https://hdl.handle.net/10216/25692 2026-07-15T11:26:25Z https://hdl.handle.net/10216/110096 Title: Reference Tracking of Depth of Anesthesia Using Optimal Control Abstract: Optimal control theory has gained increasing importance in biomedical applications, e.g., in the automatic administration of anesthetics during general anesthesia. In this context, one of the features that needs to be monitored is the depth of anesthesia. This is usually achieved by the joint administration of hypnotics and analgesics. The depth of anesthesia is quantified by the bispectral index that varies between 97.7% and 0%. This index should usually be kept at a reference level between 40% and 60% during surgeries with general anesthesia. In this contribution, we consider an open-loop control strategy to achieve this goal. In order to determine a suitable controller, we formulate a nonlinear optimal control problem and we solve it using direct methods. These methods have become increasingly useful when computing the numerical solution of an optimal control problem. Moreover, they are known to provide a very robust and general approach. 2015-01-01T00:00:00Z https://hdl.handle.net/10216/110094 Title: Robustness of a new nonlinear positive controller for BIS tracking Abstract: In this paper, the study of the robustness of the positive control law introduced in [1] in the presence of parameter uncertainties is made. This controller was developed to track a desired reference level for the BIS of a patient by means of the simultaneous administration of propofol and of remifentanil. Here, it is proven that in the presence of uncertainties in the BIS model, the controller still has a good performance and the BIS of the patient converges to clinically acceptable values. These results are illustrated by simulations. 2014-01-01T00:00:00Z https://hdl.handle.net/10216/174299 Title: CitiLink: enhancing municipal transparency and citizen engagement through searchable meeting minutes Abstract: City council minutes are typically lengthy and formal documents with a bureaucratic writing style. Although publicly available, their structure often makes it difficult for citizens or journalists to efficiently find information. In this demo, we present CitiLink, a platform designed to transform unstructured municipal meeting minutes into structured and searchable data, demonstrating how NLP and IR can enhance the accessibility and transparency of local government. The system employs LLMs to extract metadata, discussed subjects, and voting outcomes, which are then indexed in a database to support full-text search with BM25 ranking and faceted filtering through a user-friendly interface. The developed system was built over a collection of 120 min made available by six Portuguese municipalities. To assess its usability, CitiLink was tested through guided sessions with municipal personnel, providing insights into how real users interact with the system. In addition, we evaluated Gemini's performance in extracting relevant information from the minutes, highlighting its performance in data extraction. 2026-01-01T00:00:00Z https://hdl.handle.net/10216/171771 Title: The incremental process of building an annotation scheme for clinical narratives in portuguese: the contribution of human variation analysis Abstract: The development of a robust annotation scheme and corresponding guidelines is crucial for pro- ducing annotated datasets that advance both lin- guistic and computational research. This paper presents a case study that outlines a method- ology for designing an annotation scheme and its guidelines, specifically aimed at represent- ing morphosyntactic and semantic information regarding temporal features, as well as medi- cal information in medical reports written in Portuguese. We detail a multi-step process that includes reviewing existing frameworks, con- ducting an annotation experiment to determine the optimal approach, and designing a model based on these findings. We validated the ap- proach through a pilot experiment where we assessed the reliability and applicability of the annotation scheme and guidelines. In this ex- periment, two annotators independently anno- tated a patient's medical report consisting of six documents using the proposed model, while a curator established the ground truth. The analy- sis of inter-annotator agreement and the annota- tion results enabled the identification of sources of human variation and provided insights for further refinement of the annotation scheme and guidelines. 2025-01-01T00:00:00Z https://hdl.handle.net/10216/174240 Title: Assessing the Reprocessing Potential of Lithium Tailings Through Dense Medium Separation 2026-01-01T00:00:00Z https://hdl.handle.net/10216/153816 Title: In silico screening and in vitro validation of natural-based luxS inhibitors 2022-06-15T00:00:00Z https://hdl.handle.net/10216/153819 Title: Phytochemicals as adjuvants of topical antibiotics to treat biofilm related Staphylococcus aureus wound infections 2022-06-15T00:00:00Z https://hdl.handle.net/10216/152659 Title: Digital Terrain Modelling in Landscape Design 2010-01-01T00:00:00Z https://hdl.handle.net/10216/173411 Title: Street tree characterization and selection for urban environments: a case study in Matosinhos and Leça da Palmeira (Portugal) 2026-02-25T00:00:00Z https://hdl.handle.net/10216/173405 Title: Criteria for defining 'streets suitable for tree planting': a model applied to Matosinhos and Leça da Palmeira (Portugal) 2026-02-01T00:00:00Z https://hdl.handle.net/10216/90548 Title: State Elimination Ordering Strategies: Some Experimental Results Abstract: Recently, the problem of obtaining a short regular expression equivalent to a given finite automaton has been intensively investigated. Algorithms for converting finite automata to regular expressions have an exponential blow-up in the worst-case. To overcome this, simple heuristic methods have been proposed. In this paper we analyse some of the heuris- tics presented in the literature and propose new ones. We also present some experimental comparative results based on uniform random gener- ated deterministic finite automata. 2010-01-01T00:00:00Z https://hdl.handle.net/10216/171561 Title: Visual representations of temporal relations between events and time expressions in news stories Abstract: High-quality annotation is essential for the ef- fective predictions of machine learning mod- els. When annotations are dense, achieving accurate human labeling can be challenging since the most used annotation tools present an overloaded visualization of labels. Thus, we present Vitra (Visualizer of temporal relation annotations), a tool designed for viewing anno- tations made in corpora, specifically focusing on the temporal relations between events and temporal expressions. This tool aims to fill a gap in the available resources for this purpose. Our focus is on narrative text, which is a rich source for these types of elements. Vitra was developed to increase the human capacity for detecting annotation errors and uncover rela- tions between narrative components or issues about the annotation scheme. To show how this can be done, we present an analysis of a subset of the Text2Story Lusa corpus, a dataset of Portuguese news stories. Such analysis fo- cuses on the linguistic properties of the events and temporal expressions that occur in the an- notated texts, in particular, of short news. We highlight that annotation is an iterative process that involves multiple rounds of revision, and our tool facilitates this process by helping users detect inconsistencies and improve the annota- tion scheme, thus offering added value to the community 2025-01-01T00:00:00Z https://hdl.handle.net/10216/101828 Title: g-tries: an efficient data structure for discovering network motifs Abstract: In this paper we propose a novel specialized data structure that we call g-trie, designed to deal with collections of subgraphs. The main conceptual idea is akin to a prefix tree in the sense that we take advantage of common topology by constructing a multiway tree where the descendants of a node share a common substructure. We give algorithms to construct a g-trie, to list all stored subgraphs, and to find occurrences on another graph of the subgraphs stored in the g-trie. We evaluate the implementation of this structure and its associated algorithms on a set of representative benchmark biological networks in order to find network motifs. To assess the efficiency of our algorithms we compare their performance with other known network motif algorithms also implemented in the same common platform. Our results show that indeed, g-tries are a feasible, adequate and very efficient data structure for network motifs discovery, clearly outperforming previous algorithms and data structures. (c) 2010 ACM. 2010-01-01T00:00:00Z https://hdl.handle.net/10216/97578 Title: Efficient Parallel Subgraph Counting Using G-Tries Abstract: Finding and counting the occurrences of a collection of subgraphs within another larger network is a computationally hard problem, closely related to graph isomorphism. The subgraph count is by itself a very powerful characterization of a network and it is crucial for other important network measurements. G-tries are a specialized data-structure designed to store and search for subgraphs. By taking advantage of subgraph common substructure, g-tries can provide considerable speedups over previously used methods. In this paper we present a parallel algorithm based precisely on gtries that is able to efficiently find and count subgraphs. The algorithm relies on randomized receiver-initiated dynamic load balancing and is able to stop its computation at any given time, efficiently store its search position, divide what is left to compute in two halfs, and resume from where it left. We apply our algorithm to several representative real complex networks from various domains and examine its scalability. We obtain an almost linear speedup up to 128 processors, thus allowing us to reach previously unfeasible limits. We showcase the multidisciplinary potential of the algorithm by also applying it to network motif discovery. (c) 2010 IEEE. 2010-01-01T00:00:00Z https://hdl.handle.net/10216/170535 Title: Large Language Model Framework for Log Sequence Anomaly Detection 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 datasetsHDFS, BGL, and Thunderbirdwhere 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. (c) 2025 Elsevier B.V., All rights reserved. 