Parthenope University of Naples
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Optimizing storage capacity in 100 % renewable electricity supply: A GIS-based approach for Italy
No full textCatalysts of Circular Change: Role Played by Institutions and Lobbyists in Promoting Sustainable Development
No full textWhen a Lump Is Not a Cyst: A Case of Superficial Venous Aneurysm of the Hand Diagnosed with High-Resolution Ultrasound
No full textTECNICHE DI TUTELA DELLA RISOLUZIONE DEL RAPPORTO DI LAVORO PER LA “CURA DEI CARI”: AMBITO APPLICATIVO, EFFICACIA E “TRAPPOLE” NORMATIVE
No full textCost-effective approaches for microplastic pellets characterization using a machine learning tool
No full textMicroplastics, including pellets, are a persistent pollutant on beaches that pose relevant ecological and environmental challenges. Their widespread presence in marine and coastal environments endangers ecosystems, threatens marine life, and risks entering the food chain. Effective microplastic management requires reliable methods for their identification and classification, yet the high cost of required equipment hinders large-scale implementation. Artificial intelligence offers a promising solution for polymer analysis. While machine learning techniques have demonstrated potential in automating microplastic classification, existing approaches often rely on complex models requiring numerous input variables, limiting their practical application. This paper introduces a simplified methodology for pellet polymer classification using a Random Forest model requiring a limited set of variables for training. The approach reduces model complexity while maintaining high classification performance, emphasizing simplicity, speed and efficiency. The method was tested on different pellet samples collected from the coasts of Spain, Portugal and Vulcano Island (Italy). The results highlight the robustness of the proposed model and its suitability to be applied in diverse environmental contexts. By balancing accuracy with computational efficiency, the proposed approach represents a practical tool for pellet classification. This streamlined methodology can offer a significant step forward in microplastic management and pollution mitigation, contributing to the development of cost-effective, scalable solutions for addressing the environmental impacts of microplastics
First Experiences on Exploiting Physics-Informed Neural Networks for Approximating Solutions of a Biological Model
No full textRecent advances in artificial intelligence have changed the ability to study and model complex biological phenomena. Physics-Informed Neural Networks (PINNs) represent a novel approach that link deep learning techniques with fundamental physical principles in solving partial differential equations. This work proposes an implementation of PINNs for modeling tumor-induced angiogenesis through a system of coupled reaction-diffusion equations that track the interplay between different biological agents. We introduce a computational framework that combines neural network architectures with physics-based constraints, using an optimized loss function incorporating both empirical data and theoretical principles via strategic collocation points. Experimental results validate the reliability of our approach in predicting the intricate spatial and temporal patterns of blood vessel formation, showing the potential of PINNs as a robust computational tool for simulating complex biological processes
Quantum-Enhanced Water Quality Monitoring: Exploiting Sat-2 Data With Quanvolution
No full textingleserCoastal water quality monitoring is crucial for environmental sustainability and public health. This work introduces a very cutting-edge methodology, using ΦSat-2 multispectral data and quanvolutional neural networks (QNNs) to explore quantum-enhanced machine learning (ML) for water contaminant assessment. By integrating quantum preprocessing into a classical regression model, it is possible to achieve a significant reduction in model parameters while maintaining high predictive accuracy. In addition, this work introduces an innovative dataset that integrates simulated ΦSat-2 spectral data with Copernicus Marine Service biogeochemical products, ensuring a strong alignment between satellite observations and reference turbidity measurements. Our results show that quantum models use up to 98% fewer parameters than their classical counterparts, while achieving a 6.9% improvement in the Pearson correlation coefficient between the ΦSat-2 preprocessed bands and the ground-truth turbidity values, compared with the case without quantum preprocessing. In addition, the root mean square error (RMSE) improves by 7.3% over the classical baseline. These findings highlight the potential of quantum-assisted remote sensing (RS) to enable more efficient and scalable analysis of large-scale water contaminant data, paving the way for advanced big data approaches in water quality monitoring
Exploring the determinants of corporate social performance: does firm size matter?
No full textPurpose – This paper aims to investigate the relationship between relevant company determinants and corporate social performance as measured by the social pillar of the environmental, social and governance (ESG) score, exploring whether firm size matters.
Design/methodology/approach – The authors use the system generalized method of moment estimator for dynamic panel data to analyze an unbalanced panel of firms listed in the STOXX Europe 600 index from 2015 to 2021.
Findings – The results indicate that several board characteristics (size, independence, percentage of nonexecutive members, gender diversity and the presence of a corporate social responsibility sustainability committee) and fewer ESG controversies are associated with higher corporate social performance. However, the results show no relationship between corporate financial performance and the social pillar. Furthermore, the authors demonstrate that large companies and those external to the financial industry show higher social performance.
Practical implications – The findings provide important implications for several stakeholders, including regulators and policymakers. Increasing attention should be directed toward specific firm determinants to enhance corporate social performance.
Originality/value – The authors advance understanding of the existing literature by examining how corporate social performance is influenced by its main corporate determinants