniques through the integration of feature selection algorithms. As a further step, a testing phase is foreseen; in particular, the selected sensitivity methods are applied against a SA scenario, namely, an unmitigated station blackout
in a pressurized water reactor. Outcomes according to the different techniques are reported and compared, with a certain level of agreement being shown. The analysis also highlighted the need to support the application of sensitivity methods with expert judgment to corroborate the physical consistency of the obtained results
CERTIFICATION SCHEMES IN THE FOODSERVICE SECTOR: THE CASE OF MEAT
No full textCertifications play a key role in the agri-food and meat industries, where consumers, stakeholders, and legislative frameworks influence production practices and the sector’s economic, social, and environmental impacts. This study analyses certification schemes in the foodservice sector with reference to beef, pork and poultry, the most widely marketed variety of meat in the EU. The objective is to identify which standards attract the authors’ interest and whether the certifications examined vary according to the geographical area or the authors’ country of affiliation. To achieve these goals, this research carries out a systematic literature review using the PRISMA model. Findings indicate that certifications in the foodservice sector mainly cover the domains of animal welfare, food safety and quality, cultural aspects, and consumer behaviour. The findings establish the foundation for further research, which aims to integrate certification schemes into foodservice and support competitiveness of all meat producers of the supply chain in the meat sector
Quantifying Resilience of Cyber-Physical Systems to Zero-Day Threats: A Digital Twin-Based What-If Analysis
No full textTo Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuits
No full textNeuro-symbolic methods enhance the reliability of neural network classifiers through logical constraints, but they lack native support for ontologies. We aim to develop a neuro-symbolic method that reliably outputs predictions consistent with a Description Logic ontology that formalizes domain-specific knowledge. We encode a Description Logic ontology as a circuit, a feed-forward differentiable computational graph that supports tractable execution of queries and transformations. We show that the circuit can be used to (i) generate synthetic datasets that capture the semantics of the ontology; (ii) efficiently perform deductive reasoning on a GPU; (iii) implement neuro-symbolic models whose predictions are approximately or provably consistent with the knowledge defined in the ontology.
We show that the synthetic dataset generated using the circuit qualitatively captures the semantics of the ontology while being challenging for Machine Learning classifiers, including neural networks. Moreover, we show that compiling the ontology into a circuit is a promising approach for scalable deductive reasoning, with runtimes up to three orders of magnitude faster than available reasoners. Finally, we show that our neuro-symbolic classifiers reliably produce consistent predictions when compared to neural network baselines, maintaining competitive performances or even outperforming them. By compiling Description Logic ontologies into circuits, we obtain a tighter integration between the Deep Learning and Knowledge Representation fields. We show that a single circuit representation can be used to tackle different challenging tasks closely related to real-world applications
Polyketone-modified Ti3C2Tx composite coatings for enhanced solid lubrication under elevated stress and oxidative environments
No full textImproving the tribological performance of components and systems remains crucial to improve the resulting mechanical efficiency, durability, and sustainability. This study reports the development of composite coatings based on multilayer Ti3C2Tx (ML- Ti3C2Tx) and chemically modified polyketone (PKHEDA) for enhanced solid lubrication. In this regard, PKHEDA was synthesized via the Paal-Knorr reaction to improve MXenes' dispersion as well as coating's adhesion and chemical stability. Composite coatings with varying MXene-polymer ratios 1:3.3 (COM-1), 1:1.6 (COM-2), and 1:1 (COM-3) wt.-%, were spray-coated onto stainless-steel substrates and characterized using complementary materials characterization and tribo-testing. Our results demonstrate that PKHEDA effectively encapsulates ML- Ti3C2Tx, reducing its oxidation tendency and improving the overall coating integrity under mechanical stress. The tribological performance of the composite coatings was notably enhanced compared to pure Ti3C2Tx coatings and non-coated substrates, thus verifying a stable coefficient of friction and a reduction of the wear rate up to 87 %. The composite with a MXene-to-polymer ratio of 1:1.6 (COM-2) exhibited the best balance of load-bearing capacity, durability, and chemical resilience. These findings highlight the synergistic potential of ML-Ti3C2Tx/polyketone composites to develop high-performance, sustainable coatings for demanding tribological environments
