The recent need of supporting the diffusion of electric mobility around the world to progressively substitute petrol transport means, leads to the development of new hardware and software technologies to make even more convenient the use of electric vehicles (EVs). High purchasing costs and long recharging times are two major factors slowing this transition. In addition, from the end users perspective, using EV in long distance journeys is still not convenient despite the increasing diffusion of fast charging infrastructures. In this context, to facilitate traveling with EVs in long distance trips, this paper proposes a trip planner prototype based on Deep Reinforcement Learning (DRL). The trip planner prototype has the goal to suggest to the drivers the best charge stops to be performed during the trip according to the user needs and preferences. Charging stops are optimized, using the available Charging Points (CPs) along the route from origin to destination, and are shown to the user on a map taking into account important information like the EV State of Charge (SoC), the cruise velocity, and the presence of point of interest (e.g. restaurant, hotel, shops, etc.) close around. The trip plan can be done according to three objectives: minimizing the travel time, minimizing the charging costs, optimizing travel time and cost. The proposed DRL approach is compared against Genetic Algorithm (GA), heuristic, and optimization approaches considering a real-world EV trip.

The rise of the Internet of Things has introduced new challenges related to data security and transparency, especially in industries like agri-food where traceability is critical. Traditional cloud-based solutions, while scalable, pose security and privacy risks. This paper proposes a decentralized architecture using Blockchain technology to address these challenges. We deploy IoT sensors connected to a Raspberry Pi for edge processing and utilize Hyperledger Fabric, a private Blockchain, to manage and store data securely. Two approaches are evaluated: computation of a Discomfort Index on the Raspberry Pi (edge processing) versus performing the same computation on-chain using smart contracts. Performance metrics, including latency, throughput, and error rate, are measured using Hyperledger Caliper. The results show that edge processing offers superior performance in terms of latency and throughput, while Blockchain-based computation ensures greater transparency and trust. This study highlights the potential of Blockchain as a viable alternative to centralized cloud systems in IoT environments and suggests future research in scalability, hybrid architectures, and energy efficiency.

This paper explores the integration of generative AI and large language models into the realm of software engineering education and training, with a specific focus on the transformation of traditional peer assessment methodologies. The motivation stems from the growing demand for innovative educational techniques that can effectively engage and empower learners in mastering Software Engineering principles. The proposed approach involves presenting students with modeling exercises solved by ChatGPT, prompting them to critically evaluate and provide constructive feedback on the generated solutions. By engaging students in a dialogue with the AI model, we aim to foster a dynamic learning environment where learners can articulate their considerations and insights, thereby enhancing their comprehension of software engineering principles, critical thinking and self evaluation skills. Preliminary results from pilot implementations indicate promising outcomes, suggesting that this approach not only enhances the quality of peer feedback but also contributes to a more interactive and engaging educational experience.

The development of Quantum Computing has brought great advantages in terms of computational power that can be seen as an opportunity or as a potential threat to currently implemented systems. The security of a platform can be easily broken if its founding algorithms are not quantum-safe. For this reason, it is crucial to understand how quantum computers work and how much time is needed to switch to quantum-safe platforms. The main contribution of this paper consists of a software architecture for modular Blockchains to let multiple encryption algorithms coexist in order to mine new quantum-safe blocks without discarding old, validated, quantum-broken ones. Old blocks will still be unsafe for post-quantum cryptography, but this is not a threat to the chain integrity.

Traceability is a useful tool for consumers to gather as much information as possible about a particular product.Businesses, on the other hand, see traceability as a strategic marketing tool because it allows them to ensure the quality of their goods to customers in a transparent manner.The ability to readily access all information about an agri-food product is critical to customer trust.Products' information can include where they were manufactured, where they came from, what steps they took to reach at the shelter, and so on.The Blockchain technology is an illustration of how all industries are shifting toward technology and communication.The aim of this paper is to present the Tracecoop project and give an overview of the architecture of the proposed system.The platform ensures trust and guarantees a sense of community both for the consumer and the producer.