Parthenope University of Naples
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A neural network-particle swarm solver for sustainable portfolio optimization problems
No full textIn this paper, the main goal is to tackle a sustainable portfolio optimization problem in which we aim to minimize a tail-dependence risk measure. More specifically, the considered risk measure is represented by the Delta Conditional Value-at-Risk, a tail-dependence measure meant to quantify the potential losses of a portfolio due to the riskiness associated with an individual asset or a group of assets. In addition, in the portfolio construction, we take into account some real-world trading constraints. On the one hand, we impose stock market restrictions through buy-in thresholds and budget constraints. Moreover, a minimum level of guaranteed expected return is required. On the other hand, a turnover threshold restricts the total amount of trades allowed in the rebalancing phases. Finally, in order to meet the growing appetite for sustainable investment, we introduce a green threshold into the portfolio's design. To deal with these asset allocation models, in this paper we develop an improved hybrid Particle Swarm Optimizer (PSO) that is dynamically adjusted by a neural network architecture embedded with a suitable constraint-handling procedure. The neural network paradigm is fundamental for enhancing the basic PSO's performance and improving the quality of estimating the Delta Conditional Value-at-Risk. Finally, we conduct empirical tests on two different American datasets to illustrate the effectiveness of the proposed strategies and evaluate the performance of our investments as the sustainable preferences vary
JÓVENES Y ECONOMÍA SUMERGIDA E IRREGULAR EN ITALIA: CRITICIDAD Y NUEVAS PERSPECTIVAS EN EL PLAN NACIONAL DE LUCHA CONTRA EL TRABAJO NO DECLARADO
No full textRiflessioni sul rapporto tra intelligenza artificiale e giustizia predittiva
No full textIl contributo esamina il rapporto tra l’intelligenza artificiale e la giustizia
predittiva in una prospettiva che prova a chiarire come le possibili applicazioni della prima nel
processo civile potrebbero essere di varia natura, col limite invalicabile dell’impiego per
generare una decisione sostitutiva di quella resa dal giudice
An exploratory study on the acceptance of new sustainable medical devices for biomedical and industrial applications
No full text
Polyphenylene oxide based lossy mode resonance fiber sensor for the detection of volatile organic and inorganic compounds
No full textThis study presents the fabrication and characterization of a fiber optic gas sensor based on Lossy Mode Resonances (LMR). For the first time to our knowledge, a nanosized coating of polyphenylene oxide (PPO) is deposited on a cladding removed multimode silica fiber, serving both as the LMR supporting coating and sensitive overlay. The device exhibits a notable sensitivity of 2500 nm/RIU when immersed in glycerol-water based solutions. For gas detection, the PPO-based LMR device is exposed to varying concentrations of different volatile organic and inorganic compounds, including two alcohols (methanol and ethanol) and ammonia. The sensor demonstrates similar responses to methanol and ethanol gases with a sensitivity of about 0.56 nm/ppm and sensitivity of 0.25 nm/ppm to ammonia in the concentration range of 2.5–37.5 ppm, achieving limits of detection of a few ppm. To comprehensively evaluate the sensor performance, the investigation is also focused on the repeatability, reversibility, response times, as well as cross sensitivity to temperature and relative humidity
EU Rules on Transparency and Liability for Online Platforms Challenges and Perspectives
No full textThis volume represents the final scholarly output of the research project entitled
Towards Stricter Rules on Transparency and Liability for Online Platforms
in the European Digital Single Market, which was selected for funding by the
Italian Ministry of University and Research (Ministero dell’Università e della Ricerca)
under the PRIN 2022 research programme, within the framework of the
National Recovery and Resilience Plan (NRRP) – Next Generation EU, and was
formally launched in September 2023. The project has been coordinated by a team
of EU law academics: Giuseppe Morgese (Principal Investigator, University of
Bari Aldo Moro), Ilaria Ottaviano (Associate Investigator, “G. d’Annunzio” University
of Chieti-Pescara), Sara Pugliese (Associate Investigator, “Parthenope”
University of Naples), and Nicola Ruccia (Associate Investigator, University of
Sannio).Particular attention has been devoted to the current Digital Services
Act (DSA) and Digital Markets Act (DMA), which together constitute the
cornerstone of the EU’s renewed approach to platform governance. These instruments
introduce differentiated obligations calibrated to the systemic relevance and
functional roles of online platforms, with the explicit aim of addressing risks associated
with illegal content, market power and asymmetric dependencies.
Alongside the DSA and the DMA, the project has examined the growing relevance
of data as an object of regulation. The increasing interdependence between
platform activity, data accumulation, and market power has made data governance
a crucial component of EU’s transparency and liability regimes. Accordingly, such
issues have been analysed as integral elements of the broader Digital Single Market
framework. In parallel, the project has addressed selected aspects of artificial intelligence,
insofar as algorithmic decision-making systems deployed by online platforms
raise specific concerns with regard to opacity, accountability, and the attribution
of responsibility
Dai Dark Patterns agli Hyper-Engaging Dark Patterns: per un’applicazione moderna del divieto ex art. 25 DSA
No full textL’art. 25 del Digital Services Act (DSA) vieta l’utilizzo e la diffusione sulle piattaforme online dei cc.dd. dark patterns, ossia di quegli strumenti informatici progettati per influenzare il comportamento degli utenti durante la loro esperienza nel web. L’interpretazione prevalente in dottrina riguardo tale divieto – come confermato anche dalla prassi della Commissione UE – appare, tuttavia, irragionevolmente restrittiva, limitandone la vigenza esclusivamente alla categoria dei dark patterns di natura grafica. Invero, siffatta operazione ermeneutica porta con sé il rischio di creare un vuoto di tutela in relazione alle nuove generazioni di dark patterns e, più nello specifico, rispetto agli Hyper-Engaging Dark Patterns (HEDP), progettati non solo per massimizzare l’interazione con gli utenti ma, soprattutto, per spingere questi ultimi a compiere azioni da loro non intenzionalmente volute (come, ad esempio, effettuare acquisti non programmati). Alla luce di tale stato dell’arte, il presente lavoro propone un superamento dell’odierno approccio maggioritario il quale, tra le altre cose, crea una forte differenziazione di tutele tra utenti delle Very Large Online Platforms (VLOPs) e utenti delle non-VLOPs
