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 present work proposes a two-stage optimization approach for flexibility services provided by battery energy storage systems (BESS) in distribution networks with photovoltaic (PV) generation and electric vehicles (EV). The considered flexibility services include reserve allocation and voltage regulation to support network operation. The first stage optimizes the day-ahead (DA) scheduling of distributed BESS to minimize overall costs, including energy, BESS usage, and reserve, while accounting for stochastic variations in load, PV generation, and EV penetration. The second stage simulates the real-time (RT) operation of the electrical distribution network, evaluating system behavior under different scenarios based on DA decisions. A coordinated control strategy is applied, integrating DA scheduling with network voltage levels. Deviations between BESS outputs in DA and RT stages are fed back into a new DA run to adjust outputs and reduce costs. Results on a medium-voltage distribution system with 157 nodes (based on a reduced version of the EPRI CKT5 feeder) demonstrate that the proposed scenario-based model provides feasible solutions under uncertainty, with BESS playing a key role while strictly adhering to planned operational modes from DA to RT, as typically enforced in energy market participation.

Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation (DT), the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realizing the benefits of Digital Transformation (DT). This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises (SMEs) through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises (SMEs) in the Digital Transformation (DT) process.

Abstract
Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium-term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail. We propose a novel deep learning framework that integrates three-dimensional convolutional neural networks, bidirectional long short-term memory, and multihead attention to capture complex spatiotemporal patterns in asset return dynamics. Using daily data on 14 exchange-traded funds from 2017 to 2023, we demonstrate that our model improves out-of-sample covariance forecasts by reducing Euclidean and Frobenius distance metrics by up to 20% compared with classical benchmarks such as shrinkage estimators and GARCH-type models. These gains persist across distinct market regimes, including bull and bear periods, and remain robust across various forecast horizons and under both raw and excess return specifications. Portfolio simulations based on global minimum variance strategies reveal that the proposed model consistently delivers lower volatility and moderate turnover, even under no-short-selling constraints. This balance between risk reduction and trading efficiency underscores the economic relevance of the forecasts, particularly for institutional investors managing portfolios at medium-term horizons.