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{Deep Learning Models Resizing for High Energy Physics experiments}

Renewable hydrogen production through electrolysis: An analysis of the cost gap for its economic competitiveness in Italy

With global policies targeting greenhouse gas emission reduction, renewable hydrogen produced by water electrolysis powered with renewable electricity appears a promising alternative to fossil fuels. However, still high capital costs, reliance on intermittent renewable electricity sources, and high electricity cost hinder the economic viability of electrolyzers. This work develops a comprehensive analysis for assessing the need of an incentive to make renewable hydrogen economically viable, i...

Emerging security and legal challenges within renewable energy communities: key prevention and defence strategies

In the context of decarbonizing the energy system, the European Union has promoted a package of eight different directives known as the Clean Energy Package. Among them, Directive 2018/2001/EU, also known as RED II, has proposed a new legal framework for the development of renewable energy sources (RES) and, more importantly, for increasing citizen participation in the energy transition through two new tools: collective self-consumption systems (CSC) and renewable energy communities (REC). Th...

Edge Computing for Smart-City Human Habitat: A Pandemic-Resilient, AI-Powered Framework

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in hea...

IFH: a diffusion framework for flexible design of graph generative models

Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. Ideally, between these two extreme models lies a continuous range of models that adopt different levels of sequentiality. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree. IFH is based upon the theor...

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{Deep Learning Models Resizing for High Energy Physics experiments}

Renewable hydrogen production through electrolysis: An analysis of the cost gap for its economic competitiveness in Italy

Emerging security and legal challenges within renewable energy communities: key prevention and defence strategies

Edge Computing for Smart-City Human Habitat: A Pandemic-Resilient, AI-Powered Framework

IFH: a diffusion framework for flexible design of graph generative models

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