Abstract
Efficient battery management in electric vehicles plays a key role in the transition to more sustainable and energy efficient mobility. This article presents a proposal for a modular framework to optimise charging and energy consumption based on solar radiation prediction. The solution integrates three main components: climate prediction models, battery behaviour simulation, and optimisation algorithms for decision making. This approach aims to dynamically adapt charging strategies to maximise vehicle autonomy and reduce energy waste. The modularity of the framework allows it to be applied to different vehicle types and operating contexts, ensuring flexibility and scalability. In addition, preliminary studies on solar radiation forecasting have already been carried out, providing a basis for future development of the system. The implementation of this approach represents an important step towards more efficient energy management in electric vehicles, contributing to the reduction of environmental impact and the promotion of sustainable electric mobility.

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Abstract
Log analysis is fundamental to modern software observability systems, playing a key role in improving system reliability. Recently, there has been a growing adoption of Large Language Models (LLMs) for log anomaly detection, due to their ability to learn complex patterns. In this work, we propose a model-agnostic framework that allows seamless plug-and-play integration of different LLMs, making it easy to experiment with and select the model that fits specific needs. These models are first fine-tuned on normal log data, learning their patterns. During inference, the model predicts the most probable next tokens based on the preceding context in each sequence. Anomaly detection is performed using Top-K predictions, where sequences are flagged as anomalous if the actual log entry does not appear among the K most probable next tokens, with K determined using the validation dataset. The proposed framework is evaluated on three widely-used benchmark datasets-HDFS, BGL, and Thunderbird-where it consistently achieves competitive results, outperforming state-of-the-art methods in multiple scenarios. These results highlight the effectiveness of LLM-based log analysis and the importance of flexibility when selecting models for specific operational contexts.

Abstract
Fact-checking remains a demanding and time-consuming task, still largely dependent on manual verification and unable to match the rapid spread of misinformation online. This is particularly important because debunking false information typically takes longer to reach consumers than the misinformation itself; accelerating corrections through automation can therefore help counter it more effectively. Although many organizations perform manual fact-checking, this approach is difficult to scale given the growing volume of digital content. These limitations have motivated interest in automating fact-checking, where identifying claims is a crucial first step. However, progress has been uneven across languages, with English dominating due to abundant annotated data. Portuguese, like other languages, still lacks accessible, licensed datasets, limiting research, Natural Language Processing (NLP) developments, and applications. In this paper, we introduce ClaimPT, a dataset of European Portuguese news articles annotated for factual claims, comprising 1,308 articles and 6,875 individual annotations. Unlike most existing resources based on social media or parliamentary transcripts, ClaimPT focuses on journalistic content, collected through a partnership with LUSA, the Portuguese News Agency. To ensure annotation quality, two trained annotators labeled each article, with a curator validating all annotations according to a newly proposed scheme. We also provide baseline models for claim detection, establishing initial benchmarks and enabling future NLP and Information Retrieval (IR) applications. By releasing ClaimPT, we aim to advance research on low-resource fact-checking and enhance understanding of misinformation in news media.

Abstract
The design and development of consumer products require an interdisciplinary approach, often constrained by time-consuming prototyping and manual decision-making processes. As product complexity increases and market demands evolve, the need for automation and intelligent collaboration becomes evident. This paper presents a case study on the design and virtual validation of a premium pen using a multi-agent system, leveraging the integration of large language models (LLMs) and software agents. This combination enables a rational representation of human expertise and interactions, streamlining the design process while enhancing adaptability. Using CrewAI, agents were configured with specialized tasks, collaborating to optimize design, select sustainable materials, and establish quality standards. The agents generated a markdown report and a 3D simulation using Blender and Python, ensuring efficient coordination for an ergonomic, sustainable, high-quality pen. By modeling the rational behavior of human experts, the system demonstrated how LLMs and multi-agent coordination can reduce decision overhead and improve collaboration. The results show that multi-agent systems streamline product development by reducing decision overhead, improving task delegation, and enhancing collaboration. The final design met strict virtual quality standards and aligned with market preferences. This study demonstrates the role of multi-agent systems and LLM integration in Industry 4.0, supporting digital prototyping and virtual simulations to replace traditional physical prototyping.

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