2025-09-15T00:00:00Z https://hdl.handle.net/10216/167718 Title: Drawing and Mathematics in Art Education and Design: Interdisciplinary Approaches to Geometry 2025-01-01T00:00:00Z https://hdl.handle.net/10216/172267 Title: Drawing and Mathematics in Art Education and Design: Interdisciplinary Approaches to Geometry 2025-01-01T00:00:00Z https://hdl.handle.net/10216/171517 Title: An annotation scheme for financial news in Portuguese Abstract: We present an annotation scheme designed to capture information related to the maintenance or change in the price of some goods (fuels, wa- ter, and vehicles) in news articles in Portuguese. The methodology we used involved adapting an existing annotation scheme, the Text2Story scheme (Silvano et al., 2021; Leal et al., 2022), which is based on different parts of ISO 24617 to capture the essential information for this project. Adaptations were needed to accommo- date specific information, namely, information related to quantitative data and comparative re- lations that are abundant in this type of news. In this paper, we provide an overview of the annotation scheme, highlighting attributes and values of the entity and link structures specifi- cally designed to capture financial information, as well as some problems we had to overcome in the process of building it and the rationale of some decisions behind its overall architecture. 2025-01-01T00:00:00Z https://hdl.handle.net/10216/171604 Title: Using LLMs to generate patient journeys in portuguese: an experiment Abstract: The relationship of a patient with a hospital from admission to discharge is often kept in a series of textual documents that describe the patient's journey. These documents are important to analyze the di"erent steps of the clinical process and to make aggregated studies of the paths of patients in the hospital. In this paper, we explore the potential of Large Language Models (LLMs) to generate realistic and comprehensive patient journeys in European Portuguese, addressing the scarcity of medical data in this speci!c context. We employed Google's Gemini 1.5 Flash model and utilized a dataset of 285 European Portuguese published case reports from the SPMI website, published by the Portuguese Society of Internal Medicine, as references for generating synthetic medical reports. Our methodology involves a sequential approach to generating a synthetic patient journey. Initially, we generate an admission report, followed by a discharge report. Subsequently, we generate a comprehensive patient journey that integrates the admission, multiple daily progress reports, and the discharge into a cohesive narrative. This end-to-end process ensures a realistic and detailed representation of the patient's clinical pathway as a patient's journey. The generated reports were rigorously evaluated by medical and linguistic professionals, as well as automatic metrics to measure the inclusion of key medical entities, similarity to the case report, and correct Portuguese variant. Both qualitative and quantitative evaluations con!rmed that the generated synthetic reports are predominantly written in European Portuguese without the loss of important medical information from the case reports. This work contributes to developing high-quality synthetic medical data for training LLMs and advancing AI-driven healthcare applications in under-resourced language settings.12 2025-01-01T00:00:00Z https://hdl.handle.net/10216/149083 Title: Performance of Sparse Binding Arrays for Or-Parallelism Abstract: <jats:p>One important problem in the design of novel logic programming systems is the support of several forms of implicit parallelism. A new binding model, the Sparse Binding Array (SBA), has been proposed for the efficient and simplified integration of Independent-And, Determinate-And and Or-parallelism. In this paper we report on the use of this model for pure Or-parallelism. The work discusses the major implementation issues in supporting this binding model for pure Or-parallelism. We show that an implementation based on this Binding model is more efficient then the original Aurora using tbe traditional Binding Array model [16]. Moreover, we explain how the notion of a variable level can be used to reduce overheads of the Orparallel system. Our results in supporting pure or-parallelism show that the approach is very promissing for combined paralell systems.</jats:p> 1996-01-01T00:00:00Z