Archivio Istituzionale della Ricerca- Università del Salento
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Predicting ESG Controversies in Banks Using Machine Learning Techniques
Get PDFMistreating environmental, social, and governance (ESG) concerns has serious drawbacks in organizations of any type, and even more in banks. Deeply revolutionized in its taxonomy of risks, banking sector is herein evaluated in its integration of ESG parameters that, when lacking, leads to ESG-related controversies (ESGC). Thereby, this research approaches the almost uncharted territory of ESGC in banks, by means of machine learning. Aiming at selecting the set of features that are relevant in ESGC prediction, techniques belonging to feature selection are used over a real panel dataset of 140 banks evaluated for a wide set of features over 2011–2020 time-span. We find the power that governance-employees dynamics detains in making out-of-sample predictions and forecasting of ESGC banks' risk. Finally, we provide implications for researchers, practitioners and regulators, further confirming the need for the rapid inroads that machine learning tools are actually making in the banking toolkit and in the regulatory technology
UDL e didattica universitaria: esiti preliminari di un’indagine sui bisogni e sulle percezioni di un gruppo di studenti dell’Università del Salento. Il progetto PRIN DANTE-U
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L’ultima stagione del diritto europeo dello sport nella giurisprudenza della Corte di giustizia: la faticosa emersione della specificità
No full textIl saggio ricostruisce la più recente stagione del diritto europeo dello sport nella giurisprudenza della Corte di Giustizia, evidenziando come il baricentro sia ormai spostato verso un'applicazione "piana" del diritto dell'Unione, con particolare riferimento ai principi sulla concorrenza e alla tutela delle libertà economiche fondamentali. Riprendendo le fila dell'evoluzione giurisprudenziale della Corte da Walrave a Meca-Medina, si passa poi alle decisioni più recenti (TopFit, European Superleague, ISU, Antwerp, Diarra, Seraing), da cui emerge una lettura minimalista dell'articolo 165 TFUE, accanto alla marginalizzazione della "specificità" dello sport, con conseguente (possibile) erosione dello spazio di autonomia delle organizzazioni sportive
ABMOVE! Didattica inclusiva per migliorare l’apprendimento e ridurre l’ansia in matematica: pause attive curriculum-based
No full textIl programma ABMOVE! nasce nell’ambito del Progetto PRIN PNRR 2022 “Inclusive didactic for enhancing math learning and reducing math anxiety: efficacy of active breaks in the classroom” finanziato dall’Unione europea - Next Generation EU, Missione 4 Componente 1, CUP F53D23010970001. Promosso da tre Università – Università del Salento, Università di Palermo e Università di Torino – il progetto mira a potenziare le Funzioni Esecutive e, di conseguenza,
a migliorare l’apprendimento della matematica nella scuola primaria, integrando pause attive curriculum based durante le lezioni in aula. Questo approccio si propone di rendere l’esperienza didattica più attiva, coinvolgente e inclusiva, riducendo l’ansia per la matematica e promuovendo al contempo il benessere psicofisico di tutta la classe, con un particolare focus sugli studenti e sulle studentesse iperattivi/e
Enhancing Academic Performance, Cognitive Functions, and MentalWell-Being Through Active Breaks: Evidence from a Pilot Study with University Student Sample
No full textAn implementation of neural simulation-based inference for parameter estimation in ATLAS
Get PDFNeural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses
Defect‐Mediated Scintillation in Fully Inorganic Perovskites via Water‐Induced 0D/3D Phase Modulation
Get PDFScintillation detectors are essential tools in high-energy physics, medical imaging, and security, due their efficiency in converting ionizing radiation into visible light. Lead-based inorganic perovskites, particularly 3D CsPbBr3, have emerged as promising next-generation scintillators due to their high photon attenuation and fast emission properties. In contrast, the 0D phase, Cs4PbBr6, exhibits unique emission characteristics and defect-mediated behavior, offering additional opportunities to tune scintillation performance in hybrid systems. However, the role of the 0D Cs4PbBr6 phase in scintillation has remained largely unexplored, and the mechanism of the emission is not well understood. Herein, a simple and reproducible synthesis of polycrystalline perovskite powders is developed with the specific scope of modulating the 3D/0D CsPbBr3/Cs4PbBr6 phases in the samples, aiming to clarify the role of the 0D phase in the emission properties of the materials. The method relies on a solvent-antisolvent approach, in which incremental water additions selectively promote the formation of the 3D phase over the 0D one. The scintillation properties of the resulting powders are evaluated, revealing an increased scintillation yield for low water volumes used in the synthesis and an ultrafast decay time under X-ray radiation. Cathodoluminescence and temperature-dependent radioluminescence highlight defect-driven scintillation mechanisms, providing insights for future material optimization
Hygro-Thermo-Electro-Mechanical Coupled Modeling of Laminated Curved Panels
No full textThe manuscript presents a generalized two-dimensional model for evaluating the stationary hygro-thermo-mechanical response of laminated shell structures made of heterogeneous piezoelectric composite materials with thermal and hygrometric properties. In particular, the static bending response of these structures is studied, along with their coupled hygro-thermo-electrical behavior. A generalized kinematic model is introduced, enabling the assessment of arbitrary temperature and mass concentration variations with respect to the unvaried configuration at the top and bottom surfaces. This is achieved through an Equivalent Layer-Wise description of the unknown field variables using higher order polynomials and zigzag functions. Furthermore, an elastic foundation is modelled according to the Winkler-Pasternak theory. The fundamental equations, derived from the total free energy of the system, are solved analytically using Navier’s method. Then, the Fourier-based generalized differential quadrature numerical method is adopted to efficiently recover the through-the-thickness distribution of secondary variables, in agreement with the hygro-thermal loading conditions. The formulation is applied in some examples of investigation where the response of panels with different curvatures and lamination schemes is evaluated under external hygro-thermal fluxes and prescribed values of temperature and moisture concentration. In addition, we investigate the effect of the hygro-thermal coupling due to Dufour and Soret effect. The present formulation is verified to be a valuable tool for assessing the mechanical response of laminated structures in a thermal and hygrometric environment with reduced computational effort
