Abstract
Significant medical image visualization and annotation tools, tailored for clinical users, play a crucial role in disease diagnosis and treatment. Developing algorithms for annotation assistance, particularly machine learning (ML)-based ones, can be intricate, emphasizing the need for a user-friendly graphical interface for developers. Many software tools are available to meet these requirements, but there is still room for improvement, making the research for new tools highly compelling. The envisioned tool focuses on navigating sequences of DICOM images from diverse modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, Ultrasound (US), and X-rays. Specific requirements involve implementing manual annotation features such as freehand drawing, copying, pasting, and modifying annotations. A scripting plugin interface is essential for running Artificial Intelligence (AI)-based models and adjusting results. Additionally, adaptable surveys complement graphical annotations with textual notes, enhancing information provision. The user evaluation results pinpointed areas for improvement, including incorporating some useful functionalities, as well as enhancements to the user interface for a more intuitive and convenient experience. Despite these suggestions, participants praised the application's simplicity and consistency, highlighting its suitability for the proposed tasks. The ability to revisit annotations ensures flexibility and ease of use in this context.

Abstract
Legislation is a technical domain characterized by highly specialized knowledge forming a large corpus where content is interdependent in nature, but the context is poorly formalized. Typically, the legal domain involves several document types that can be related. Amendments, past judicial interpretations, or new laws can refer to other legal documents to contextualize or support legal formulation. Lengthy and complex texts are frequently unstructured or in some cases semi-structured. Therefore, several problems arise since legal documents, articles, or specific constraints can be cited and referenced differently. Based on legal annotations from a real-world scenario, an architectural approach for modeling a Knowledge Organization System for classifying legal documents and the related legal objects is presented. Data is summarized and classified using a topic modeling approach, with a view toward the improvement of browsing and retrieval of main legal topics and associated terms.

Abstract
This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.

Abstract

Abstract
Weather change plays an important role in work-related accidents, it impairs people's cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.

Abstract
Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues.