Abstract
Around 80% of websites change significantly or disappear altogether after the first year, resulting in the loss of invaluable information. In this volatile scenario, preserving online content is increasingly essential. This is especially critical for local news outlets, which produce a wealth of information within the unique context of their communities but often lack sufficient archiving resources. In this paper, we take a significant step forward by leveraging the information preserved by the Portuguese Web Archive, Arquivo.pt, to recreate the website of a local news outlet. This online demo grants users direct access to previously lost news articles, images, and front covers, thus contributing to preserving local digital heritage. An IR system was also implemented to ensure easy access, along with a recommendation system based on BERT embeddings to suggest related news articles and enhance user engagement. As a final contribution, we also provide a Python package, enabling others to replicate the process of collecting, processing, retrieving, and recreating websites for local news outlets in Portugal.

Abstract
The electrification of transportation, driven by the widespread adoption of electric vehicles and increased integration of renewable energy, is critical to decarbonizing mobility and society. Demand response strategies, such as dynamic pricing, enable indirect control of charging processes, but their success relies on accurately estimating consumer responses to tariff changes. Observational data can provide insights into consumer behavior, but the presence of confounding variables motivates the use of causal inference techniques for a reliable elasticity estimation. This study proposes a data-driven framework for optimizing day-ahead charging tariffs, leveraging causal discovery and inference algorithms validated on a synthetically generated dataset. A sensitivity analysis explores the impact of data availability on elasticity estimation and the performance of the resulting demand response strategy. The findings highlight the potential of causal machine learning to characterize consumers and, ultimately, the need for regular characterization to improve the efficiency of demand-side management.

Abstract

This paper presents an integrated analytical approach to assess the reliability of power electronic converters in Permanent Magnet Synchronous Generator (PMSG)-based wind farms under variable wind conditions. The study focuses on analyzing the impact of wake effect turbulences and thermal management on power converter reliability, driven by the thermal stress induced by fluctuating wind speeds on power converters. Through extensive simulations using FLORIS and MATLAB, the thermal behavior of converters in wind farms affected by wake interactions was examined to identify potential reliability issues. The methodology involved modeling an 80-turbine wind farm in FLORIS to simulate wake effects, processing high-resolution wind speed data in MATLAB to refine wind speed profiles, and using Simulink to simulate the thermal profiles of power electronics. The results of FLORIS simulations highlighted the variations in turbulence intensity (TI) and power output, while the MATLAB and Simulink models quantified critical thermal stresses in power converters, correlating the locations of the turbine rows with temperature fluctuations and potential failures. Machine learning models, including Gradient Boosting and Random Forest Regressor, were utilized to refine and predict the multi-objective reliability function. The findings underscore the importance of understanding and managing thermal dynamics to improve the reliability and operational resilience of the power converter, supporting sustainable wind farm operations in dynamically changing wind conditions.

Abstract
The definition of rigorous and well-structured annotation schemes is a key element in the advancement of Natural Language Processing (NLP). This paper aims to compare the performance of a general-purpose annotation scheme - Text2Story, based on the ISO 24617-1 standard-with that of a domain-specific scheme - i2b2 - in the context of clinical narrative annotation; and to assess the feasibility of harmonizing ISO 24617-1, originally designed for general-domain applications, with a specialized extension tailored to the medical domain. Based on the results of this comparative analysis, we present Med2Story, a medical-specific extension of ISO 24617-1 developed to address the particularities of clinical text annotation.

Abstract
Discourse markers (DMs) are linguistic expressions that convey different semantic and pragmatic values, managing and organizing the structure of spoken and written discourses. They can be either single-word or multiword expressions (MWE), made up of conjunctions, adverbs, and prepositional phrases. Although DMs are the focus of many studies, some questions regarding the interoperability of taxonomies and automatic identification and classification require further research. We aim to tackle these issues by offering a critical analysis and discussing the constitution of a multilingual corpus in 10 languages, i.e., English, Lithuanian, Bulgarian, German, Macedonian, Romanian, Hebrew, Polish, European Portuguese, and Italian. The novel two-level annotation approach is based on (i) signaling the existence or non-existence of DMs in a given text, and (ii) applying the ISO-24617 standard to annotate the DMs' discourse relation and communicative function in the corpora. Additionally, we introduce prediction models for detecting the presence of DMs within a text. Marcatorii discursivi (DM-uri) sunt expresii lingvistice care transmit diverse valori semantice si pragmatice, av & acirc;nd rolul de a gestiona si organiza structura discursurilor vorbite si scrise. Acestia pot fi fie expresii formate dintr-un singur cuv & acirc;nt, fie locutiuni, expresii formate din mai multe cuvinte (MWE), alc & abreve;tuite din conjunctii, adverbe si grupuri prepozitionale. Desi marcatorii discursivi reprezint & abreve; obiectul multor studii, unele & icirc;ntreb & abreve;ri legate de interoperabilitatea taxonomiilor si de identificarea si clasificarea automat & abreve; a acestora necesit & abreve; cercet & abreve;ri suplimentare. Ne propunem s & abreve; abord & abreve;m aceste aspecte printr-o analiz & abreve; critic & abreve; si prin discutarea constituirii unui corpus multilingv & icirc;n 10 limbi, si anume: englez & abreve;, lituanian & abreve;, bulgar & abreve;, german & abreve;, macedonean & abreve;, rom & acirc;n & abreve;, ebraic & abreve;, polonez & abreve;, portughez & abreve; european & abreve; si italian & abreve;. Noua abordare de adnotare pe dou & abreve; niveluri se bazeaz & abreve; pe (i) semnalarea existentei sau inexistentei marcatorilor discursivi & icirc;ntr-un text dat si (ii) aplicarea standardului ISO-24617 pentru a adnota relatia discursiv & abreve; si functia comunicativ & abreve; a marcatorilor & icirc;n corpusuri. & Icirc;n plus, & icirc;n acest articol, introducem modele de predictie pentru detectarea prezentei marcatorilor discursivi & icirc;ntr-un text.

Abstract
The aging of the Portuguese population is a multifaceted challenge that requires a coordinated and comprehensive response from society. In this context, social service institutions play a fundamental role in providing aid and support to the elderly, ensuring that they can enjoy a dignified and fulfilling life even in the face of the challenges of aging. This research proposes a Balanced Multiple Traveling Salesman Problem based on the Ant Colony Optimization algorithm (ACO-BmTSP) to solve a distribution of meals problem in the municipality of Mogadouro, Portugal. The Multiple Traveling Salesman Problem (mTSP) is an NP-complete problem where m salesmen perform a shortest tour between different cities, visiting each only once. The primary purpose is to minimize the sum of all distance traveled by all salesmen keeping the tours balanced. This paper shows the results of computing obtained for three, four, and five agents with this new approach and their comparison with other approaches like the standard Particle Swarm Optimization and Ant Colony Optimization algorithms. As can be seen, the ACO-BmTSP, in addition to obtaining much more equitable paths, also achieves better results in lower total costs. In conclusion, some benchmark problems were used to evaluate the efficiency of ACO-BmTSP, and the results clearly indicate that this algorithm represents a strong alternative to be considered when the problem size involves fewer than one hundred locations.