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Un principe caduto da cavallo. Il Principe e Gli Eroici di Giovan Battista Pigna tra poetica e politica
No full textTraditionally seen as just a courtly tribute to his Patron, Alfonso II d’Este Duke of Ferrara, Giovan Battista Pigna’s treatise on heroic poetry is actually an attempt to edify ‘the idea of the perfect prince’, in line with his contemporary treatise on the Prince, which from the very title bears the echo of Machiavelli’s lesson. Rather than being only an occupation of a space at the Court, Pigna’s theory (which would happen to be highly influential on Torquato Tasso’s political thinking) is a serious attempt to construct a sort of ‘metaphysics of the Prince’ that goes through all fields of knowledge, from epic poetry to politics and historiography
Integrating diffusion dialysis for sustainable acid recovery from ion exchange regeneration stages: Characterization of metal and non-metal ions migration
Get PDFSeawater mining presents a potential option for recovering the European Union’s Critical Raw Materials (CRMs), but direct extraction from seawater is challenging due to their low concentrations, as most of them are Trace Elements (TEs) (at levels of mg/L or μg/L). Saltworks bitterns (ultraconcentrated brines resulting from the sea salt production process) offer an alternative solution, naturally concentrated up to 40 times more than seawater. These bitterns can be further processed with chelating Ion Exchange (IX) sorbents to selectively extract TEs. However, this process requires an acidic elution stage with strong acids, followed by neutralization, to recover TEs through precipitation, demanding extensive chemicals consumption. Diffusion Dialysis (DD) could be used to recover the excess acid without external reagents, using an acid-resistant Anion Exchange Membrane (AEM). This study evaluates DD through batch and once-through tests for acid recovery from simulated IX eluate generated in the elution stage of TEs (B, Ga, Ge, Co, Sr) recovery from saltworks bitterns.
Batch tests achieved high recoveries for HCl (45–50 %) and H2SO4 (30–37 %), being the theoretical maximum attainable recovery equal to 50 %. B and Ge only partially permeated through the membrane (82 % rejection) by a diffusion mechanism in their neutral form (H3BO3(aq), H4GeO4(aq)). Ga, Co and Sr, in cationic form, were highly rejected (>96 %). Permeability followed the order Ga < Sr < Co < B, due to the relevant charge and size. HCl permeability correlated linearly with concentration, while H2SO4 was inversely proportional. Once-through tests showed higher acid (74 % HCl, 62 % H2SO4) and oxoacid (66 % H3BO3(aq), 52 % H4GeO4(aq)) recovery at a low specific flow rate, or apparent flux, (0.38 L/(m2membrane·h)) due to increased residence time. Water to acid flow rate ratios did not affect species transport when an excess of water was guaranteed. Conversely, an influence was observed when the ratio was below 1, with a minimum at 0.18, where a very low passage of species was observed due to the reduced dilution volume of the dialysate solution (water). A 1D transport model, incorporating the solutes permeabilities determined experimentally, effectively described the system performance, especially for HCl and B, albeit slightly overestimating the other TEs’ transpor
Giovan Battista Pigna, Gli Heroici
No full textScomparso dagli interessi di letterati e studiosi per quasi cinque secoli, il trattato Gli Heroici di Giovan Battista Pigna torna ora in edizione critica, per le cure di Marco De Masi e Stefano Jossa, per rimettere in circolazione una riflessione sulla poesia narrativa e una modalità di costruzione dell'egemonia culturale che segnò il passaggio dallo scrittore di corte al segretario del principe ed ebbe un'influenza decisiva sulla formazione di Torquato Tasso
Understanding data-driven business model innovation in complexity: A system dynamics approach
No full textWith the growing complexity of today’s big data environments, data-driven business model innovation has shown the key features of a complex system, such as dynamics and non-linearity, but relevant research mainly draws a static and linear perspective, which necessitates unveiling data-driven business model innovation as a complex system. To this end, building on complexity theory, this study divides the complex system of data-driven business model innovation into three interdependent subsystems (i.e., big data, business model innovation, and data value). Each subsystem has its more granular components and elements. Then, the system dynamics approach is adopted to clarify the coevolution process and its key influencing factors of data-driven business model innovation. By conducting simulation and sensitivity analysis of key variables, the findings suggest that, like the “flywheel effect”, big data insight, value proposition, customer performance, and firm performance increase with time. Among them, big data insight, value proposition, and customer performance have a basically consistent pace. By contrast, firm performance grows much more slowly at the beginning but has a stronger acceleration in later stages. Besides, improving big data analytics cannot directly increase data value. Only when combined with businesses, it can create marginal benefits, among which business matching is the most salient.
This study not only contributes to the advancement of complexity theory and data-driven business model innovation but also deepens business model research through holistic and systematic approaches
Дидактические функции ошибок: как использовать «ошибки» италоязычных изучающих РКИ при освоении дейктических глаголов движения в русском языке.
