An Analytical Review of Cyber Risk Management by Insurance Companies: A Mathematical Perspective

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions

Il Data Protection Officer (DPO): il Responsabile per la Protezione dei Dati

SOMMARIO: 1. La figura del Data Protection Officer (DPO) e la normativa di riferimento – 2. La formazione del DPO e i requisiti per la nomina o designazione – 3. Il ruolo del DPO: compiti e responsabilità – 3.1. Il DPO e i registri delle attività di trattamento – 3.2. Il soggetto responsabile per la protezione dei dati all’interno o all’esterno – 3.3. Durata del rapporto e rinnovo dell’incarico – 4. Assenza di responsabilità “esterna” del DPO per i trattamenti dell’ente designante e per i trattamenti attuati nell’esercizio delle proprie funzioni – 5. Responsabilità del DPO per inadempimento: inquadramento – 5.1. (segue): princìpi applicabili – 5.2. Allocazione di responsabilità tra designante e DPO – 6. Profili di responsabilità in caso di DPO in conflitto di interessi – 7. Effetti del conflitto di interessi sul contratto

Dimensioni e questioni della diseguaglianza di genere. Studi multidisciplinari

Il volume affronta il tema della dimensione di genere nel lavoro, nel sistema produttivo e nella società con diverse prospettive scientifico disciplinari e con diversi approcci metodologici. I contributi forniscono un insieme estremamente ricco e fecondo di analisi e approfondimenti che, da un lato, mettono in evidenza la multidimensionalità della questione della (dis)eguaglianza di genere e, dall’altro lato, confermano l’importanza della prospettiva multidisciplinare per cogliere le caratteristiche profonde di un fenomeno complesso e ancora in attesa di avanzamenti significativi verso la conquista della parità sostanziale

Study of the response of a large volume TPC prototype for the CYGNO experiment at LNGS

Astrophysical and cosmological observations have provided strong evidence that a significant portion of the Universe is composed of non-luminous matter, known as Dark Matter (DM). The Standard Model (SM) of particle physics successfully describes the fundamental particles and their interactions but does not account for any Dark Matter particle candidate. One of the most supported hypotheses suggests that DM is composed of particles different from those in the SM. Among the most promising candidates are Weakly Interactive Massive Particles (WIMPs), which are non-relativistic particles that interact weakly with SM particles. A stable, weakly interacting particle in thermal equilibrium in the early Universe would be capable of explaining the observed relic DM density. The motion of the Sun and the Earth with respect to the rest frame of the Galaxy produces an apparent "wind" of WIMPs coming from the direction of the Cygnus constellation. Assuming DM can interact with SM particles, it is possible to exploit the weak interaction with regular matter measuring the recoils induced by DM interactions. This is known as direct detection. The fundamental strategy is to expose a large amount of instrumented mass and wait for DM to produce recoils in it. Direct detection experiments look for nuclear recoils of low energy, 1-100 keV, with an expected rate below 1 event/kg/year. The low expected event rate implies detectors with extremely challenging requirements on the background reduction techniques, such as operating detectors underground to suppress cosmic rays, using radiopure materials, and implementing active or passive shielding. The CYGNO project aims to build a large O(30m 3 ) directional detector for rare event searches, including Dark Matter. The detector uses a gaseous Time Projection Chamber (TPC), which is sensitive to recoil topology and allows direction measurement. The TPC is filled with a gas mixture rich in helium and fluorine at atmospheric pressure, making it sensitive to both Spin-Independent and Spin-Dependent interactions. A triple Gas Electron Multiplier (GEM) stack provides ionization signal amplification, and the signals are optically read out using photomultiplier tubes (PMTs) and scientific CMOS cameras, enabling 3D track reconstruction. Several CYGNO prototypes have been built and tested in both overground and underground environments to assess their performance. Currently, the CYGNO project is at the end of the R&D phase with the Long Imaging ModulE (LIME), the largest prototype built, comprising a 50 liters active volume. After commissioning overground at Laboratori Nazionali di Frascati (LNF), LIME was installed underground at Laboratori Nazionali del Gran Sasso (LNGS) in February 2022. This underground operation marks a significant milestone in the CYGNO roadmap towards constructing a large-scale TPC for directional Dark Matter searches

Role of extracellular trafficking of neuroglobin in neurodegeneration and neuroprotection

