Abstract
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government's and its citizens' relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp-mi, smc-chi(2) and mc-chi(2) conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.

Abstract
A semi-distributed optical fiber bending extensometer system based on OTDR is proposed, consisting of N-loops designed to enable different maximum extension measurements and sensitivities. This system offers a low-cost solution for monitoring landslides and similar civil structures. Tests conducted at 1625 nm demonstrate that different series of sensors can be independently measured with elongation errors typically within +/- 0.25 cm across a range from 0 to 9 cm.

Abstract
The mobility sector is expected to significantly impact the power system by deploying battery electric vehicles (BEV) and fuel cell vehicles (FCEV). This work improves CEVESA, a market model for the long-term planning and operation of the Iberian Electricity Market, by modelling FCEV as an alternative to BEV and internal combustion vehicles (ICEV), and its impact on the H-2 demand and storage. The mobility and H-2 economy models interact with the power system through the electricity needs and price. CEVESA is then applied to estimate potential expansion paths of ICEV, BEV and FCEV mobility alternatives considering the total system costs and the EU decarbonization strategy. The findings suggest that if FCEVs technology matures, it could rival BEVs, offering greater system flexibility via electrolyzers and extended driving ranges for users.

Abstract

Abstract
A flexible wearable sensor utilizing a balloon-shaped interferometer structure, created from a bent standard single-mode fiber and a 3D-printed piece, was introduced and shown for respiratory monitoring. The interferometer is a compact, cost-effective, and easily fabricated sensor. The fiber's curvature causes interference between the core and cladding modes, which in turn results in the sensor operation. In the balloon-shaped curving section, light traversing the core partially escapes and interacts with the cladding. The preliminary results demonstrate an average displacement of 9.3 nm and the capability to evaluate breathing rate.

Abstract
This paper presents a Local Energy Market (LEM) model based on Walrasian Auctions for near real-time energy trading among peers in an Energy Community. The market operates with minimal information exchange, where peers only indicate trade decisions and quantities. The auctioneer updates prices iteratively to balance supply and demand. Two core algorithms support the LEM: (1) the Auctioneer Price Decision Algorithm, which adjusts prices based on past imbalances, and (2) a real-time bidding optimization algorithm, which optimizes peers' energy dispatch and local energy trading decisions based on expected demand, generation, storage, and opportunity costs of external trading. This work details the design and implementation of the bidding optimization algorithm and evaluates its performance through simulations. The results compare the LEM to a centralized pool-based market and individual optimizations, assessing its efficiency and imbalance control. The findings support the development of innovative and decentralized energy markets and smart grid applications.