Candida rugosa Lipase Bioconjugation to Cellulose Nanocrystals with High Immobilization Efficiency: Comparison with Nonspecific Approach
No full textNanostructured materials are promising substrates for biocatalyst immobilization. We report a green and sustainable strategy for enzyme immobilization using cellulose nanocrystals (CNCs) derived from renewable sources. CNCs offer biodegradability, low toxicity, and high surface area, enabling efficient immobilization of Candida rugosa lipase (CRL). Covalent bioconjugation on TEMPO-oxidized cellulose nanocrystals (TO_CNCs) provides an almost quantitative immobilization yield without releasing toxic byproducts, but with reduced enzymatic activity per mg of immobilized protein. Conversely, nonspecific immobilization on sulfated cellulose nanocrystals (S_CNCs) shows very low immobilization yield but preserves enzyme mobility and slightly enhances activity. The immobilized biocatalysts were characterized by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, high-resolution synchrotron X-ray diffractometry (XRD), ultraviolet–visible spectroscopy (UV–vis), field emission scanning electron microscopy (FE-SEM), bicinchoninic acid assay (BCA), solid-state nuclear magnetic resonance (ssNMR) spectroscopy, and enzymatic activity measurements. Notably, ssNMR reveals the effectiveness of TO_CNCs in preventing enzyme dispersion
I beni delle Chiese vescovili toscane alla fine del medioevo. Alcune riflessioni a partire dal primo Catasto fiorentino
No full textEnhancing randomized recurrent neural networks with explainable attribution methods
No full textRecurrent Neural Networks (RNNs) are well-suited for temporal data modeling but remain limited by their high training computational cost. As a lightweight alternative, randomized RNNs mitigate this issue by employing a fixed, randomly initialized recurrent layer combined with a simple, trainable output layer. To classify a given input sequence, randomized RNNs usually rely on the final reservoir state, which can be suboptimal when relevant temporal information is sparse or masked by noise. In this work, we investigate how explainable attribution methods can improve the performance of randomized RNNs in classification tasks. In particular, we adopt gradient-based attribution explainability techniques to weigh reservoir states according to their relevance to the final prediction. We theoretically justify the effectiveness of our approach through linear stability analysis, offering geometric intuition via an estimation of the variability of the recurrent dynamics by means of explainability techniques. Our experimental evaluation spans 30 binary and 10 multiclass time series classification tasks, comparing several randomized recurrent models. Results show that explainability-guided weighting can improve classification performance in noisy scenarios
Novel synthesis of flexible polyurethane foams with high bio-based content derived from waste cooking oil
No full textThis study investigates the production and characterization of open-cell flexible polyurethane foams (PUFs) with high bio-based content, using waste cooking oil (WCO) as a precursor for polyols to replace conventional fossil-based polyols. WCO was epoxidized to varying degrees (66–94%) through heterogeneously catalyzed oxidation with Amberlite® IR 120, followed by ring-opening reactions with ethanol to synthesize polyols with hydroxyl numbers ranging from 132 to 177 mg KOH/g. These polyols were then used to produce PUFs through confined expansion, incorporating a partially bio-based diisocyanate, water as a blowing agent, and specifically tailored additives. An isocyanate-to-hydroxyl molar ratio (NCO/OH) of 0.9 was employed to achieve efficient foaming with different crosslinking densities. Comprehensive chemical, morphological, thermal, and mechanical analyses confirmed the successful production of open-cell flexible foams. The results indicated that cell size decreased with an increasing hydroxyl number of the polyol, corresponding to a higher crosslinking density. The foams exhibited an exceptionally high bio-based content of approximately 80 wt.%, densities ranging from 82 ± 1 to 87 ± 1 kg/m3, and compression force deflection (CFD) values at 50% deformation between 6.7 ± 0.5 and 56.5 ± 2.9 kPa. Higher hydroxyl numbers in the polyols resulted in increased CFD values, highlighting the effectiveness of hydroxyl number as a strategy to control foam cellular structure and mechanical properties. These findings demonstrate the potential of WCO-derived polyols as a sustainable and efficient alternative to fossil-based raw materials in the production of flexible PUFs, offering a customizable approach for diverse applications.