Cellular survival relies on the regulation of growth, metabolism, and stress responses. Neurons, due to their post-mitotic nature, are particularly vulnerable and irreplaceable. In fact, in response to stress, they activate protective mechanisms, but excessive stress can lead to apoptosis. This progressive neuronal loss underlies neurodegeneration, contributing to aging and disorders such as Alzheimer’s, Parkinson’s, Huntington’s diseases and amyotrophic lateral sclerosis (Kole et al., 2013; Wilson III et al., 2023). Although the exact causes of these diseases remain unclear, they share common features including oxidative stress, mitochondrial dysfunction, and the accumulation of misfolded proteins, which induce endoplasmic reticulum (ER) stress and disrupt cellular homeostasis. Furthermore, neurodegenerative diseases are characterized by impaired autophagy, altered glucose metabolism, genetic mutations, and structural changes in neurons. Chronic inflammation exacerbates neuronal damage, accelerating disease progression. Given the rising prevalence of neurodegenerative diseases, largely driven by an aging population, there is an urgent need to identify new therapeutic targets. Current treatments are predominantly palliative, highlighting the importance of exploring alternative approaches (Marmolejo-Martìnez-Artesero et al., 2021; Wilson III et al., 2023). Consequently, recent research has focused on enhancing the intrinsic stress response mechanisms of neurons, with neuroglobin (NGB) emerging as a promising neuroprotective factor. NGB, a monomeric heme-globin, is primarily expressed in neurons of both the central and peripheral nervous systems (Burmester et al., 2000). Since its discovery in 2000, NGB has been associated with neuroprotective properties against various stressors linked to neuronal death, such as oxidative stress, hypoxia, ischemia, and oxygen/glucose deprivation (Fiocchetti et al., 2021). Intracellular NGB expression has been connected to several cell-autonomous neuroprotective mechanisms, influenced by both NGB expression levels and its intracellular distribution (Ascenzi et al., 2016). Furthermore, recent studies have identified NGB in astrocyte-derived exosomes (Venturini et al., 2019), suggesting a potential cytoprotective role beyond its intracellular effects. This supports the hypothesis that NGB may function as an intercellular resistance factor through non-cell-autonomous mechanisms. Previous research in our laboratory demonstrated the extracellular release of NGB in breast cancer models, showing that extracellular NGB could protect breast cancer cells from oxidative stress-induced apoptosis (Fiocchetti et al., 2020). However, the extracellular release of NGB and its potential neuroprotective effects on surrounding cells neuron like context remain unexplored. For this reason, the primary aim of this doctoral thesis is to investigate the extracellular release of NGB and assess its role in promoting neuronal cell resilience under stress and neurodegenerative conditions. The specific objectives of the thesis are as follows: (i) To characterize the extracellular release of NGB and identify potential regulatory mechanisms involved. (ii) To evaluate the functional significance of extracellular NGB in enhancing cellular resilience under stress conditions associated with neuropathologies in cells with neuronal characteristics and neurodegenerative phenotype. (iii) To investigate the potential influence of extracellular NGB on stress response pathways. REFERENCE: • Ascenzi P, di Masi A, Leboffe L, Fiocchetti M, Nuzzo MT, Brunori M, Marino M. Neuroglobin: From structure to function in health and disease. Mol Aspects Med. 2016 Dec;52:1-48. doi: 10.1016/j.mam.2016.10.004. Epub 2016 Nov 4. PMID: 27825818. • Burmester T, Weich B, Reinhardt S, Hankeln T. A vertebrate globin expressed in the brain. Nature. 2000 Sep 28;407(6803):520-3. doi: 10.1038/35035093. PMID: 11029004. • Fiocchetti M, Cracco P, Montalesi E, Solar Fernandez V, Stuart JA, Marino M. Neuroglobin and mitochondria: The impact on neurodegenerative diseases. Arch Biochem Biophys. 2021 Apr 15;701:108823. doi: 10.1016/j.abb.2021.108823. Epub 2021 Mar 3. PMID: 33675812. • Fiocchetti M, Solar Fernandez V, Segatto M, Leone S, Cercola P, Massari A, Cavaliere F, Marino M. Extracellular Neuroglobin as a Stress-Induced Factor Activating Pre-Adaptation Mechanisms against Oxidative Stress and Chemotherapy-Induced Cell Death in Breast Cancer. Cancers (Basel). 2020 Aug 29;12(9):2451. doi: 10.3390/cancers12092451. PMID: 32872414; PMCID: PMC7564643. • Kole AJ, Annis RP, Deshmukh M. Mature neurons: equipped for survival. Cell Death Dis. 2013 Jun 27;4(6):e689. doi: 10.1038/cddis.2013.220. PMID: 23807218; PMCID: PMC3702294. • Marmolejo-Martínez-Artesero S, Casas C, Romeo-Guitart D. Endogenous Mechanisms of Neuroprotection: To Boost or Not to Boost. Cells. 2021 Feb 10;10(2):370. doi: 10.3390/cells10020370. PMID: 33578870; PMCID: PMC7916582. • Venturini A, Passalacqua M, Pelassa S, Pastorino F, Tedesco M, Cortese K, Gagliani MC, Leo G, Maura G, Guidolin D, Agnati LF, Marcoli M, Cervetto C. Exosomes From Astrocyte Processes: Signaling to Neurons. Front Pharmacol. 2019 Dec 2;10:1452. doi: 10.3389/fphar.2019.01452. PMID: 31849688; PMCID: PMC6901013. • Wilson DM 3rd, Cookson MR, Van Den Bosch L, Zetterberg H, Holtzman DM, Dewachter I. Hallmarks of neurodegenerative diseases. Cell. 2023 Feb 16;186(4):693-714. doi: 10.1016/j.cell.2022.12.032. PMID: 36803602

Efficient hysteresis characterization and prediction in 3D-printed magnetic materials using deep learning

This research proposes a data processing pipeline employing Fourier analysis and deep neural networks to replicate the phenomenon of magnetic hysteresis, in particular frequency components derived from experimental data gathered using a newly developed 3D-printed material. The characterisation of hysteresis is essential for enhancing material performance and constructing precise models to anticipate material behaviour under diverse operating circumstances, especially in 3D-printed materials where properties can be meticulously regulated to ensure successful applications. The experimental signals were used for training and testing a neural network, exploiting Fourier coefficients to condense signals into the frequency components. This compression extracts fewer parameters and thus reduces and optimises the resources required by the neural network. It also improves the generalisation performance of the model, allowing it to make more accurate predictions on unseen data. This therefore optimises traditional modelling that requires a complete representation of hysteresis loops in the time domain, which must be addressed with the use of complex neural networks and large datasets. The experimental results show lower computational costs during the prediction process and a smaller memory footprint. Furthermore, the proposed model is easily adaptable for the loss estimation in different types of materials and input signals