Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addrebes these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
The Software-Defined Storage (SDS) paradigm has emerged as a way to ease the orchestration and management complexities of storage systems. This work aims to mitigate the storage performance issues that large-scale HPC infrastructures are currently facing by developing a scalable and dependable control plane that can be integrated into an SDS design to take full advantage of the tools this paradigm offers. The proposed solution will enable system administrators to define storage policies (e.g., I/O prioritization, rate limiting) and, based on them, the control plane will orchestrate the storage system to provide better QoS for data-centric applications.

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Programs with a continuous state space or that interact with physical processes often require notions of equivalence going beyond the standard binary setting in which equivalence either holds or does not hold. In this paper we explore the idea of equivalence taking values in a quantale V, which covers the cases of (in)equations and (ultra)metric equations among others.Our main result is the introduction of a V-equational deductive system for linear lambda-calculus together with a proof that it is sound and complete. In fact we go further than this, by showing that linear lambda-theories based on this V-equational system form a category equivalent to a category of autonomous categories enriched over 'generalised metric spaces'. If we instantiate this result to inequations, we get an equivalence with autonomous categories enriched over partial orders. In the case of (ultra)metric equations, we get an equivalence with autonomous categories enriched over (ultra)metric spaces. Additionally, we show that this syntax-semantics correspondence extends to the affine setting.We use our results to develop examples of inequational and metric equational systems for higher-order programming in the setting of real-time, probabilistic, and quantum computing.