Get PDFThe verbs of motion in the Russian language are a challenging topic for learners. Considering this matter from the researcher’s point of view, it is possible to analyse and question the process of learning these verbs and the difficulties involved. Why do we discuss about mistakes? It is because, from a didactic point of view, they are a complex element that hides contradictions manifested in all areas of linguistics. By making mistakes, a person can learn, but also reveals an inability to learn. Working on mistakes involves the teacher and the learner managing information considered incorrect,
identifying the mistake, controlling it, and transforming it into knowledge
ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY IN DISTRIBUTED SYSTEMS
No full textThe rise of distributed systems, encompassing cloud computing, mobile devices, and Internet of Things, has revolutionized modern digital infrastructure but also exposed it to numerous cybersecurity threats. Artificial intelligence (AI), particularly machine learning, has emerged as a powerful tool to enhance security in these distributed environments by detecting and responding to attacks.However, traditional cybersecurity measures are no longer sufficient to address modern threats that constantly evolve to avoid detection. Thus, there is a growing need for adaptive, intelligent security solutions that can keep pace with the dynamic nature of cyber threats.This dissertation presents a comprehensive study of AI techniques to secure networks and networked devices, focusing on the challenge of ensuring that security systems remain effective in the face of evolving threats.The main hurdle in this task is obtaining adequate up-to-date training data in a timely fashion to build robust models.To this end, unsupervised learning methods are explored to detect network intrusions, with a focus on collaborative approaches that leverage the capabilities of multiple devices.For supervised malware classification tasks, Federated Learning (FL) is identified as a promising approach to enable crowdsourced security solutions while preserving the participants' privacy.In addition, novel approaches are developed to ensure that the model performance remain robust over time, even when the data distribution changes.Þspite the potential of FL, the lack of oversight on client behavior can lead clients to deviate from the prescribed learning protocol to obtain unfair advantages.Thus, this dissertation also analyzes the impact of these clients on the model's performance and fairness, and proposes an approach to mitigate their influence,and an incentive mechanism to align the client goals with the server is presented.Additionally, a personalization mechanism is introduced to ensure that each participant obtains a model well-suited to their current local data distribution at any given time
Modelli di multilinguismo nella didattica fra Senegal e Italia. Una ricerca e una sperimentazione sul campo.
Get PDFDesign, Synthesis, and In Silico Insights of new 4‐Piperazinylquinolines as Antiproliferative Agents against NCI Renal Cancer Cell Lines
Get PDF: In the search for new anticancer compounds, quinoline and piperazine moieties represent the most promising pharmacophoric fragments for the development of more effective drugs. A particularly interesting approach in medicinal chemistry is molecular hybridization, where different known components are integrated into a single chemical entity, resulting in hybrid molecules with enhanced biological activity. In this study, we have developed a new series of 4-(4-benzoylpiperazin-1-yl)-6-nitroquinoline-3-carbonitrile compounds (8 a-l), with potential anticancer effect, by combining the quinoline, the piperazinyl and the benzoylamino moieties. The rationalized compounds (8 a-l) were first evaluated in silico to assess the ADMET and drug-likeness profiles, synthesized using appropriate synthetic strategies and then tested in vitro under the National Cancer Institute DTP-NCI60 program. The entire series exhibited potent anticancer activity against the renal cell carcinoma (RCC) cell line UO-31, with compounds 8 c and 8 g effectively inhibiting cancer cell growth without excessive cytotoxic effects (growth percentages of -7 and -19, respectively). In silico induced fit docking (IFD) and molecular dynamics (MD) studies provided further insights into the putative mechanisms of action for both compounds, which were predicted to strongly bind key oncogenic proteins involved in RCC progression. The promising in vitro and in silico results herein presented provide a solid foundation for the development of a new series of small heterocyclic molecules with anticancer activity
Statistically validated network for analysing textual data
Get PDFThis paper presents a novel methodology, called Word Co-occurrence SVN topic
model (WCSVNtm), for document clustering and topic modeling in textual datasets.
This method represents the corpus as a bipartite network of words and documents
to rigorously assess the statistical significance of word co-occurrences within documents
and document overlap based on shared vocabulary. By employing the Leiden
community detection algorithm to the SVN, distinct communities of words can be
identified and interpreted as topics. Similarly, documents can be sorted into groups
based on their thematic similarities. We demonstrate the effectiveness of our approach
by analyzing three datasets: a set of 120 Wikipedia articles, the arXiv10 dataset, which
consists of 100,000 abstracts from scientific papers, and a sampled subset of 10,000
documents from the original arXiv10. To benchmark our results, we compare our
approach with several well-established models in the field of topic modeling and document
clustering, including the hierarchical Stochastic Block Model (hSBM), BERTopic,
and Latent Dirichlet Allocation (LDA). The results show that WCSVNtm achieves competitive
performance across all datasets, automatically selecting the number of topics
and document clusters, whereas state-of-the-art methods require prior knowledge
or additional tuning for optimization. Finally, any advancements in community detection
algorithms could further improve our method
An Inclusive supply chain model for the treatment of respiratory diseases based on Personalized medicine through modern biosensing devices
No full textThe relentless escalation of healthcare costs and the ageing population process in industrialized countries threaten the long-term sustainability of modern healthcare systems. In particular, respiratory diseases, affecting around 500 million people globally, are a most relevant cause of inability and mortality worldwide and a main contributor to global healthcare spending. Increasing the coverage of healthcare systems in order to strengthen prevention and diagnostics is regarded as the only viable possibility to reduce the costs of treatment while maintaining or improving the quality of patient care. In such regard, the recent technological advances in biosensing and ICT technologies offer a substantial opportunity to implement advanced self-testing systems allowing for the provision of territorial servicers in coherence with the modern approaches to patient-centric and personalized medicine. Most of the testing operation in respiratory medicine, however, are currently performed in specialized labs, with costly and bulky equipment operated by professional personnel, thus making decentralized healthcare approaches a hardly viable and economically sustainable solution. The lack of effective screening and prevention programs, the significant incidence of underdiagnosed or late-diagnosed cases complicates the treatment of respiratory diseases and increases the overall costs. In such context this paper proposes a novel personalized testing method for respiratory diseases, and discusses the achievable benefits compared to traditional “Point of Care” (POC) and lab-based testing, thus offering an original contribution to the scientific debate on the effectiveness of decentralized healthcare Supply Chain (SC) models and some relevant insights on self-testing and community healthcare. Based on the results obtained, the proposed personalized testing method emerges a viable possibility to establish affordable screening and prevention of respiratory diseases, thus improving the inclusiveness of the services while preserving the economic sustainability of the healthcare system