Abstract

Abstract

This work examines how key modelling decisions in Deep Reinforcement Learning (DRL) shape the quality of learned scheduling policies. Although DRL has been increasingly applied to the Flexible Job-shop Scheduling Problem (FJSP), existing studies provide limited understanding of how distinct action and reward definitions respond to specific problem settings. We address this gap by integrating Constraint Programming (CP) into the design and training processes of a multi-agent DRL framework. Using CP-derived optimality bounds, we analyse the impact of job and machine heterogeneity in the suitability of distinct action and reward formulations, considering standard FJSP benchmarks. Building on these insights, we (i) design a set of reward functions that outperform existing ones, reducing the optimality gap by roughly 10% in high-heterogeneity scenarios, and (ii) introduce a hybrid DRL method (DQL-D) that incorporates expert demonstrations from CP solutions into a multi-agent Deep Q-Learning architecture. This hybrid strategy accelerates convergence and improves stability, yielding a 5% reduction in optimality gap relative to a standard DQL baseline. Overall, our findings demonstrate that optimality-guided DRL modelling enables lightweight, fast, and reliable scheduling agents, meeting the real-time and decentralized decision support demanded by modern manufacturing systems.

Abstract
This paper focuses on the comprehensive assessment of regional performance in attaining the 2030 Strategic Framework for Education and Training (ET2030) established by the European Union. To this end, we propose a composite indicator framework based on robust Benefit-of-the-doubt models empirically validated through an extensive analysis of data spanning 32 countries and 101 NUTS-I level regions for 2019. We integrate contextual variables into a robust conditional model to ensure an equitable evaluation among regions grappling with distinct circumstances. Specifically, the unemployment rate and the percentage of the population holding national citizenship are considered. Moreover, the research identifies best practices from high-performing regions that can serve as benchmarks for underperforming areas. Analyzing regional-level data is crucial for understanding disparities between European regions and within countries.

Abstract
Our team previously introduced an innovative concept for an "invisible" Electrocardiography (ECG) system, incorporating electrodes and sensors into a toilet seat design to enable signal acquisition from the thighs. Building upon that work, we now present a novel dataset featuring real-world, single-lead ECG signals captured at the thighs, offering a valuable resource for advancing research on thigh-based ECG for cardiovascular disease assessment. To our knowledge, this is the first dataset of its kind. The tOLIet dataset comprises 149 ECG recordings collected from 86 individuals (50 females, 36 males) with an average age of 31.73 ± 13.11 years, a mean weight of 66.89 ± 10.70 kg, and an average height of 166.82 ± 6.07 cm. Participants were recruited through direct contact with the Principal Investigator at Centro Hospitalar Universitario de Lisboa Central (CHULC) and via clinical consultations conducted at the same institution. Each recording includes four differential signals acquired from electrode pairs embedded in the toilet seat, with reference signals obtained from a standard 12-lead hospital ECG system.

Abstract
This paper presents a novel approach to the computational representation of cellular lines using transformer-based embeddings. By leveraging state-of-the-art natural language processing techniques, we generate context-aware embeddings from biomedical literature from the PubMed database, offering a more nuanced and biologically relevant representation of cellular lines compared to traditional methods like TF-IDF and SVDD. We applied these embeddings to cluster cellular lines, using the elbow method to identify a set of distinct clusters that reflect biologically meaningful relationships. To evaluate the quality of these clusters, we employed the Topic Coherence metric, achieving a coherence score of 0.395, indicative of moderate consistency across clusters. The results demonstrate the potential of transformer-based models to improve drug discovery by identifying shared characteristics between cellular lines, enabling more accurate drug response predictions and advancing personalized medicine. This method offers an interesting improvement in the precision of cellular line modeling, paving the way for more efficient drug repositioning and targeted therapies in cancer research.

Abstract
The integration of renewable energy and dynamic pricing in European electricity systems requires innovative approaches to manage residential flexibility. This paper presents an elasticity-aware clustering and optimisation framework to jointly model behavioural and asset-based flexibility, addressing gaps in current demand-side management strategies. The methodology comprises three tools: (i) causal inference to estimate consumers' price elasticities, isolating the impact of price variations on consumption; (ii) unsupervised clustering using both elasticity estimates and electric water heater (EWH) load profiles, capturing heterogeneity in flexibility; and (iii) cluster-level optimization for EWH scheduling, minimizing energy usage while preserving user comfort. Simulation results demonstrate measurable reductions in electricity usage across clusters (around 75%). The framework supports aggregators and retailers in designing differentiated market strategies, enhancing coordination in future flexibility markets. By bridging behavioural and operational flexibility, this approach offers relevant insights for residential demand-side management.