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Publicações

2025

Blockchain Hybrid-model Scheme for Scalable Cross-domain Authorisation

Autores
Mukhandi, M; Granjal, J; Vilela, JP;

Publicação
Blockchain: Research and Applications

Abstract

2025

Persuasive Smart Bin Technology for Sustainable Behavior: A Case Study of Recycling

Autores
da Silva, EM; Schneider, D; Miceli, C; Correia, A;

Publicação
2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Abstract

2025

Integrating Cross-Sector Flexible Assets in Flexibility Bidding Curves for Energy Communities

Autores
Rodrigues, L; Mello, J; Silva, R; Faria, S; Cruz, F; Paulos, J; Soares, T; Villar, J;

Publicação
2025 21ST INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered.

2025

QoS-Aware Multimodal Underwater Wireless Networks

Autores
Cunha, FS; Loureiro, JP; Teixeira, FB; Campos, R;

Publicação
OCEANS 2025 BREST

Abstract
The growing demands of the Blue Economy are increasingly supported by sensing platforms, including as Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). Multimodal Underwater Wireless Networks (MUWNs), which may combine acoustic, radio-frequency, and optical wireless technologies, enhance underwater data transmission capabilities. Although Delay-Tolerant Networks (DTNs) address connectivity intermittency in such environments, not all data streams are delay-tolerant, and transmitting high-bandwidth DTN traffic over narrowband links can lead to significant inefficiencies. This paper presents QoS-MUWCom, a Quality of Service (QoS)-aware communication solution designed to manage both real-time and delay-tolerant traffic across dynamically selected multimodal interfaces. Experimental evaluations conducted in a freshwater tank demonstrate that QoS-MUWCom achieves near-zero packet loss for low-demand traffic even under link saturation, improves throughput for prioritized flows up to three times in mobility scenarios, and adapts to link availability and node mobility. The results confirm that QoS-MUWCom outperforms conventional multimodal strategies, contributing to more robust, resilient and efficient underwater communications.

2025

Reinforcement learning for hexapod robot trajectory control: a study of Q-learning and SARSA algorithms

Autores
Benyoucef, A; Zennir, Y; Belatreche, A; Silva, MF; Benghanem, M;

Publicação
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS

Abstract
Hexapod robots, with their six-legged design, excel in stability and adaptability on challenging terrain but pose significant control challenges due to their high degrees of freedom. While reinforcement learning (RL) has been explored for robot navigation, few studies have systematically compared on-policy and off-policy methods for multi-legged locomotion. This work presents a comparative study of SARSA and Q-Learning for trajectory control of a simulated hexapod robot, focusing on the influence of learning rate (alpha), discount factor (gamma), and eligibility trace (lambda). The evaluation spans eight initial poses, with performance measured through lateral deviation (Ey), orientation error (E theta), and iteration count. Results show that Q-Learning generally achieves faster convergence and greater stability, particularly with higher gamma and lambda values, while SARSA can achieve competitive accuracy with careful parameter tuning. The findings demonstrate that eligibility traces substantially improve learning precision and provide practical guidelines for robust RL-based control in multi-legged robotic systems.

2025

Extending the Quantitative Pattern-Matching Paradigm

Autores
Alves, S; Kesner, D; Ramos, M;

Publicação
PROGRAMMING LANGUAGES AND SYSTEMS, APLAS 2024

Abstract
We show how (well-established) type systems based on non-idempotent intersection types can be extended to characterize termination properties of functional programming languages with pattern matching features. To model such programming languages, we use a (weak and closed) lambda-calculus integrating a pattern matching mechanism on algebraic data types (ADTs). Remarkably, we also show that this language not only encodes Plotkin's CBV and CBN lambda-calculus as well as other subsuming frameworks, such as the bang-calculus, but can also be used to interpret the semantics of effectful languages with exceptions. After a thorough study of the untyped language, we introduce a type system based on intersection types, and we show through purely logical methods that the set of terminating terms of the language corresponds exactly to that of well-typed terms. Moreover, by considering non-idempotent intersection types, this characterization turns out to be quantitative, i.e. the size of the type derivation of a term t gives an upper bound for the number of evaluation steps from t to its normal form.

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